Merge pull request #142 from CNugteren/gemm_batched

Added a first batched version of the GEMM routine

CHANGELOG

@@ -15,6 +15,7 @@ Development version (next release)
   * STRSM/DTRSM/CTRSM/ZTRSM (experimental, un-optimized)
 - Added batched (non-BLAS) routines:
   * SAXPYBATCHED/DAXPYBATCHED/CAXPYBATCHED/ZAXPYBATCHED/HAXPYBATCHED (batched version of AXPY)
+  * SGEMMBATCHED/DGEMMBATCHED/CGEMMBATCHED/ZGEMMBATCHED/HGEMMBATCHED (batched version of GEMM)
 
 Version 0.10.0
 - Updated to version 8.0 of the CLCudaAPI C++11 OpenCL header

CMakeLists.txt

@@ -159,7 +159,7 @@ set(LEVEL1_ROUTINES xswap xscal xcopy xaxpy xdot xdotu xdotc xnrm2 xasum xamax)
 set(LEVEL2_ROUTINES xgemv xgbmv xhemv xhbmv xhpmv xsymv xsbmv xspmv xtrmv xtbmv xtpmv xtrsv
                     xger xgeru xgerc xher xhpr xher2 xhpr2 xsyr xspr xsyr2 xspr2)
 set(LEVEL3_ROUTINES xgemm xsymm xhemm xsyrk xherk xsyr2k xher2k xtrmm xtrsm)
-set(LEVELX_ROUTINES xomatcopy xaxpybatched)
+set(LEVELX_ROUTINES xomatcopy xaxpybatched xgemmbatched)
 set(ROUTINES ${LEVEL1_ROUTINES} ${LEVEL2_ROUTINES} ${LEVEL3_ROUTINES} ${LEVELX_ROUTINES})
 set(PRECISIONS 32 64 3232 6464 16)
 

README.md

@@ -276,6 +276,13 @@ CLBlast supports almost all the Netlib BLAS routines plus a couple of extra non-
 | xTRMM    | ✔ | ✔ | ✔ | ✔ | ✔ |
 | xTRSM    | ✔ | ✔ | ✔ | ✔ |   | (experimental, un-optimized)
 
+Futhermore, there are also batched versions of BLAS routines available, processing multiple smaller computations in one go for better performance:
+
+| Batched      | S | D | C | Z | H |
+| -------------|---|---|---|---|---|
+| xAXPYBATCHED | ✔ | ✔ | ✔ | ✔ | ✔ |
+| xGEMMBATCHED | ✔ | ✔ | ✔ | ✔ | ✔ |
+
 In addition, some extra non-BLAS routines are also supported by CLBlast, classified as level-X. They are experimental and should be used with care:
 
 | Level-X    | S | D | C | Z | H |

doc/clblast.md

@@ -2969,6 +2969,105 @@ Arguments to AXPYBATCHED:
 
 
 
+xGEMMBATCHED: Batched version of GEMM
+-------------
+
+As GEMM, but multiple operations are batched together for better performance.
+
+C++ API:
+```
+template <typename T>
+StatusCode GemmBatched(const Layout layout, const Transpose a_transpose, const Transpose b_transpose,
+                       const size_t m, const size_t n, const size_t k,
+                       const T *alphas,
+                       const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                       const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                       const T *betas,
+                       cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                       const size_t batch_count,
+                       cl_command_queue* queue, cl_event* event)
+```
+
+C API:
+```
+CLBlastStatusCode CLBlastSgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                      const size_t m, const size_t n, const size_t k,
+                                      const float *alphas,
+                                      const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                      const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                      const float *betas,
+                                      cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                      const size_t batch_count,
+                                      cl_command_queue* queue, cl_event* event)
+CLBlastStatusCode CLBlastDgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                      const size_t m, const size_t n, const size_t k,
+                                      const double *alphas,
+                                      const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                      const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                      const double *betas,
+                                      cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                      const size_t batch_count,
+                                      cl_command_queue* queue, cl_event* event)
+CLBlastStatusCode CLBlastCgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                      const size_t m, const size_t n, const size_t k,
+                                      const cl_float2 *alphas,
+                                      const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                      const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                      const cl_float2 *betas,
+                                      cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                      const size_t batch_count,
+                                      cl_command_queue* queue, cl_event* event)
+CLBlastStatusCode CLBlastZgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                      const size_t m, const size_t n, const size_t k,
+                                      const cl_double2 *alphas,
+                                      const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                      const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                      const cl_double2 *betas,
+                                      cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                      const size_t batch_count,
+                                      cl_command_queue* queue, cl_event* event)
+CLBlastStatusCode CLBlastHgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                      const size_t m, const size_t n, const size_t k,
+                                      const cl_half *alphas,
+                                      const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                      const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                      const cl_half *betas,
+                                      cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                      const size_t batch_count,
+                                      cl_command_queue* queue, cl_event* event)
+```
+
+Arguments to GEMMBATCHED:
+
+* `const Layout layout`: Data-layout of the matrices, either `Layout::kRowMajor` (101) for row-major layout or `Layout::kColMajor` (102) for column-major data-layout.
+* `const Transpose a_transpose`: Transposing the input matrix A, either `Transpose::kNo` (111), `Transpose::kYes` (112), or `Transpose::kConjugate` (113) for a complex-conjugate transpose.
+* `const Transpose b_transpose`: Transposing the input matrix B, either `Transpose::kNo` (111), `Transpose::kYes` (112), or `Transpose::kConjugate` (113) for a complex-conjugate transpose.
+* `const size_t m`: Integer size argument. This value must be positive.
+* `const size_t n`: Integer size argument. This value must be positive.
+* `const size_t k`: Integer size argument. This value must be positive.
+* `const T *alphas`: Input scalar constants.
+* `const cl_mem a_buffer`: OpenCL buffer to store the input A matrix.
+* `const size_t *a_offsets`: The offsets in elements from the start of the input A matrix.
+* `const size_t a_ld`: Leading dimension of the input A matrix. This value must be greater than 0.
+* `const cl_mem b_buffer`: OpenCL buffer to store the input B matrix.
+* `const size_t *b_offsets`: The offsets in elements from the start of the input B matrix.
+* `const size_t b_ld`: Leading dimension of the input B matrix. This value must be greater than 0.
+* `const T *betas`: Input scalar constants.
+* `cl_mem c_buffer`: OpenCL buffer to store the output C matrix.
+* `const size_t *c_offsets`: The offsets in elements from the start of the output C matrix.
+* `const size_t c_ld`: Leading dimension of the output C matrix. This value must be greater than 0.
+* `const size_t batch_count`: Number of batches. This value must be positive.
+* `cl_command_queue* queue`: Pointer to an OpenCL command queue associated with a context and device to execute the routine on.
+* `cl_event* event`: Pointer to an OpenCL event to be able to wait for completion of the routine's OpenCL kernel(s). This is an optional argument.
+
+Requirements for GEMMBATCHED:
+
+* When `transpose_a == Transpose::kNo`, then `a_ld` must be at least `m`, otherwise `a_ld` must be at least `k`.
+* When `transpose_b == Transpose::kNo`, then `b_ld` must be at least `k`, otherwise `b_ld` must be at least `n`.
+* The value of `c_ld` must be at least `m`.
+
+
+
 ClearCache: Resets the cache of compiled binaries (auxiliary function)
 -------------
 

include/clblast.h

@@ -619,6 +619,18 @@ StatusCode AxpyBatched(const size_t n,
                        const size_t batch_count,
                        cl_command_queue* queue, cl_event* event = nullptr);
 
+// Batched version of GEMM: SGEMMBATCHED/DGEMMBATCHED/CGEMMBATCHED/ZGEMMBATCHED/HGEMMBATCHED
+template <typename T>
+StatusCode GemmBatched(const Layout layout, const Transpose a_transpose, const Transpose b_transpose,
+                       const size_t m, const size_t n, const size_t k,
+                       const T *alphas,
+                       const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                       const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                       const T *betas,
+                       cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                       const size_t batch_count,
+                       cl_command_queue* queue, cl_event* event = nullptr);
+
 // =================================================================================================
 
 // CLBlast stores binaries of compiled kernels into a cache in case the same kernel is used later on

include/clblast_c.h

@@ -1360,6 +1360,53 @@ CLBlastStatusCode PUBLIC_API CLBlastHaxpyBatched(const size_t n,
                                                  const size_t batch_count,
                                                  cl_command_queue* queue, cl_event* event);
 
+// Batched version of GEMM: SGEMMBATCHED/DGEMMBATCHED/CGEMMBATCHED/ZGEMMBATCHED/HGEMMBATCHED
+CLBlastStatusCode PUBLIC_API CLBlastSgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                                 const size_t m, const size_t n, const size_t k,
+                                                 const float *alphas,
+                                                 const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                                 const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                                 const float *betas,
+                                                 cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                                 const size_t batch_count,
+                                                 cl_command_queue* queue, cl_event* event);
+CLBlastStatusCode PUBLIC_API CLBlastDgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                                 const size_t m, const size_t n, const size_t k,
+                                                 const double *alphas,
+                                                 const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                                 const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                                 const double *betas,
+                                                 cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                                 const size_t batch_count,
+                                                 cl_command_queue* queue, cl_event* event);
+CLBlastStatusCode PUBLIC_API CLBlastCgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                                 const size_t m, const size_t n, const size_t k,
+                                                 const cl_float2 *alphas,
+                                                 const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                                 const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                                 const cl_float2 *betas,
+                                                 cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                                 const size_t batch_count,
+                                                 cl_command_queue* queue, cl_event* event);
+CLBlastStatusCode PUBLIC_API CLBlastZgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                                 const size_t m, const size_t n, const size_t k,
+                                                 const cl_double2 *alphas,
+                                                 const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                                 const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                                 const cl_double2 *betas,
+                                                 cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                                 const size_t batch_count,
+                                                 cl_command_queue* queue, cl_event* event);
+CLBlastStatusCode PUBLIC_API CLBlastHgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                                 const size_t m, const size_t n, const size_t k,
+                                                 const cl_half *alphas,
+                                                 const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                                 const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                                 const cl_half *betas,
+                                                 cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                                 const size_t batch_count,
+                                                 cl_command_queue* queue, cl_event* event);
+
 // =================================================================================================
 
 // CLBlast stores binaries of compiled kernels into a cache in case the same kernel is used later on

scripts/generator/generator.py

@@ -41,7 +41,7 @@ FILES = [
     "/include/clblast_netlib_c.h",
     "/src/clblast_netlib_c.cpp",
 ]
-HEADER_LINES = [122, 76, 126, 23, 29, 41, 65, 32]
+HEADER_LINES = [123, 76, 126, 23, 29, 41, 65, 32]
 FOOTER_LINES = [25, 138, 27, 38, 6, 6, 9, 2]
 HEADER_LINES_DOC = 0
 FOOTER_LINES_DOC = 63
@@ -163,6 +163,7 @@ ROUTINES = [
   Routine(True,  True,  False, "x", "omatcopy", T, [S,D,C,Z,H],   ["m","n"],            ["layout","a_transpose"],                              ["a"],      ["b"],                        [amn,bnma],      ["alpha"],        "",    "Scaling and out-place transpose/copy (non-BLAS function)", "Performs scaling and out-of-place transposition/copying of matrices according to _B = alpha*op(A)_, in which _A_ is an input matrix (_m_ rows by _n_ columns), _B_ an output matrix, and _alpha_ a scalar value. The operation _op_ can be a normal matrix copy, a transposition or a conjugate transposition.", [ald_m, bld_n]),
   # Batched routines:
   Routine(True,  True,  True,  "x", "axpy",     T, [S,D,C,Z,H],   ["n"],                [],                                                    ["x"],      ["y"],                        [xn,yn],         ["alpha"],        "",    "Batched version of AXPY", "As AXPY, but multiple operations are batched together for better performance.", []),
+  Routine(True,  True,  True,  "x", "gemm",     T, [S,D,C,Z,H],   ["m","n","k"],        ["layout","a_transpose","b_transpose"],                ["a","b"],  ["c"],                        [amk,bkn,cmn],   ["alpha","beta"], "",    "Batched version of GEMM", "As GEMM, but multiple operations are batched together for better performance.", [ald_transa_m_k, bld_transb_k_n, cld_m]),
 ]]
 
 

src/clblast.cpp

@@ -72,6 +72,7 @@
 // Level-x includes (non-BLAS)
 #include "routines/levelx/xomatcopy.hpp"
 #include "routines/levelx/xaxpybatched.hpp"
+#include "routines/levelx/xgemmbatched.hpp"
 
 namespace clblast {
 
@@ -2231,6 +2232,89 @@ template StatusCode PUBLIC_API AxpyBatched<half>(const size_t,
                                                  cl_mem, const size_t*, const size_t,
                                                  const size_t,
                                                  cl_command_queue*, cl_event*);
+
+// Batched version of GEMM: SGEMMBATCHED/DGEMMBATCHED/CGEMMBATCHED/ZGEMMBATCHED/HGEMMBATCHED
+template <typename T>
+StatusCode GemmBatched(const Layout layout, const Transpose a_transpose, const Transpose b_transpose,
+                       const size_t m, const size_t n, const size_t k,
+                       const T *alphas,
+                       const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                       const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                       const T *betas,
+                       cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                       const size_t batch_count,
+                       cl_command_queue* queue, cl_event* event) {
+  try {
+    auto queue_cpp = Queue(*queue);
+    auto routine = XgemmBatched<T>(queue_cpp, event);
+    auto alphas_cpp = std::vector<T>();
+    auto betas_cpp = std::vector<T>();
+    auto a_offsets_cpp = std::vector<size_t>();
+    auto b_offsets_cpp = std::vector<size_t>();
+    auto c_offsets_cpp = std::vector<size_t>();
+    for (auto batch = size_t{0}; batch < batch_count; ++batch) {
+      alphas_cpp.push_back(alphas[batch]);
+      betas_cpp.push_back(betas[batch]);
+      a_offsets_cpp.push_back(a_offsets[batch]);
+      b_offsets_cpp.push_back(b_offsets[batch]);
+      c_offsets_cpp.push_back(c_offsets[batch]);
+    }
+    routine.DoGemmBatched(layout, a_transpose, b_transpose,
+                          m, n, k,
+                          alphas_cpp,
+                          Buffer<T>(a_buffer), a_offsets_cpp, a_ld,
+                          Buffer<T>(b_buffer), b_offsets_cpp, b_ld,
+                          betas_cpp,
+                          Buffer<T>(c_buffer), c_offsets_cpp, c_ld,
+                          batch_count);
+    return StatusCode::kSuccess;
+  } catch (...) { return DispatchException(); }
+}
+template StatusCode PUBLIC_API GemmBatched<float>(const Layout, const Transpose, const Transpose,
+                                                  const size_t, const size_t, const size_t,
+                                                  const float*,
+                                                  const cl_mem, const size_t*, const size_t,
+                                                  const cl_mem, const size_t*, const size_t,
+                                                  const float*,
+                                                  cl_mem, const size_t*, const size_t,
+                                                  const size_t,
+                                                  cl_command_queue*, cl_event*);
+template StatusCode PUBLIC_API GemmBatched<double>(const Layout, const Transpose, const Transpose,
+                                                   const size_t, const size_t, const size_t,
+                                                   const double*,
+                                                   const cl_mem, const size_t*, const size_t,
+                                                   const cl_mem, const size_t*, const size_t,
+                                                   const double*,
+                                                   cl_mem, const size_t*, const size_t,
+                                                   const size_t,
+                                                   cl_command_queue*, cl_event*);
+template StatusCode PUBLIC_API GemmBatched<float2>(const Layout, const Transpose, const Transpose,
+                                                   const size_t, const size_t, const size_t,
+                                                   const float2*,
+                                                   const cl_mem, const size_t*, const size_t,
+                                                   const cl_mem, const size_t*, const size_t,
+                                                   const float2*,
+                                                   cl_mem, const size_t*, const size_t,
+                                                   const size_t,
+                                                   cl_command_queue*, cl_event*);
+template StatusCode PUBLIC_API GemmBatched<double2>(const Layout, const Transpose, const Transpose,
+                                                    const size_t, const size_t, const size_t,
+                                                    const double2*,
+                                                    const cl_mem, const size_t*, const size_t,
+                                                    const cl_mem, const size_t*, const size_t,
+                                                    const double2*,
+                                                    cl_mem, const size_t*, const size_t,
+                                                    const size_t,
+                                                    cl_command_queue*, cl_event*);
+template StatusCode PUBLIC_API GemmBatched<half>(const Layout, const Transpose, const Transpose,
+                                                 const size_t, const size_t, const size_t,
+                                                 const half*,
+                                                 const cl_mem, const size_t*, const size_t,
+                                                 const cl_mem, const size_t*, const size_t,
+                                                 const half*,
+                                                 cl_mem, const size_t*, const size_t,
+                                                 const size_t,
+                                                 cl_command_queue*, cl_event*);
 // =================================================================================================
 
 // Clears the cache of stored binaries

src/clblast_c.cpp

@@ -3554,6 +3554,163 @@ CLBlastStatusCode CLBlastHaxpyBatched(const size_t n,
   } catch (...) { return static_cast<CLBlastStatusCode>(clblast::DispatchExceptionForC()); }
 }
 
+// GEMM
+CLBlastStatusCode CLBlastSgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                      const size_t m, const size_t n, const size_t k,
+                                      const float *alphas,
+                                      const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                      const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                      const float *betas,
+                                      cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                      const size_t batch_count,
+                                      cl_command_queue* queue, cl_event* event) {
+  auto alphas_cpp = std::vector<float>();
+  auto betas_cpp = std::vector<float>();
+  for (auto batch = size_t{0}; batch < batch_count; ++batch) {
+    alphas_cpp.push_back(alphas[batch]);
+    betas_cpp.push_back(betas[batch]);
+  }
+  try {
+    return static_cast<CLBlastStatusCode>(
+      clblast::GemmBatched(static_cast<clblast::Layout>(layout),
+                           static_cast<clblast::Transpose>(a_transpose),
+                           static_cast<clblast::Transpose>(b_transpose),
+                           m, n, k,
+                           alphas_cpp.data(),
+                           a_buffer, a_offsets, a_ld,
+                           b_buffer, b_offsets, b_ld,
+                           betas_cpp.data(),
+                           c_buffer, c_offsets, c_ld,
+                           batch_count,
+                           queue, event)
+    );
+  } catch (...) { return static_cast<CLBlastStatusCode>(clblast::DispatchExceptionForC()); }
+}
+CLBlastStatusCode CLBlastDgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                      const size_t m, const size_t n, const size_t k,
+                                      const double *alphas,
+                                      const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                      const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                      const double *betas,
+                                      cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                      const size_t batch_count,
+                                      cl_command_queue* queue, cl_event* event) {
+  auto alphas_cpp = std::vector<double>();
+  auto betas_cpp = std::vector<double>();
+  for (auto batch = size_t{0}; batch < batch_count; ++batch) {
+    alphas_cpp.push_back(alphas[batch]);
+    betas_cpp.push_back(betas[batch]);
+  }
+  try {
+    return static_cast<CLBlastStatusCode>(
+      clblast::GemmBatched(static_cast<clblast::Layout>(layout),
+                           static_cast<clblast::Transpose>(a_transpose),
+                           static_cast<clblast::Transpose>(b_transpose),
+                           m, n, k,
+                           alphas_cpp.data(),
+                           a_buffer, a_offsets, a_ld,
+                           b_buffer, b_offsets, b_ld,
+                           betas_cpp.data(),
+                           c_buffer, c_offsets, c_ld,
+                           batch_count,
+                           queue, event)
+    );
+  } catch (...) { return static_cast<CLBlastStatusCode>(clblast::DispatchExceptionForC()); }
+}
+CLBlastStatusCode CLBlastCgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                      const size_t m, const size_t n, const size_t k,
+                                      const cl_float2 *alphas,
+                                      const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                      const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                      const cl_float2 *betas,
+                                      cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                      const size_t batch_count,
+                                      cl_command_queue* queue, cl_event* event) {
+  auto alphas_cpp = std::vector<float2>();
+  auto betas_cpp = std::vector<float2>();
+  for (auto batch = size_t{0}; batch < batch_count; ++batch) {
+    alphas_cpp.push_back(float2{alphas[batch].s[0], alphas[batch].s[1]});
+    betas_cpp.push_back(float2{betas[batch].s[0], betas[batch].s[1]});
+  }
+  try {
+    return static_cast<CLBlastStatusCode>(
+      clblast::GemmBatched(static_cast<clblast::Layout>(layout),
+                           static_cast<clblast::Transpose>(a_transpose),
+                           static_cast<clblast::Transpose>(b_transpose),
+                           m, n, k,
+                           alphas_cpp.data(),
+                           a_buffer, a_offsets, a_ld,
+                           b_buffer, b_offsets, b_ld,
+                           betas_cpp.data(),
+                           c_buffer, c_offsets, c_ld,
+                           batch_count,
+                           queue, event)
+    );
+  } catch (...) { return static_cast<CLBlastStatusCode>(clblast::DispatchExceptionForC()); }
+}
+CLBlastStatusCode CLBlastZgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                      const size_t m, const size_t n, const size_t k,
+                                      const cl_double2 *alphas,
+                                      const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                      const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                      const cl_double2 *betas,
+                                      cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                      const size_t batch_count,
+                                      cl_command_queue* queue, cl_event* event) {
+  auto alphas_cpp = std::vector<double2>();
+  auto betas_cpp = std::vector<double2>();
+  for (auto batch = size_t{0}; batch < batch_count; ++batch) {
+    alphas_cpp.push_back(double2{alphas[batch].s[0], alphas[batch].s[1]});
+    betas_cpp.push_back(double2{betas[batch].s[0], betas[batch].s[1]});
+  }
+  try {
+    return static_cast<CLBlastStatusCode>(
+      clblast::GemmBatched(static_cast<clblast::Layout>(layout),
+                           static_cast<clblast::Transpose>(a_transpose),
+                           static_cast<clblast::Transpose>(b_transpose),
+                           m, n, k,
+                           alphas_cpp.data(),
+                           a_buffer, a_offsets, a_ld,
+                           b_buffer, b_offsets, b_ld,
+                           betas_cpp.data(),
+                           c_buffer, c_offsets, c_ld,
+                           batch_count,
+                           queue, event)
+    );
+  } catch (...) { return static_cast<CLBlastStatusCode>(clblast::DispatchExceptionForC()); }
+}
+CLBlastStatusCode CLBlastHgemmBatched(const CLBlastLayout layout, const CLBlastTranspose a_transpose, const CLBlastTranspose b_transpose,
+                                      const size_t m, const size_t n, const size_t k,
+                                      const cl_half *alphas,
+                                      const cl_mem a_buffer, const size_t *a_offsets, const size_t a_ld,
+                                      const cl_mem b_buffer, const size_t *b_offsets, const size_t b_ld,
+                                      const cl_half *betas,
+                                      cl_mem c_buffer, const size_t *c_offsets, const size_t c_ld,
+                                      const size_t batch_count,
+                                      cl_command_queue* queue, cl_event* event) {
+  auto alphas_cpp = std::vector<half>();
+  auto betas_cpp = std::vector<half>();
+  for (auto batch = size_t{0}; batch < batch_count; ++batch) {
+    alphas_cpp.push_back(alphas[batch]);
+    betas_cpp.push_back(betas[batch]);
+  }
+  try {
+    return static_cast<CLBlastStatusCode>(
+      clblast::GemmBatched(static_cast<clblast::Layout>(layout),
+                           static_cast<clblast::Transpose>(a_transpose),
+                           static_cast<clblast::Transpose>(b_transpose),
+                           m, n, k,
+                           alphas_cpp.data(),
+                           a_buffer, a_offsets, a_ld,
+                           b_buffer, b_offsets, b_ld,
+                           betas_cpp.data(),
+                           c_buffer, c_offsets, c_ld,
+                           batch_count,
+                           queue, event)
+    );
+  } catch (...) { return static_cast<CLBlastStatusCode>(clblast::DispatchExceptionForC()); }
+}
+
 // =================================================================================================
 
 // Clears the cache of stored binaries

src/kernels/level3/copy_pad.opencl

@@ -24,16 +24,14 @@ R"(
 // Copies a matrix from source to destination. The output is padded with zero values in case the
 // destination matrix dimensions are larger than the source matrix dimensions. Additionally, the ld
 // value and offset can be different.
-__kernel __attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1)))
-void CopyPadMatrix(const int src_one, const int src_two,
-                   const int src_ld, const int src_offset,
-                   __global const real* restrict src,
-                   const int dest_one, const int dest_two,
-                   const int dest_ld, const int dest_offset,
-                   __global real* dest,
-                   const real_arg arg_alpha,
-                   const int do_conjugate) {
-  const real alpha = GetRealArg(arg_alpha);
+inline void _CopyPadMatrix(const int src_one, const int src_two,
+                           const int src_ld, const int src_offset,
+                           __global const real* restrict src,
+                           const int dest_one, const int dest_two,
+                           const int dest_ld, const int dest_offset,
+                           __global real* dest,
+                           const real alpha,
+                           const int do_conjugate) {
 
   // Loops over the work per thread in both dimensions
   #pragma unroll
@@ -60,22 +58,36 @@ void CopyPadMatrix(const int src_one, const int src_two,
   }
 }
 
+// Interface to the above function
+__kernel __attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1)))
+void CopyPadMatrix(const int src_one, const int src_two,
+                   const int src_ld, const int src_offset,
+                   __global const real* restrict src,
+                   const int dest_one, const int dest_two,
+                   const int dest_ld, const int dest_offset,
+                   __global real* dest,
+                   const real_arg arg_alpha,
+                   const int do_conjugate) {
+  const real alpha = GetRealArg(arg_alpha);
+  _CopyPadMatrix(src_one, src_two, src_ld, src_offset, src,
+                 dest_one, dest_two, dest_ld, dest_offset, dest,
+                 alpha, do_conjugate);
+}
+
 // =================================================================================================
 
 // Same as above, but now un-pads a matrix. This kernel reads data from a padded source matrix, but
 // writes only the actual data back to the destination matrix. Again, the ld value and offset can
 // be different.
-__kernel __attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1)))
-void CopyMatrix(const int src_one, const int src_two,
-                const int src_ld, const int src_offset,
-                __global const real* restrict src,
-                const int dest_one, const int dest_two,
-                const int dest_ld, const int dest_offset,
-                __global real* dest,
-                const real_arg arg_alpha,
-                const int upper, const int lower,
-                const int diagonal_imag_zero) {
-  const real alpha = GetRealArg(arg_alpha);
+inline void _CopyMatrix(const int src_one, const int src_two,
+                        const int src_ld, const int src_offset,
+                        __global const real* restrict src,
+                        const int dest_one, const int dest_two,
+                        const int dest_ld, const int dest_offset,
+                        __global real* dest,
+                        const real alpha,
+                        const int upper, const int lower,
+                        const int diagonal_imag_zero) {
 
   // Loops over the work per thread in both dimensions
   #pragma unroll
@@ -105,6 +117,62 @@ void CopyMatrix(const int src_one, const int src_two,
   }
 }
 
+// Interface to the above function
+__kernel __attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1)))
+void CopyMatrix(const int src_one, const int src_two,
+                const int src_ld, const int src_offset,
+                __global const real* restrict src,
+                const int dest_one, const int dest_two,
+                const int dest_ld, const int dest_offset,
+                __global real* dest,
+                const real_arg arg_alpha,
+                const int upper, const int lower,
+                const int diagonal_imag_zero) {
+  const real alpha = GetRealArg(arg_alpha);
+  _CopyMatrix(src_one, src_two, src_ld, src_offset, src,
+              dest_one, dest_two, dest_ld, dest_offset, dest,
+              alpha, upper, lower, diagonal_imag_zero);
+}
+
+// =================================================================================================
+#if defined(ROUTINE_GEMMBATCHED)
+
+// Batched version of the above
+__kernel __attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1)))
+void CopyPadMatrixBatched(const int src_one, const int src_two,
+                          const int src_ld, const __constant int* src_offsets,
+                          __global const real* restrict src,
+                          const int dest_one, const int dest_two,
+                          const int dest_ld, const __constant int* dest_offsets,
+                          __global real* dest,
+                          const int do_conjugate) {
+  const int batch = get_group_id(2);
+  const int src_offset = src_offsets[batch];
+  const int dest_offset = dest_offsets[batch];
+  real alpha; SetToOne(alpha);
+  _CopyPadMatrix(src_one, src_two, src_ld, src_offset, src,
+                 dest_one, dest_two, dest_ld, dest_offset, dest,
+                 alpha, do_conjugate);
+}
+
+// Batched version of the above
+__kernel __attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1)))
+void CopyMatrixBatched(const int src_one, const int src_two,
+                       const int src_ld, const __constant int* src_offsets,
+                       __global const real* restrict src,
+                       const int dest_one, const int dest_two,
+                       const int dest_ld, const __constant int* dest_offsets,
+                       __global real* dest) {
+  const int batch = get_group_id(2);
+  const int src_offset = src_offsets[batch];
+  const int dest_offset = dest_offsets[batch];
+  real alpha; SetToOne(alpha);
+  _CopyMatrix(src_one, src_two, src_ld, src_offset, src,
+              dest_one, dest_two, dest_ld, dest_offset, dest,
+              alpha, 0, 0, 0);
+}
+
+#endif
 // =================================================================================================
 
 // End of the C++11 raw string literal

src/kernels/level3/transpose_pad.opencl

@@ -24,19 +24,15 @@ R"(
 
 // Transposes a matrix from source to destination. The output is padded with zero values in case the
 // destination matrix dimensions are larger than the transposed source matrix dimensions.
-__kernel __attribute__((reqd_work_group_size(PADTRA_TILE, PADTRA_TILE, 1)))
-void TransposePadMatrix(const int src_one, const int src_two,
-                        const int src_ld, const int src_offset,
-                        __global const real* restrict src,
-                        const int dest_one, const int dest_two,
-                        const int dest_ld, const int dest_offset,
-                        __global real* dest,
-                        const real_arg arg_alpha,
-                        const int do_conjugate) {
-  const real alpha = GetRealArg(arg_alpha);
-
-  // Local memory to store a tile of the matrix (for coalescing)
-  __local real tile[PADTRA_WPT*PADTRA_TILE][PADTRA_WPT*PADTRA_TILE + PADTRA_PAD];
+inline void _TransposePadMatrix(__local real* tile,
+                                const int src_one, const int src_two,
+                                const int src_ld, const int src_offset,
+                                __global const real* restrict src,
+                                const int dest_one, const int dest_two,
+                                const int dest_ld, const int dest_offset,
+                                __global real* dest,
+                                const real alpha,
+                                const int do_conjugate) {
 
   // Loop over the work per thread
   #pragma unroll
@@ -56,7 +52,9 @@ void TransposePadMatrix(const int src_one, const int src_two,
       if (id_src_two < src_two && id_src_one < src_one) {
         value = src[id_src_two*src_ld + id_src_one + src_offset];
       }
-      tile[get_local_id(1)*PADTRA_WPT + w_two][get_local_id(0)*PADTRA_WPT + w_one] = value;
+      const int tile_id0 = get_local_id(0)*PADTRA_WPT + w_one;
+      const int tile_id1 = get_local_id(1)*PADTRA_WPT + w_two;
+      tile[tile_id1 * (PADTRA_WPT*PADTRA_TILE + PADTRA_PAD) + tile_id0] = value;
     }
   }
 
@@ -75,7 +73,9 @@ void TransposePadMatrix(const int src_one, const int src_two,
 
       // Stores the transposed value in the destination matrix
       if ((id_dest_one < dest_one) && (id_dest_two < dest_two)) {
-        real value = tile[get_local_id(0)*PADTRA_WPT + w_two][get_local_id(1)*PADTRA_WPT + w_one];
+        const int tile_id0 = get_local_id(1)*PADTRA_WPT + w_one;
+        const int tile_id1 = get_local_id(0)*PADTRA_WPT + w_two;
+        real value = tile[tile_id1 * (PADTRA_WPT*PADTRA_TILE + PADTRA_PAD) + tile_id0];
         if (do_conjugate == 1) { COMPLEX_CONJUGATE(value); }
         Multiply(dest[id_dest_two*dest_ld + id_dest_one + dest_offset], alpha, value);
       }
@@ -83,25 +83,38 @@ void TransposePadMatrix(const int src_one, const int src_two,
   }
 }
 
+// Interface to the above function
+__kernel __attribute__((reqd_work_group_size(PADTRA_TILE, PADTRA_TILE, 1)))
+void TransposePadMatrix(const int src_one, const int src_two,
+                        const int src_ld, const int src_offset,
+                        __global const real* restrict src,
+                        const int dest_one, const int dest_two,
+                        const int dest_ld, const int dest_offset,
+                        __global real* dest,
+                        const real_arg arg_alpha,
+                        const int do_conjugate) {
+  const real alpha = GetRealArg(arg_alpha);
+  __local real tile[(PADTRA_WPT*PADTRA_TILE) * (PADTRA_WPT*PADTRA_TILE + PADTRA_PAD)];
+  _TransposePadMatrix(tile, src_one, src_two, src_ld, src_offset, src,
+                      dest_one, dest_two, dest_ld, dest_offset, dest,
+                      alpha, do_conjugate);
+}
+
 // =================================================================================================
 
 // Transposes a matrix, while considering possible padding in the source matrix. Data is read from a
 // padded source matrix, but only the actual data is written back to the transposed destination
 // matrix. This kernel optionally checks for upper/lower triangular matrices.
-__kernel __attribute__((reqd_work_group_size(PADTRA_TILE, PADTRA_TILE, 1)))
-void TransposeMatrix(const int src_one, const int src_two,
-                     const int src_ld, const int src_offset,
-                     __global const real* restrict src,
-                     const int dest_one, const int dest_two,
-                     const int dest_ld, const int dest_offset,
-                     __global real* dest,
-                     const real_arg arg_alpha,
-                     const int upper, const int lower,
-                     const int diagonal_imag_zero) {
-  const real alpha = GetRealArg(arg_alpha);
-
-  // Local memory to store a tile of the matrix (for coalescing)
-  __local real tile[PADTRA_WPT*PADTRA_TILE][PADTRA_WPT*PADTRA_TILE + PADTRA_PAD];
+inline void _TransposeMatrix(__local real* tile,
+                             const int src_one, const int src_two,
+                             const int src_ld, const int src_offset,
+                             __global const real* restrict src,
+                             const int dest_one, const int dest_two,
+                             const int dest_ld, const int dest_offset,
+                             __global real* dest,
+                             const real alpha,
+                             const int upper, const int lower,
+                             const int diagonal_imag_zero) {
 
   // Loop over the work per thread
   #pragma unroll
@@ -117,7 +130,9 @@ void TransposeMatrix(const int src_one, const int src_two,
       // Loads data into the local memory if the thread IDs are within bounds of the source matrix.
       if ((id_src_one < src_one) && (id_src_two < src_two)) {
         real value = src[id_src_two*src_ld + id_src_one + src_offset];
-        tile[get_local_id(1)*PADTRA_WPT + w_two][get_local_id(0)*PADTRA_WPT + w_one] = value;
+        const int tile_id0 = get_local_id(0)*PADTRA_WPT + w_one;
+        const int tile_id1 = get_local_id(1)*PADTRA_WPT + w_two;
+        tile[tile_id1 * (PADTRA_WPT*PADTRA_TILE + PADTRA_PAD) + tile_id0] = value;
       }
     }
   }
@@ -145,7 +160,9 @@ void TransposeMatrix(const int src_one, const int src_two,
 
         // Stores the transposed value in the destination matrix
         if ((id_dest_one < dest_one) && (id_dest_two < dest_two)) {
-          real value = tile[get_local_id(0)*PADTRA_WPT + w_two][get_local_id(1)*PADTRA_WPT + w_one];
+          const int tile_id0 = get_local_id(1)*PADTRA_WPT + w_one;
+          const int tile_id1 = get_local_id(0)*PADTRA_WPT + w_two;
+          real value = tile[tile_id1 * (PADTRA_WPT*PADTRA_TILE + PADTRA_PAD) + tile_id0];
           if (diagonal_imag_zero == 1 && id_dest_one == id_dest_two) { ImagToZero(value); }
           Multiply(dest[id_dest_two*dest_ld + id_dest_one + dest_offset], alpha, value);
         }
@@ -154,6 +171,65 @@ void TransposeMatrix(const int src_one, const int src_two,
   }
 }
 
+// Interface to the above function
+__kernel __attribute__((reqd_work_group_size(PADTRA_TILE, PADTRA_TILE, 1)))
+void TransposeMatrix(const int src_one, const int src_two,
+                     const int src_ld, const int src_offset,
+                     __global const real* restrict src,
+                     const int dest_one, const int dest_two,
+                     const int dest_ld, const int dest_offset,
+                     __global real* dest,
+                     const real_arg arg_alpha,
+                     const int upper, const int lower,
+                     const int diagonal_imag_zero) {
+  const real alpha = GetRealArg(arg_alpha);
+  __local real tile[(PADTRA_WPT*PADTRA_TILE) * (PADTRA_WPT*PADTRA_TILE + PADTRA_PAD)];
+  _TransposeMatrix(tile, src_one, src_two, src_ld, src_offset, src,
+                   dest_one, dest_two, dest_ld, dest_offset, dest,
+                   alpha, upper, lower, diagonal_imag_zero);
+}
+
+// =================================================================================================
+#if defined(ROUTINE_GEMMBATCHED)
+
+// Batched version of the above
+__kernel __attribute__((reqd_work_group_size(PADTRA_TILE, PADTRA_TILE, 1)))
+void TransposePadMatrixBatched(const int src_one, const int src_two,
+                               const int src_ld, const __constant int* src_offsets,
+                               __global const real* restrict src,
+                               const int dest_one, const int dest_two,
+                               const int dest_ld, const __constant int* dest_offsets,
+                               __global real* dest,
+                               const int do_conjugate) {
+  const int batch = get_group_id(2);
+  const int src_offset = src_offsets[batch];
+  const int dest_offset = dest_offsets[batch];
+  real alpha; SetToOne(alpha);
+  __local real tile[(PADTRA_WPT*PADTRA_TILE) * (PADTRA_WPT*PADTRA_TILE + PADTRA_PAD)];
+  _TransposePadMatrix(tile, src_one, src_two, src_ld, src_offset, src,
+                      dest_one, dest_two, dest_ld, dest_offset, dest,
+                      alpha, do_conjugate);
+}
+
+// Batched version of the above
+__kernel __attribute__((reqd_work_group_size(PADTRA_TILE, PADTRA_TILE, 1)))
+void TransposeMatrixBatched(const int src_one, const int src_two,
+                            const int src_ld, const __constant int* src_offsets,
+                            __global const real* restrict src,
+                            const int dest_one, const int dest_two,
+                            const int dest_ld, const __constant int* dest_offsets,
+                            __global real* dest) {
+  const int batch = get_group_id(2);
+  const int src_offset = src_offsets[batch];
+  const int dest_offset = dest_offsets[batch];
+  real alpha; SetToOne(alpha);
+  __local real tile[(PADTRA_WPT*PADTRA_TILE) * (PADTRA_WPT*PADTRA_TILE + PADTRA_PAD)];
+  _TransposeMatrix(tile, src_one, src_two, src_ld, src_offset, src,
+                   dest_one, dest_two, dest_ld, dest_offset, dest,
+                   alpha, 0, 0, 0);
+}
+
+#endif
 // =================================================================================================
 
 // End of the C++11 raw string literal

src/kernels/level3/xgemm_batched.opencl

@@ -0,0 +1,70 @@
+
+// =================================================================================================
+// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
+// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
+// width of 100 characters per line.
+//
+// Author(s):
+//   Cedric Nugteren <www.cedricnugteren.nl>
+//
+// This file contains the batched version of the non-direct GEMM kernel. See part 1 for information
+// about the non-batched version of the kernel.
+//
+// =================================================================================================
+
+// Enables loading of this file using the C++ pre-processor's #include (C++11 standard raw string
+// literal). Comment-out this line for syntax-highlighting when developing.
+R"(
+
+// =================================================================================================
+
+// Main entry point of the kernel. This is the regular full version.
+__kernel __attribute__((reqd_work_group_size(MDIMC, NDIMC, 1)))
+void XgemmBatched(const int kSizeM, const int kSizeN, const int kSizeK,
+                  const __constant real_arg* arg_alphas,
+                  const __constant real_arg* arg_betas,
+                  const __global realM* restrict agm, const int a_one, const int a_two,
+                  const __global realN* restrict bgm, const int b_one, const int b_two,
+                  __global realM* cgm, const int c_one, const int c_two) {
+  const int batch = get_group_id(2);
+  const real alpha = GetRealArg(arg_alphas[batch]);
+  const real beta = GetRealArg(arg_betas[batch]);
+
+  // Sets the offsets
+  const int a_offset = batch * a_one * a_two;
+  const int b_offset = batch * b_one * b_two;
+  const int c_offset = batch * c_one * c_two;
+  const __global realM* restrict agm_ = &agm[a_offset / VWM];
+  const __global realN* restrict bgm_ = &bgm[b_offset / VWN];
+  __global realM* restrict cgm_ = &cgm[c_offset / VWM];
+
+  // Allocates workgroup-private memory (local memory)
+  #if SA == 1
+    __local realM alm[KWG * MWG/VWM];
+  #endif
+  #if SB == 1
+    __local realN blm[KWG * NWG/VWN];
+  #endif
+
+  // Computes the matrix-multiplication and stores the result in register memory
+  realM cpm[NWI][MWI/VWM];
+  #if SA == 1 && SB == 1
+    XgemmBody(kSizeM, kSizeN, kSizeK, agm_, bgm_, cgm_, cpm, alm, blm);
+  #elif SA == 1
+    XgemmBody(kSizeM, kSizeN, kSizeK, agm_, bgm_, cgm_, cpm, alm);
+  #elif SB == 1
+    XgemmBody(kSizeM, kSizeN, kSizeK, agm_, bgm_, cgm_, cpm, blm);
+  #else
+    XgemmBody(kSizeM, kSizeN, kSizeK, agm_, bgm_, cgm_, cpm);
+  #endif
+
+  // Stores an MWG * NWG tile of results and performs the multiplication with alpha and beta
+  StoreResults(cgm_, cpm, kSizeM, alpha, beta);
+}
+
+// =================================================================================================
+
+// End of the C++11 raw string literal
+)"
+
+// =================================================================================================

src/kernels/level3/xgemm_direct_batched.opencl

@@ -0,0 +1,110 @@
+
+// =================================================================================================
+// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
+// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
+// width of 100 characters per line.
+//
+// Author(s):
+//   Cedric Nugteren <www.cedricnugteren.nl>
+//
+// This file contains the batched version of the direct GEMM kernels. See part 1 for information
+// about the non-batched version of the kernel.
+//
+// =================================================================================================
+
+// Enables loading of this file using the C++ pre-processor's #include (C++11 standard raw string
+// literal). Comment-out this line for syntax-highlighting when developing.
+R"(
+
+// =================================================================================================
+
+// Direct version of the batched GEMM kernel with [A, B] = [non-transposed, non-transposed]
+__attribute__((reqd_work_group_size(MDIMCD, NDIMCD, 1)))
+__kernel void XgemmDirectBatchedNN(const int kSizeM, const int kSizeN, const int kSizeK,
+                                   const __constant real_arg* arg_alphas, const __constant real_arg* arg_betas,
+                                   const __global realMD* restrict agm, const __constant int* a_offsets, const int a_ld,
+                                   const __global realND* restrict bgm, const __constant int* b_offsets, const int b_ld,
+                                   __global real* cgm, const __constant int* c_offsets, const int c_ld,
+                                   const int c_transpose, const int a_conjugate, const int b_conjugate) {
+  const int batch = get_group_id(2);
+  const real_arg arg_alpha = arg_alphas[batch];
+  const real_arg arg_beta = arg_betas[batch];
+  const int a_offset = a_offsets[batch];
+  const int b_offset = b_offsets[batch];
+  const int c_offset = c_offsets[batch];
+  __local real alm[WGD * (WGD + PADA)];
+  __local real blm[WGD * (WGD + PADB)];
+  XgemmDirect(kSizeM, kSizeN, kSizeK, arg_alpha, arg_beta,
+              agm, a_offset, a_ld, bgm, b_offset, b_ld, cgm, c_offset, c_ld,
+              alm, blm, 0, 0, c_transpose, a_conjugate, b_conjugate);
+}
+
+// Direct version of the batched GEMM kernel with [A, B] = [non-transposed, transposed]
+__attribute__((reqd_work_group_size(MDIMCD, NDIMCD, 1)))
+__kernel void XgemmDirectBatchedNT(const int kSizeM, const int kSizeN, const int kSizeK,
+                                   const __constant real_arg* arg_alphas, const __constant real_arg* arg_betas,
+                                   const __global realMD* restrict agm, const __constant int* a_offsets, const int a_ld,
+                                   const __global realND* restrict bgm, const __constant int* b_offsets, const int b_ld,
+                                   __global real* cgm, const __constant int* c_offsets, const int c_ld,
+                                   const int c_transpose, const int a_conjugate, const int b_conjugate) {
+  const int batch = get_group_id(2);
+  const real_arg arg_alpha = arg_alphas[batch];
+  const real_arg arg_beta = arg_betas[batch];
+  const int a_offset = a_offsets[batch];
+  const int b_offset = b_offsets[batch];
+  const int c_offset = c_offsets[batch];
+  __local real alm[WGD * (WGD + PADA)];
+  __local real blm[WGD * (WGD + PADB)];
+  XgemmDirect(kSizeM, kSizeN, kSizeK, arg_alpha, arg_beta,
+              agm, a_offset, a_ld, bgm, b_offset, b_ld, cgm, c_offset, c_ld,
+              alm, blm, 0, 1, c_transpose, a_conjugate, b_conjugate);
+}
+
+// Direct version of the batched GEMM kernel with [A, B] = [transposed, non-transposed]
+__attribute__((reqd_work_group_size(MDIMCD, NDIMCD, 1)))
+__kernel void XgemmDirectBatchedTN(const int kSizeM, const int kSizeN, const int kSizeK,
+                                   const __constant real_arg* arg_alphas, const __constant real_arg* arg_betas,
+                                   const __global realMD* restrict agm, const __constant int* a_offsets, const int a_ld,
+                                   const __global realND* restrict bgm, const __constant int* b_offsets, const int b_ld,
+                                   __global real* cgm, const __constant int* c_offsets, const int c_ld,
+                                   const int c_transpose, const int a_conjugate, const int b_conjugate) {
+  const int batch = get_group_id(2);
+  const real_arg arg_alpha = arg_alphas[batch];
+  const real_arg arg_beta = arg_betas[batch];
+  const int a_offset = a_offsets[batch];
+  const int b_offset = b_offsets[batch];
+  const int c_offset = c_offsets[batch];
+  __local real alm[WGD * (WGD + PADA)];
+  __local real blm[WGD * (WGD + PADB)];
+  XgemmDirect(kSizeM, kSizeN, kSizeK, arg_alpha, arg_beta,
+              agm, a_offset, a_ld, bgm, b_offset, b_ld, cgm, c_offset, c_ld,
+              alm, blm, 1, 0, c_transpose, a_conjugate, b_conjugate);
+}
+
+// Direct version of the batched GEMM kernel with [A, B] = [transposed, transposed]
+__attribute__((reqd_work_group_size(MDIMCD, NDIMCD, 1)))
+__kernel void XgemmDirectBatchedTT(const int kSizeM, const int kSizeN, const int kSizeK,
+                                   const __constant real_arg* arg_alphas, const __constant real_arg* arg_betas,
+                                   const __global realMD* restrict agm, const __constant int* a_offsets, const int a_ld,
+                                   const __global realND* restrict bgm, const __constant int* b_offsets, const int b_ld,
+                                   __global real* cgm, const __constant int* c_offsets, const int c_ld,
+                                   const int c_transpose, const int a_conjugate, const int b_conjugate) {
+  const int batch = get_group_id(2);
+  const real_arg arg_alpha = arg_alphas[batch];
+  const real_arg arg_beta = arg_betas[batch];
+  const int a_offset = a_offsets[batch];
+  const int b_offset = b_offsets[batch];
+  const int c_offset = c_offsets[batch];
+  __local real alm[WGD * (WGD + PADA)];
+  __local real blm[WGD * (WGD + PADB)];
+  XgemmDirect(kSizeM, kSizeN, kSizeK, arg_alpha, arg_beta,
+              agm, a_offset, a_ld, bgm, b_offset, b_ld, cgm, c_offset, c_ld,
+              alm, blm, 1, 1, c_transpose, a_conjugate, b_conjugate);
+}
+
+// =================================================================================================
+
+// End of the C++11 raw string literal
+)"
+
+// =================================================================================================

src/routines/common.hpp

@@ -196,6 +196,70 @@ void PadCopyTransposeMatrix(Queue &queue, const Device &device,
   }
 }
 
+// Batched version of the above
+template <typename T>
+void PadCopyTransposeMatrixBatched(Queue &queue, const Device &device,
+                                   const Databases &db,
+                                   EventPointer event, const std::vector<Event> &waitForEvents,
+                                   const size_t src_one, const size_t src_two,
+                                   const size_t src_ld, const Buffer<int> &src_offsets,
+                                   const Buffer<T> &src,
+                                   const size_t dest_one, const size_t dest_two,
+                                   const size_t dest_ld, const Buffer<int> &dest_offsets,
+                                   const Buffer<T> &dest,
+                                   const Program &program, const bool do_pad,
+                                   const bool do_transpose, const bool do_conjugate,
+                                   const size_t batch_count) {
+
+  // Determines the right kernel
+  auto kernel_name = std::string{};
+  if (do_transpose) {
+    kernel_name = (do_pad) ? "TransposePadMatrixBatched" : "TransposeMatrixBatched";
+  }
+  else {
+    kernel_name = (do_pad) ? "CopyPadMatrixBatched" : "CopyMatrixBatched";
+  }
+
+  // Retrieves the kernel from the compiled binary
+  auto kernel = Kernel(program, kernel_name);
+
+  // Sets the kernel arguments
+  kernel.SetArgument(0, static_cast<int>(src_one));
+  kernel.SetArgument(1, static_cast<int>(src_two));
+  kernel.SetArgument(2, static_cast<int>(src_ld));
+  kernel.SetArgument(3, src_offsets());
+  kernel.SetArgument(4, src());
+  kernel.SetArgument(5, static_cast<int>(dest_one));
+  kernel.SetArgument(6, static_cast<int>(dest_two));
+  kernel.SetArgument(7, static_cast<int>(dest_ld));
+  kernel.SetArgument(8, dest_offsets());
+  kernel.SetArgument(9, dest());
+  if (do_pad) {
+    kernel.SetArgument(10, static_cast<int>(do_conjugate));
+  }
+
+  // Launches the kernel and returns the error code. Uses global and local thread sizes based on
+  // parameters in the database.
+  if (do_transpose) {
+    const auto global = std::vector<size_t>{
+      Ceil(CeilDiv(dest_one, db["PADTRA_WPT"]), db["PADTRA_TILE"]),
+      Ceil(CeilDiv(dest_two, db["PADTRA_WPT"]), db["PADTRA_TILE"]),
+      batch_count
+    };
+    const auto local = std::vector<size_t>{db["PADTRA_TILE"], db["PADTRA_TILE"], 1};
+    RunKernel(kernel, queue, device, global, local, event, waitForEvents);
+  }
+  else {
+    const auto global = std::vector<size_t>{
+      Ceil(CeilDiv(dest_one, db["PAD_WPTX"]), db["PAD_DIMX"]),
+      Ceil(CeilDiv(dest_two, db["PAD_WPTY"]), db["PAD_DIMY"]),
+      batch_count
+    };
+    const auto local = std::vector<size_t>{db["PAD_DIMX"], db["PAD_DIMY"], 1};
+    RunKernel(kernel, queue, device, global, local, event, waitForEvents);
+  }
+}
+
 // =================================================================================================
 } // namespace clblast
 

src/routines/level3/xgemm.cpp

@@ -104,19 +104,19 @@ void Xgemm<T>::DoGemm(const Layout layout,
   // Selects which version of GEMM to run
   const auto do_gemm_direct = (m * n * k < db_["XGEMM_MIN_INDIRECT_SIZE"]);
   if (do_gemm_direct) { // for small sizes (single kernel)
-    return GemmDirect(m, n, k, alpha,
-                      a_buffer, a_offset, a_ld, b_buffer, b_offset, b_ld, beta,
-                      c_buffer, c_offset, c_ld,
-                      a_do_transpose, b_do_transpose, c_do_transpose, a_conjugate, b_conjugate);
+    GemmDirect(m, n, k, alpha,
+               a_buffer, a_offset, a_ld, b_buffer, b_offset, b_ld, beta,
+               c_buffer, c_offset, c_ld,
+               a_do_transpose, b_do_transpose, c_do_transpose, a_conjugate, b_conjugate);
   }
   else { // for larger sizes (pre/post-processing plus a very fast kernel)
-    return GemmIndirect(m, n, k, alpha,
-                        a_buffer, a_offset, a_ld, b_buffer, b_offset, b_ld, beta,
-                        c_buffer, c_offset, c_ld,
-                        a_do_transpose, b_do_transpose, c_do_transpose, a_conjugate, b_conjugate,
-                        a_one, a_two, a_want_rotated,
-                        b_one, b_two, b_want_rotated,
-                        c_one, c_two, c_want_rotated);
+    GemmIndirect(m, n, k, alpha,
+                 a_buffer, a_offset, a_ld, b_buffer, b_offset, b_ld, beta,
+                 c_buffer, c_offset, c_ld,
+                 a_do_transpose, b_do_transpose, c_do_transpose, a_conjugate, b_conjugate,
+                 a_one, a_two, a_want_rotated,
+                 b_one, b_two, b_want_rotated,
+                 c_one, c_two, c_want_rotated);
   }
 }
 

src/routines/levelx/xgemmbatched.cpp

@@ -0,0 +1,350 @@
+
+// =================================================================================================
+// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
+// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
+// width of 100 characters per line.
+//
+// Author(s):
+//   Cedric Nugteren <www.cedricnugteren.nl>
+//
+// This file implements the XgemmBatched class (see the header for information about the class).
+//
+// =================================================================================================
+
+#include "routines/levelx/xgemmbatched.hpp"
+
+#include <string>
+#include <vector>
+
+namespace clblast {
+// =================================================================================================
+
+// Constructor: forwards to base class constructor
+template <typename T>
+XgemmBatched<T>::XgemmBatched(Queue &queue, EventPointer event, const std::string &name):
+    Routine(queue, event, name,
+            {"Copy","Pad","Transpose","Padtranspose","Xgemm","XgemmDirect","KernelSelection"},
+            PrecisionValue<T>(), {}, {
+    #include "../../kernels/level3/level3.opencl"
+    #include "../../kernels/level3/copy_fast.opencl"
+    #include "../../kernels/level3/copy_pad.opencl"
+    #include "../../kernels/level3/transpose_fast.opencl"
+    #include "../../kernels/level3/transpose_pad.opencl"
+    , // separated in multiple parts to prevent C1091 in MSVC 2013
+    #include "../../kernels/level3/xgemm_direct_part1.opencl"
+    #include "../../kernels/level3/xgemm_direct_part2.opencl"
+    #include "../../kernels/level3/xgemm_direct_part3.opencl"
+    , // separated in multiple parts to prevent C1091 in MSVC 2013
+    #include "../../kernels/level3/xgemm_part1.opencl"
+    #include "../../kernels/level3/xgemm_part2.opencl"
+    #include "../../kernels/level3/xgemm_part3.opencl"
+    , // separated in multiple parts to prevent C1091 in MSVC 2013
+    #include "../../kernels/level3/xgemm_batched.opencl"
+    #include "../../kernels/level3/xgemm_direct_batched.opencl"
+    }) {
+}
+
+// =================================================================================================
+
+// The main routine
+template <typename T>
+void XgemmBatched<T>::DoGemmBatched(const Layout layout, const Transpose a_transpose, const Transpose b_transpose,
+                                    const size_t m, const size_t n, const size_t k,
+                                    const std::vector<T> &alphas,
+                                    const Buffer<T> & a_buffer, const std::vector<size_t> &a_offsets, const size_t a_ld,
+                                    const Buffer<T> & b_buffer, const std::vector<size_t> &b_offsets, const size_t b_ld,
+                                    const std::vector<T> &betas,
+                                    const Buffer<T> & c_buffer, const std::vector<size_t> &c_offsets, const size_t c_ld,
+                                    const size_t batch_count) {
+
+  // Tests for a valid batch count
+  if ((batch_count < 1) || (alphas.size() != batch_count) || (betas.size() != batch_count) ||
+      (a_offsets.size() != batch_count) || (b_offsets.size() != batch_count) || (c_offsets.size() != batch_count)) {
+    throw BLASError(StatusCode::kInvalidBatchCount);
+  }
+
+  // Makes sure all dimensions are larger than zero
+  if ((m == 0) || (n == 0) || (k == 0)) { throw BLASError(StatusCode::kInvalidDimension); }
+
+  // Computes whether or not the matrices are transposed in memory. See GEMM routine for details.
+  const auto a_rotated = (layout == Layout::kColMajor && a_transpose != Transpose::kNo) ||
+                         (layout == Layout::kRowMajor && a_transpose == Transpose::kNo);
+  const auto b_rotated = (layout == Layout::kColMajor && b_transpose != Transpose::kNo) ||
+                         (layout == Layout::kRowMajor && b_transpose == Transpose::kNo);
+  const auto c_rotated = (layout == Layout::kRowMajor);
+  static const auto a_want_rotated = false;
+  static const auto b_want_rotated = true;
+  static const auto c_want_rotated = false;
+  const auto a_do_transpose = a_rotated != a_want_rotated;
+  const auto b_do_transpose = b_rotated != b_want_rotated;
+  const auto c_do_transpose = c_rotated != c_want_rotated;
+
+  // In case of complex data-types, the transpose can also become a conjugate transpose
+  const auto a_conjugate = (a_transpose == Transpose::kConjugate);
+  const auto b_conjugate = (b_transpose == Transpose::kConjugate);
+
+  // Computes the first and second dimensions of the 3 matrices taking into account whether the
+  // matrices are rotated or not
+  const auto a_one = (a_rotated) ? k : m;
+  const auto a_two = (a_rotated) ? m : k;
+  const auto b_one = (b_rotated) ? n : k;
+  const auto b_two = (b_rotated) ? k : n;
+  const auto c_one = (c_rotated) ? n : m;
+  const auto c_two = (c_rotated) ? m : n;
+
+  // Tests the matrices for validity
+  for (auto batch = size_t{0}; batch < batch_count; ++batch) {
+    TestMatrixA(a_one, a_two, a_buffer, a_offsets[batch], a_ld);
+    TestMatrixB(b_one, b_two, b_buffer, b_offsets[batch], b_ld);
+    TestMatrixC(c_one, c_two, c_buffer, c_offsets[batch], c_ld);
+  }
+
+  // Upload the scalar arguments to the device
+  auto alphas_device = Buffer<T>(context_, BufferAccess::kReadOnly, batch_count);
+  auto betas_device = Buffer<T>(context_, BufferAccess::kReadOnly, batch_count);
+  alphas_device.Write(queue_, batch_count, alphas);
+  betas_device.Write(queue_, batch_count, betas);
+
+  // Converts the offset to integers
+  std::vector<int> a_offsets_int(a_offsets.begin(), a_offsets.end());
+  std::vector<int> b_offsets_int(b_offsets.begin(), b_offsets.end());
+  std::vector<int> c_offsets_int(c_offsets.begin(), c_offsets.end());
+
+  // Selects which version of the batched GEMM to run
+  const auto do_gemm_direct = true;
+  if (do_gemm_direct) { // single generic kernel
+    BatchedGemmDirect(m, n, k, alphas_device,
+                      a_buffer, a_offsets_int, a_ld, b_buffer, b_offsets_int, b_ld,
+                      betas_device, c_buffer, c_offsets_int, c_ld,
+                      a_do_transpose, b_do_transpose, c_do_transpose, a_conjugate, b_conjugate,
+                      batch_count);
+  }
+  else { // pre/post-processing plus a very fast kernel
+    BatchedGemmIndirect(m, n, k, alphas_device,
+                        a_buffer, a_offsets_int, a_ld, b_buffer, b_offsets_int, b_ld,
+                        betas_device, c_buffer, c_offsets_int, c_ld,
+                        a_do_transpose, b_do_transpose, c_do_transpose, a_conjugate, b_conjugate,
+                        a_one, a_two, a_want_rotated,
+                        b_one, b_two, b_want_rotated,
+                        c_one, c_two, c_want_rotated,
+                        batch_count);
+  }
+}
+
+
+// =================================================================================================
+
+// The indirect version of batched GEMM. This uses the faster but non-general kernel. It has specific
+// requirements, but several pre and post-processing kernels take care of those. However, the
+// overhead of these extra kernels might not be ideal for certain devices/arguments.
+template <typename T>
+void XgemmBatched<T>::BatchedGemmIndirect(const size_t m, const size_t n, const size_t k,
+                                          const Buffer<T> &alphas,
+                                          const Buffer<T> &a_buffer, const std::vector<int> &a_offsets, const size_t a_ld,
+                                          const Buffer<T> &b_buffer, const std::vector<int> &b_offsets, const size_t b_ld,
+                                          const Buffer<T> &betas,
+                                          const Buffer<T> &c_buffer, const std::vector<int> &c_offsets, const size_t c_ld,
+                                          const bool a_do_transpose, const bool b_do_transpose, const bool c_do_transpose,
+                                          const bool a_conjugate, const bool b_conjugate,
+                                          const size_t a_one, const size_t a_two, const bool a_want_rotated,
+                                          const size_t b_one, const size_t b_two, const bool b_want_rotated,
+                                          const size_t c_one, const size_t c_two, const bool c_want_rotated,
+                                          const size_t batch_count) {
+  // Calculates the ceiled versions of m, n, and k
+  const auto m_ceiled = Ceil(Ceil(m, db_["MWG"]), db_["VWM"]);
+  const auto n_ceiled = Ceil(Ceil(n, db_["NWG"]), db_["VWN"]);
+  const auto k_ceiled = Ceil(Ceil(k, db_["KWG"]), db_["VWM"]);
+
+  // Computes the first and second "internal" (ceiled) dimensions of the 3 matrices taking into account
+  // whether the matrices need to be rotated or not for the kernel.
+  const auto a_one_i = (a_want_rotated) ? k_ceiled : m_ceiled;
+  const auto a_two_i = (a_want_rotated) ? m_ceiled : k_ceiled;
+  const auto b_one_i = (b_want_rotated) ? n_ceiled : k_ceiled;
+  const auto b_two_i = (b_want_rotated) ? k_ceiled : n_ceiled;
+  const auto c_one_i = (c_want_rotated) ? n_ceiled : m_ceiled;
+  const auto c_two_i = (c_want_rotated) ? m_ceiled : n_ceiled;
+
+  // Sets the "internal" offsets, i.e. the perfect offsets
+  auto a_offsets_i = std::vector<int>(batch_count);
+  auto b_offsets_i = std::vector<int>(batch_count);
+  auto c_offsets_i = std::vector<int>(batch_count);
+  for (auto batch = size_t{0}; batch < batch_count; ++batch) {
+    a_offsets_i[batch] = batch * a_one_i * a_two_i;
+    b_offsets_i[batch] = batch * b_one_i * b_two_i;
+    c_offsets_i[batch] = batch * c_one_i * c_two_i;
+  }
+
+  // Determines whether or not temporary matrices are needed
+  auto a_no_temp = a_one == a_one_i && a_two == a_two_i && a_ld == a_one && a_offsets == a_offsets_i &&
+                   a_do_transpose == false && a_conjugate == false;
+  auto b_no_temp = b_one == b_one_i && b_two == b_two_i && b_ld == b_one && b_offsets == b_offsets_i &&
+                   b_do_transpose == false && b_conjugate == false;
+  auto c_no_temp = c_one == c_one_i && c_two == c_two_i && c_ld == c_one && c_offsets == c_offsets_i &&
+                   c_do_transpose == false;
+
+  // Creates the temporary matrices
+  const auto a_temp = (a_no_temp) ? a_buffer : Buffer<T>(context_, batch_count * a_one_i * a_two_i);
+  const auto b_temp = (b_no_temp) ? b_buffer : Buffer<T>(context_, batch_count * b_one_i * b_two_i);
+  const auto c_temp = (c_no_temp) ? c_buffer : Buffer<T>(context_, batch_count * c_one_i * c_two_i);
+
+  // Events of all kernels (including pre/post processing kernels)
+  auto eventWaitList = std::vector<Event>();
+  auto emptyEventList = std::vector<Event>();
+
+  // Runs the pre-processing kernel for matrix A. This transposes the matrix, but also pads zeros
+  // to fill it up until it reaches a certain multiple of size (kernel parameter dependent). In
+  // case nothing has to be done, these kernels can be skipped.
+  if (!a_no_temp) {
+    auto a_offsets_device = Buffer<int>(context_, BufferAccess::kReadOnly, batch_count);
+    auto a_offsets_i_device = Buffer<int>(context_, BufferAccess::kReadOnly, batch_count);
+    a_offsets_device.Write(queue_, batch_count, a_offsets);
+    a_offsets_i_device.Write(queue_, batch_count, a_offsets_i);
+    auto eventProcessA = Event();
+    PadCopyTransposeMatrixBatched(queue_, device_, db_, eventProcessA.pointer(), emptyEventList,
+                                  a_one, a_two, a_ld, a_offsets_device, a_buffer,
+                                  a_one_i, a_two_i, a_one_i, a_offsets_i_device, a_temp,
+                                  program_, true, a_do_transpose, a_conjugate, batch_count);
+    eventWaitList.push_back(eventProcessA);
+  }
+
+  // As above, but now for matrix B
+  if (!b_no_temp) {
+    auto b_offsets_device = Buffer<int>(context_, BufferAccess::kReadOnly, batch_count);
+    auto b_offsets_i_device = Buffer<int>(context_, BufferAccess::kReadOnly, batch_count);
+    b_offsets_device.Write(queue_, batch_count, b_offsets);
+    b_offsets_i_device.Write(queue_, batch_count, b_offsets_i);
+    auto eventProcessB = Event();
+    PadCopyTransposeMatrixBatched(queue_, device_, db_, eventProcessB.pointer(), emptyEventList,
+                                  b_one, b_two, b_ld, b_offsets_device, b_buffer,
+                                  b_one_i, b_two_i, b_one_i, b_offsets_i_device, b_temp,
+                                  program_, true, b_do_transpose, b_conjugate, batch_count);
+    eventWaitList.push_back(eventProcessB);
+  }
+
+  // As above, but now for matrix C
+  auto c_offsets_device = Buffer<int>(context_, BufferAccess::kReadOnly, batch_count);
+  auto c_offsets_i_device = Buffer<int>(context_, BufferAccess::kReadOnly, batch_count);
+  if (!c_no_temp) {
+    c_offsets_device.Write(queue_, batch_count, c_offsets);
+    c_offsets_i_device.Write(queue_, batch_count, c_offsets_i);
+    auto eventProcessC = Event();
+    PadCopyTransposeMatrixBatched(queue_, device_, db_, eventProcessC.pointer(), emptyEventList,
+                                  c_one, c_two, c_ld, c_offsets_device, c_buffer,
+                                  c_one_i, c_two_i, c_one_i, c_offsets_i_device, c_temp,
+                                  program_, true, c_do_transpose, false, batch_count);
+    eventWaitList.push_back(eventProcessC);
+  }
+
+  // Retrieves the Xgemm kernel from the compiled binary
+  auto kernel = Kernel(program_, "XgemmBatched");
+
+  // Sets the kernel arguments
+  kernel.SetArgument(0, static_cast<int>(m_ceiled));
+  kernel.SetArgument(1, static_cast<int>(n_ceiled));
+  kernel.SetArgument(2, static_cast<int>(k_ceiled));
+  kernel.SetArgument(3, alphas());
+  kernel.SetArgument(4, betas());
+  kernel.SetArgument(5, a_temp());
+  kernel.SetArgument(6, static_cast<int>(a_one_i));
+  kernel.SetArgument(7, static_cast<int>(a_two_i));
+  kernel.SetArgument(8, b_temp());
+  kernel.SetArgument(9, static_cast<int>(b_one_i));
+  kernel.SetArgument(10, static_cast<int>(b_two_i));
+  kernel.SetArgument(11, c_temp());
+  kernel.SetArgument(12, static_cast<int>(c_one_i));
+  kernel.SetArgument(13, static_cast<int>(c_two_i));
+
+  // Computes the global and local thread sizes
+  const auto global = std::vector<size_t>{
+    (c_one_i * db_["MDIMC"]) / db_["MWG"],
+    (c_two_i * db_["NDIMC"]) / db_["NWG"],
+    batch_count
+  };
+  const auto local = std::vector<size_t>{db_["MDIMC"], db_["NDIMC"], 1};
+
+  // Launches the kernel
+  auto eventKernel = Event();
+  auto eventPointer = eventKernel.pointer();
+  RunKernel(kernel, queue_, device_, global, local, eventPointer, eventWaitList);
+
+  // Runs the post-processing kernel if needed
+  if (!c_no_temp) {
+    eventWaitList.push_back(eventKernel);
+    PadCopyTransposeMatrixBatched(queue_, device_, db_, event_, eventWaitList,
+                                  c_one_i, c_two_i, c_one_i, c_offsets_i_device, c_temp,
+                                  c_one, c_two, c_ld, c_offsets_device, c_buffer,
+                                  program_, false, c_do_transpose, false, batch_count);
+  }
+}
+
+// =================================================================================================
+
+// The direct version of batched GEMM, requiring just one kernel, no pre or post-processing kernels.
+template <typename T>
+void XgemmBatched<T>::BatchedGemmDirect(const size_t m, const size_t n, const size_t k,
+                                        const Buffer<T> &alphas,
+                                        const Buffer<T> &a_buffer, const std::vector<int> &a_offsets, const size_t a_ld,
+                                        const Buffer<T> &b_buffer, const std::vector<int> &b_offsets, const size_t b_ld,
+                                        const Buffer<T> &betas,
+                                        const Buffer<T> &c_buffer, const std::vector<int> &c_offsets, const size_t c_ld,
+                                        const bool a_do_transpose, const bool b_do_transpose, const bool c_do_transpose,
+                                        const bool a_conjugate, const bool b_conjugate,
+                                        const size_t batch_count) {
+
+  // Uploads the offsets to the device
+  auto a_offsets_device = Buffer<int>(context_, BufferAccess::kReadOnly, batch_count);
+  auto b_offsets_device = Buffer<int>(context_, BufferAccess::kReadOnly, batch_count);
+  auto c_offsets_device = Buffer<int>(context_, BufferAccess::kReadOnly, batch_count);
+  a_offsets_device.Write(queue_, batch_count, a_offsets);
+  b_offsets_device.Write(queue_, batch_count, b_offsets);
+  c_offsets_device.Write(queue_, batch_count, c_offsets);
+
+  // Retrieves the proper XgemmDirect kernel from the compiled binary
+  const auto name = (a_do_transpose) ? (b_do_transpose ? "XgemmDirectBatchedTT" : "XgemmDirectBatchedTN") :
+                                       (b_do_transpose ? "XgemmDirectBatchedNT" : "XgemmDirectBatchedNN");
+  auto kernel = Kernel(program_, name);
+
+  // Sets the kernel arguments
+  kernel.SetArgument(0, static_cast<int>(m));
+  kernel.SetArgument(1, static_cast<int>(n));
+  kernel.SetArgument(2, static_cast<int>(k));
+  kernel.SetArgument(3, alphas());
+  kernel.SetArgument(4, betas());
+  kernel.SetArgument(5, a_buffer());
+  kernel.SetArgument(6, a_offsets_device());
+  kernel.SetArgument(7, static_cast<int>(a_ld));
+  kernel.SetArgument(8, b_buffer());
+  kernel.SetArgument(9, b_offsets_device());
+  kernel.SetArgument(10, static_cast<int>(b_ld));
+  kernel.SetArgument(11, c_buffer());
+  kernel.SetArgument(12, c_offsets_device());
+  kernel.SetArgument(13, static_cast<int>(c_ld));
+  kernel.SetArgument(14, static_cast<int>(c_do_transpose));
+  kernel.SetArgument(15, static_cast<int>(a_conjugate));
+  kernel.SetArgument(16, static_cast<int>(b_conjugate));
+
+  // Computes the global and local thread sizes
+  const auto m_ceiled = Ceil(m, db_["WGD"]);
+  const auto n_ceiled = Ceil(n, db_["WGD"]);
+  const auto global = std::vector<size_t>{
+    (m_ceiled * db_["MDIMCD"]) / db_["WGD"],
+    (n_ceiled * db_["NDIMCD"]) / db_["WGD"],
+    batch_count
+  };
+  const auto local = std::vector<size_t>{db_["MDIMCD"], db_["NDIMCD"], 1};
+
+  // Launches the kernel
+  RunKernel(kernel, queue_, device_, global, local, event_);
+}
+
+// =================================================================================================
+
+// Compiles the templated class
+template class XgemmBatched<half>;
+template class XgemmBatched<float>;
+template class XgemmBatched<double>;
+template class XgemmBatched<float2>;
+template class XgemmBatched<double2>;
+
+// =================================================================================================
+} // namespace clblast

src/routines/levelx/xgemmbatched.hpp

@@ -0,0 +1,72 @@
+
+// =================================================================================================
+// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
+// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
+// width of 100 characters per line.
+//
+// Author(s):
+//   Cedric Nugteren <www.cedricnugteren.nl>
+//
+// This file implements the XgemmBatched routine. This is a non-blas batched version of GEMM.
+//
+// =================================================================================================
+
+#ifndef CLBLAST_ROUTINES_XGEMMBATCHED_H_
+#define CLBLAST_ROUTINES_XGEMMBATCHED_H_
+
+#include <vector>
+
+#include "routine.hpp"
+
+namespace clblast {
+// =================================================================================================
+
+// See comment at top of file for a description of the class
+template <typename T>
+class XgemmBatched: public Routine {
+ public:
+
+  // Constructor
+  XgemmBatched(Queue &queue, EventPointer event, const std::string &name = "GEMMBATCHED");
+
+  // Templated-precision implementation of the routine
+  void DoGemmBatched(const Layout layout, const Transpose a_transpose, const Transpose b_transpose,
+                     const size_t m, const size_t n, const size_t k,
+                     const std::vector<T> &alphas,
+                     const Buffer<T> & a_buffer, const std::vector<size_t> &a_offsets, const size_t a_ld,
+                     const Buffer<T> & b_buffer, const std::vector<size_t> &b_offsets, const size_t b_ld,
+                     const std::vector<T> &betas,
+                     const Buffer<T> & c_buffer, const std::vector<size_t> &c_offsets, const size_t c_ld,
+                     const size_t batch_count);
+
+  // Indirect version of batched GEMM (with pre and post-processing kernels)
+  void BatchedGemmIndirect(const size_t m, const size_t n, const size_t k,
+                           const Buffer<T> &alphas,
+                           const Buffer<T> &a_buffer, const std::vector<int> &a_offsets, const size_t a_ld,
+                           const Buffer<T> &b_buffer, const std::vector<int> &b_offsets, const size_t b_ld,
+                           const Buffer<T> &betas,
+                           const Buffer<T> &c_buffer, const std::vector<int> &c_offsets, const size_t c_ld,
+                           const bool a_do_transpose, const bool b_do_transpose, const bool c_do_transpose,
+                           const bool a_conjugate, const bool b_conjugate,
+                           const size_t a_one, const size_t a_two, const bool a_want_rotated,
+                           const size_t b_one, const size_t b_two, const bool b_want_rotated,
+                           const size_t c_one, const size_t c_two, const bool c_want_rotated,
+                           const size_t batch_count);
+
+  // Direct version of batched GEMM (no pre and post-processing kernels)
+  void BatchedGemmDirect(const size_t m, const size_t n, const size_t k,
+                         const Buffer<T> &alphas,
+                         const Buffer<T> &a_buffer, const std::vector<int> &a_offsets, const size_t a_ld,
+                         const Buffer<T> &b_buffer, const std::vector<int> &b_offsets, const size_t b_ld,
+                         const Buffer<T> &betas,
+                         const Buffer<T> &c_buffer, const std::vector<int> &c_offsets, const size_t c_ld,
+                         const bool a_do_transpose, const bool b_do_transpose, const bool c_do_transpose,
+                         const bool a_conjugate, const bool b_conjugate,
+                         const size_t batch_count);
+};
+
+// =================================================================================================
+} // namespace clblast
+
+// CLBLAST_ROUTINES_XGEMMBATCHED_H_
+#endif

src/tuning/kernels/copy_fast.cpp

@@ -46,6 +46,7 @@ class TuneCopy {
   static size_t DefaultM() { return 1024; }
   static size_t DefaultN() { return 1024; }
   static size_t DefaultK() { return 1; } // N/A for this kernel
+  static size_t DefaultBatchCount() { return 1; } // N/A for this kernel
   static double DefaultFraction() { return 1.0; } // N/A for this kernel
   static size_t DefaultNumRuns() { return 2; } // run every kernel this many times for averaging
 

src/tuning/kernels/copy_pad.cpp

@@ -46,6 +46,7 @@ class TunePad {
   static size_t DefaultM() { return 1024; }
   static size_t DefaultN() { return 1024; }
   static size_t DefaultK() { return 1; } // N/A for this kernel
+  static size_t DefaultBatchCount() { return 1; } // N/A for this kernel
   static double DefaultFraction() { return 1.0; } // N/A for this kernel
   static size_t DefaultNumRuns() { return 2; } // run every kernel this many times for averaging
 

src/tuning/kernels/transpose_fast.cpp

@@ -46,6 +46,7 @@ class TuneTranspose {
   static size_t DefaultM() { return 1024; }
   static size_t DefaultN() { return 1024; }
   static size_t DefaultK() { return 1; } // N/A for this kernel
+  static size_t DefaultBatchCount() { return 1; } // N/A for this kernel
   static double DefaultFraction() { return 1.0; } // N/A for this kernel
   static size_t DefaultNumRuns() { return 2; } // run every kernel this many times for averaging
 

src/tuning/kernels/transpose_pad.cpp

@@ -46,6 +46,7 @@ class TunePadTranspose {
   static size_t DefaultM() { return 1024; }
   static size_t DefaultN() { return 1024; }
   static size_t DefaultK() { return 1; } // N/A for this kernel
+  static size_t DefaultBatchCount() { return 1; } // N/A for this kernel
   static double DefaultFraction() { return 1.0; } // N/A for this kernel
   static size_t DefaultNumRuns() { return 2; } // run every kernel this many times for averaging
 

src/tuning/kernels/xaxpy.cpp

@@ -50,6 +50,7 @@ class TuneXaxpy {
   static size_t DefaultM() { return 1; } // N/A for this kernel
   static size_t DefaultN() { return 4096*1024; }
   static size_t DefaultK() { return 1; } // N/A for this kernel
+  static size_t DefaultBatchCount() { return 1; } // N/A for this kernel
   static double DefaultFraction() { return 1.0; } // N/A for this kernel
   static size_t DefaultNumRuns() { return 2; } // run every kernel this many times for averaging
 

src/tuning/kernels/xdot.cpp

@@ -46,6 +46,7 @@ class TuneXdot {
   static size_t DefaultM() { return 1; } // N/A for this kernel
   static size_t DefaultN() { return 2*1024*1024; }
   static size_t DefaultK() { return 1; } // N/A for this kernel
+  static size_t DefaultBatchCount() { return 1; } // N/A for this kernel
   static double DefaultFraction() { return 1.0; } // N/A for this kernel
   static size_t DefaultNumRuns() { return 2; } // run every kernel this many times for averaging
 

src/tuning/kernels/xgemm.cpp

@@ -51,6 +51,7 @@ class TuneXgemm {
   static size_t DefaultM() { return 1024; }
   static size_t DefaultN() { return 1024; }
   static size_t DefaultK() { return 1024; }
+  static size_t DefaultBatchCount() { return 1; } // N/A for this kernel
   static double DefaultFraction() { return (V==1) ? 1.0 : 512.0; } // test all or sample randomly
   static size_t DefaultNumRuns() { return 2; } // run every kernel this many times for averaging
 

src/tuning/kernels/xgemm_direct.cpp

@@ -51,6 +51,7 @@ class TuneXgemmDirect {
   static size_t DefaultM() { return 256; }
   static size_t DefaultN() { return 256; }
   static size_t DefaultK() { return 256; }
+  static size_t DefaultBatchCount() { return 1; } // N/A for this kernel
   static double DefaultFraction() { return (V==1) ? 1.0 : 32.0; } // test all or sample randomly
   static size_t DefaultNumRuns() { return 4; } // run every kernel this many times for averaging
 

src/tuning/kernels/xgemv.cpp

@@ -49,6 +49,7 @@ class TuneXgemv {
   static size_t DefaultM() { return 2048; }
   static size_t DefaultN() { return 2048; }
   static size_t DefaultK() { return 1; } // N/A for this kernel
+  static size_t DefaultBatchCount() { return 1; } // N/A for this kernel
   static double DefaultFraction() { return 1.0; } // N/A for this kernel
   static size_t DefaultNumRuns() { return 2; } // run every kernel this many times for averaging
 

src/tuning/kernels/xger.cpp

@@ -46,6 +46,7 @@ class TuneXger {
   static size_t DefaultM() { return 1024; }
   static size_t DefaultN() { return 1024; }
   static size_t DefaultK() { return 1; } // N/A for this kernel
+  static size_t DefaultBatchCount() { return 1; } // N/A for this kernel
   static double DefaultFraction() { return 1.0; } // N/A for this kernel
   static size_t DefaultNumRuns() { return 2; } // run every kernel this many times for averaging
 

src/tuning/tuning.hpp

@@ -47,6 +47,7 @@ void Tuner(int argc, char* argv[]) {
     if (o == kArgAlpha)    { args.alpha    = GetArgument(command_line_args, help, kArgAlpha, GetScalar<T>()); }
     if (o == kArgBeta)     { args.beta     = GetArgument(command_line_args, help, kArgBeta, GetScalar<T>()); }
     if (o == kArgFraction) { args.fraction = GetArgument(command_line_args, help, kArgFraction, C::DefaultFraction()); }
+    if (o == kArgBatchCount) { args.batch_count = GetArgument(command_line_args, help, kArgBatchCount, C::DefaultBatchCount()); }
   }
   const auto num_runs = GetArgument(command_line_args, help, kArgNumRuns, C::DefaultNumRuns());
 
@@ -158,6 +159,7 @@ void Tuner(int argc, char* argv[]) {
     if (o == kArgK)     { metadata.push_back({"arg_k", std::to_string(args.k)}); }
     if (o == kArgAlpha) { metadata.push_back({"arg_alpha", ToString(args.alpha)}); }
     if (o == kArgBeta)  { metadata.push_back({"arg_beta", ToString(args.beta)}); }
+    if (o == kArgBatchCount) { metadata.push_back({"arg_batch_count", ToString(args.batch_count)}); }
   }
   tuner.PrintJSON("clblast_"+C::KernelFamily()+"_"+precision_string+".json", metadata);
 }

test/correctness/routines/levelx/xgemmbatched.cpp

@@ -0,0 +1,30 @@
+
+// =================================================================================================
+// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
+// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
+// width of 100 characters per line.
+//
+// Author(s):
+//   Cedric Nugteren <www.cedricnugteren.nl>
+//
+// =================================================================================================
+
+#include "test/correctness/testblas.hpp"
+#include "test/routines/levelx/xgemmbatched.hpp"
+
+// Shortcuts to the clblast namespace
+using float2 = clblast::float2;
+using double2 = clblast::double2;
+
+// Main function (not within the clblast namespace)
+int main(int argc, char *argv[]) {
+  auto errors = size_t{0};
+  errors += clblast::RunTests<clblast::TestXgemmBatched<float>, float, float>(argc, argv, false, "SGEMMBATCHED");
+  errors += clblast::RunTests<clblast::TestXgemmBatched<double>, double, double>(argc, argv, true, "DGEMMBATCHED");
+  errors += clblast::RunTests<clblast::TestXgemmBatched<float2>, float2, float2>(argc, argv, true, "CGEMMBATCHED");
+  errors += clblast::RunTests<clblast::TestXgemmBatched<double2>, double2, double2>(argc, argv, true, "ZGEMMBATCHED");
+  errors += clblast::RunTests<clblast::TestXgemmBatched<half>, half, half>(argc, argv, true, "HGEMMBATCHED");
+  if (errors > 0) { return 1; } else { return 0; }
+}
+
+// =================================================================================================

test/correctness/testblas.cpp

@@ -21,6 +21,8 @@
 namespace clblast {
 // =================================================================================================
 
+template <typename T, typename U> const auto TestBlas<T,U>::kSeed = 42; // fixed seed for reproducibility
+
 // Test settings for the regular test. Append to these lists in case more tests are required.
 template <typename T, typename U> const std::vector<size_t> TestBlas<T,U>::kVectorDims = { 7, 93, 4096 };
 template <typename T, typename U> const std::vector<size_t> TestBlas<T,U>::kIncrements = { 1, 2, 7 };

test/correctness/testblas.hpp

@@ -30,7 +30,7 @@ namespace clblast {
 template <typename T, typename U>
 class TestBlas: public Tester<T,U> {
  public:
-  static constexpr auto kSeed = 42; // fixed seed for reproducibility
+  static const int kSeed;
 
   // Uses several variables from the Tester class
   using Tester<T,U>::context_;

test/performance/client.cpp

@@ -24,6 +24,8 @@
 namespace clblast {
 // =================================================================================================
 
+template <typename T, typename U> const int Client<T,U>::kSeed = 42; // fixed seed for reproducibility
+
 // Constructor
 template <typename T, typename U>
 Client<T,U>::Client(const Routine run_routine,

test/performance/client.hpp

@@ -40,7 +40,7 @@ namespace clblast {
 template <typename T, typename U>
 class Client {
  public:
-  static constexpr auto kSeed = 42; // fixed seed for reproducibility
+  static const int kSeed;
 
   // Shorthand for the routine-specific functions passed to the tester
   using Routine = std::function<StatusCode(const Arguments<U>&, Buffers<T>&, Queue&)>;

test/performance/routines/levelx/xgemmbatched.cpp

@@ -0,0 +1,37 @@
+
+// =================================================================================================
+// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
+// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
+// width of 100 characters per line.
+//
+// Author(s):
+//   Cedric Nugteren <www.cedricnugteren.nl>
+//
+// =================================================================================================
+
+#include "test/performance/client.hpp"
+#include "test/routines/levelx/xgemmbatched.hpp"
+
+// Shortcuts to the clblast namespace
+using float2 = clblast::float2;
+using double2 = clblast::double2;
+
+// Main function (not within the clblast namespace)
+int main(int argc, char *argv[]) {
+  const auto command_line_args = clblast::RetrieveCommandLineArguments(argc, argv);
+  switch(clblast::GetPrecision(command_line_args, clblast::Precision::kSingle)) {
+    case clblast::Precision::kHalf:
+      clblast::RunClient<clblast::TestXgemmBatched<half>, half, half>(argc, argv); break;
+    case clblast::Precision::kSingle:
+      clblast::RunClient<clblast::TestXgemmBatched<float>, float, float>(argc, argv); break;
+    case clblast::Precision::kDouble:
+      clblast::RunClient<clblast::TestXgemmBatched<double>, double, double>(argc, argv); break;
+    case clblast::Precision::kComplexSingle:
+      clblast::RunClient<clblast::TestXgemmBatched<float2>, float2, float2>(argc, argv); break;
+    case clblast::Precision::kComplexDouble:
+      clblast::RunClient<clblast::TestXgemmBatched<double2>, double2, double2>(argc, argv); break;
+  }
+  return 0;
+}
+
+// =================================================================================================

test/routines/levelx/xgemmbatched.hpp

@@ -0,0 +1,207 @@
+
+// =================================================================================================
+// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
+// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
+// width of 100 characters per line.
+//
+// Author(s):
+//   Cedric Nugteren <www.cedricnugteren.nl>
+//
+// This file implements a class with static methods to describe the XgemmBatched routine. Examples of
+// such 'descriptions' are how to calculate the size a of buffer or how to run the routine. These
+// static methods are used by the correctness tester and the performance tester.
+//
+// =================================================================================================
+
+#ifndef CLBLAST_TEST_ROUTINES_XGEMMBATCHED_H_
+#define CLBLAST_TEST_ROUTINES_XGEMMBATCHED_H_
+
+#include <vector>
+#include <string>
+
+#ifdef CLBLAST_REF_CLBLAS
+  #include "test/wrapper_clblas.hpp"
+#endif
+#ifdef CLBLAST_REF_CBLAS
+  #include "test/wrapper_cblas.hpp"
+#endif
+
+namespace clblast {
+// =================================================================================================
+
+// See comment at top of file for a description of the class
+template <typename T>
+class TestXgemmBatched {
+ public:
+
+  // Although it is a non-BLAS routine, it can still be tested against level-3 routines in a loop
+  static size_t BLASLevel() { return 3; }
+
+  // The list of arguments relevant for this routine
+  static std::vector<std::string> GetOptions() {
+    return {kArgM, kArgN, kArgK,
+            kArgLayout, kArgATransp, kArgBTransp,
+            kArgALeadDim, kArgBLeadDim, kArgCLeadDim,
+            kArgAOffset, kArgBOffset, kArgCOffset,
+            kArgBatchCount, kArgAlpha, kArgBeta};
+  }
+
+  // Helper for the sizes per batch
+  static size_t PerBatchSizeA(const Arguments<T> &args) {
+    auto a_rotated = (args.layout == Layout::kColMajor && args.a_transpose != Transpose::kNo) ||
+                     (args.layout == Layout::kRowMajor && args.a_transpose == Transpose::kNo);
+    auto a_two = (a_rotated) ? args.m : args.k;
+    return a_two * args.a_ld;
+  }
+  static size_t PerBatchSizeB(const Arguments<T> &args) {
+    auto b_rotated = (args.layout == Layout::kColMajor && args.b_transpose != Transpose::kNo) ||
+                     (args.layout == Layout::kRowMajor && args.b_transpose == Transpose::kNo);
+    auto b_two = (b_rotated) ? args.k : args.n;
+    return b_two * args.b_ld;
+  }
+  static size_t PerBatchSizeC(const Arguments<T> &args) {
+    auto c_rotated = (args.layout == Layout::kRowMajor);
+    auto c_two = (c_rotated) ? args.m : args.n;
+    return c_two * args.c_ld;
+  }
+
+  // Describes how to obtain the sizes of the buffers
+  static size_t GetSizeA(const Arguments<T> &args) {
+    return PerBatchSizeA(args) * args.batch_count + args.a_offset;
+  }
+  static size_t GetSizeB(const Arguments<T> &args) {
+    return PerBatchSizeB(args) * args.batch_count + args.b_offset;
+  }
+  static size_t GetSizeC(const Arguments<T> &args) {
+    return PerBatchSizeC(args) * args.batch_count + args.c_offset;
+  }
+
+  // Describes how to set the sizes of all the buffers
+  static void SetSizes(Arguments<T> &args) {
+    args.a_size = GetSizeA(args);
+    args.b_size = GetSizeB(args);
+    args.c_size = GetSizeC(args);
+
+    // Also sets the batch-related variables
+    args.a_offsets = std::vector<size_t>(args.batch_count);
+    args.b_offsets = std::vector<size_t>(args.batch_count);
+    args.c_offsets = std::vector<size_t>(args.batch_count);
+    args.alphas = std::vector<T>(args.batch_count);
+    args.betas = std::vector<T>(args.batch_count);
+    for (auto batch = size_t{0}; batch < args.batch_count; ++batch) {
+      args.a_offsets[batch] = batch * PerBatchSizeA(args) + args.a_offset;
+      args.b_offsets[batch] = batch * PerBatchSizeB(args) + args.b_offset;
+      args.c_offsets[batch] = batch * PerBatchSizeC(args) + args.c_offset;
+      args.alphas[batch] = args.alpha + Constant<T>(batch);
+      args.betas[batch] = args.beta + Constant<T>(batch);
+    }
+  }
+
+  // Describes what the default values of the leading dimensions of the matrices are
+  static size_t DefaultLDA(const Arguments<T> &args) { return args.k; }
+  static size_t DefaultLDB(const Arguments<T> &args) { return args.n; }
+  static size_t DefaultLDC(const Arguments<T> &args) { return args.n; }
+
+  // Describes which transpose options are relevant for this routine
+  using Transposes = std::vector<Transpose>;
+  static Transposes GetATransposes(const Transposes &all) { return all; }
+  static Transposes GetBTransposes(const Transposes &all) { return all; }
+
+  // Describes how to prepare the input data
+  static void PrepareData(const Arguments<T>&, Queue&, const int, std::vector<T>&,
+                          std::vector<T>&, std::vector<T>&, std::vector<T>&, std::vector<T>&,
+                          std::vector<T>&, std::vector<T>&) {} // N/A for this routine
+
+  // Describes how to run the CLBlast routine
+  static StatusCode RunRoutine(const Arguments<T> &args, Buffers<T> &buffers, Queue &queue) {
+    auto queue_plain = queue();
+    auto event = cl_event{};
+    auto status = GemmBatched(args.layout, args.a_transpose, args.b_transpose,
+                              args.m, args.n, args.k, args.alphas.data(),
+                              buffers.a_mat(), args.a_offsets.data(), args.a_ld,
+                              buffers.b_mat(), args.b_offsets.data(), args.b_ld, args.betas.data(),
+                              buffers.c_mat(), args.c_offsets.data(), args.c_ld,
+                              args.batch_count,
+                              &queue_plain, &event);
+    if (status == StatusCode::kSuccess) { clWaitForEvents(1, &event); clReleaseEvent(event); }
+    return status;
+  }
+
+  // Describes how to run the clBLAS routine (for correctness/performance comparison)
+  #ifdef CLBLAST_REF_CLBLAS
+    static StatusCode RunReference1(const Arguments<T> &args, Buffers<T> &buffers, Queue &queue) {
+      auto queue_plain = queue();
+      for (auto batch = size_t{0}; batch < args.batch_count; ++batch) {
+        auto event = cl_event{};
+        auto status = clblasXgemm(convertToCLBLAS(args.layout),
+                                  convertToCLBLAS(args.a_transpose),
+                                  convertToCLBLAS(args.b_transpose),
+                                  args.m, args.n, args.k, args.alphas[batch],
+                                  buffers.a_mat, args.a_offsets[batch], args.a_ld,
+                                  buffers.b_mat, args.b_offsets[batch], args.b_ld, args.betas[batch],
+                                  buffers.c_mat, args.c_offsets[batch], args.c_ld,
+                                  1, &queue_plain, 0, nullptr, &event);
+        clWaitForEvents(1, &event);
+        if (static_cast<StatusCode>(status) != StatusCode::kSuccess) {
+          return static_cast<StatusCode>(status);
+        }
+      }
+      return StatusCode::kSuccess;
+    }
+  #endif
+
+  // Describes how to run the CPU BLAS routine (for correctness/performance comparison)
+  #ifdef CLBLAST_REF_CBLAS
+    static StatusCode RunReference2(const Arguments<T> &args, Buffers<T> &buffers, Queue &queue) {
+      std::vector<T> a_mat_cpu(args.a_size, static_cast<T>(0));
+      std::vector<T> b_mat_cpu(args.b_size, static_cast<T>(0));
+      std::vector<T> c_mat_cpu(args.c_size, static_cast<T>(0));
+      buffers.a_mat.Read(queue, args.a_size, a_mat_cpu);
+      buffers.b_mat.Read(queue, args.b_size, b_mat_cpu);
+      buffers.c_mat.Read(queue, args.c_size, c_mat_cpu);
+      for (auto batch = size_t{0}; batch < args.batch_count; ++batch) {
+        cblasXgemm(convertToCBLAS(args.layout),
+                   convertToCBLAS(args.a_transpose),
+                   convertToCBLAS(args.b_transpose),
+                   args.m, args.n, args.k, args.alphas[batch],
+                   a_mat_cpu, args.a_offsets[batch], args.a_ld,
+                   b_mat_cpu, args.b_offsets[batch], args.b_ld, args.betas[batch],
+                   c_mat_cpu, args.c_offsets[batch], args.c_ld);
+      }
+      buffers.c_mat.Write(queue, args.c_size, c_mat_cpu);
+      return StatusCode::kSuccess;
+    }
+  #endif
+
+  // Describes how to download the results of the computation (more importantly: which buffer)
+  static std::vector<T> DownloadResult(const Arguments<T> &args, Buffers<T> &buffers, Queue &queue) {
+    std::vector<T> result(args.c_size, static_cast<T>(0));
+    buffers.c_mat.Read(queue, args.c_size, result);
+    return result;
+  }
+
+  // Describes how to compute the indices of the result buffer
+  static size_t ResultID1(const Arguments<T> &args) { return args.m; }
+  static size_t ResultID2(const Arguments<T> &args) { return args.n * args.batch_count; }
+  static size_t GetResultIndex(const Arguments<T> &args, const size_t id1, const size_t id2_3) {
+    const size_t id2 = id2_3 % args.n;
+    const size_t id3 = id2_3 / args.n;
+    return (args.layout == Layout::kRowMajor) ?
+           id1*args.c_ld + id2 + args.c_offsets[id3]:
+           id2*args.c_ld + id1 + args.c_offsets[id3];
+  }
+
+  // Describes how to compute performance metrics
+  static size_t GetFlops(const Arguments<T> &args) {
+    return args.batch_count * (2 * args.m * args.n * args.k);
+  }
+  static size_t GetBytes(const Arguments<T> &args) {
+    return args.batch_count * (args.m*args.k + args.k*args.n + 2*args.m*args.n) * sizeof(T);
+  }
+};
+
+// =================================================================================================
+} // namespace clblast
+
+// CLBLAST_TEST_ROUTINES_XGEMMBATCHED_H_
+#endif