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226 lines
9.5 KiB
C++
226 lines
9.5 KiB
C++
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// =================================================================================================
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// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This
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// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max-
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// width of 100 characters per line.
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//
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// Author(s):
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// Cedric Nugteren <www.cedricnugteren.nl>
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//
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// This file implements a class with static methods to describe the Xinvert routine. Examples of
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// such 'descriptions' are how to calculate the size a of buffer or how to run the routine. These
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// static methods are used by the correctness tester and the performance tester.
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//
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// =================================================================================================
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#ifndef CLBLAST_TEST_ROUTINES_XINVERT_H_
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#define CLBLAST_TEST_ROUTINES_XINVERT_H_
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#include "test/routines/common.hpp"
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namespace clblast {
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// =================================================================================================
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template <typename T>
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StatusCode RunReference(const Arguments<T> &args, BuffersHost<T> &buffers_host) {
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const bool is_upper = ((args.triangle == Triangle::kUpper && args.layout != Layout::kRowMajor) ||
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(args.triangle == Triangle::kLower && args.layout == Layout::kRowMajor));
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// Helper variables
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const auto block_size = args.m;
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const auto num_blocks = CeilDiv(args.n, block_size);
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const auto a_ld = args.a_ld;
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const auto b_ld = block_size;
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// Checks for valid arguments
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if ((block_size == 0) || (args.n == 0)) {
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return StatusCode::kInvalidDimension;
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}
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if ((block_size % 16 != 0) || (block_size > 128)) {
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return StatusCode::kUnknownError;
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}
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// Loops over the amount of diagonal blocks of size args.m by args.m each
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for (auto block_id = size_t{0}; block_id < num_blocks; ++block_id) {
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const auto a_offset = block_id * (block_size + a_ld * block_size) + args.a_offset;
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const auto b_offset = block_id * block_size * block_size;
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// Inverts the diagonal elements of the matrix
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for (auto i = size_t{0}; i < block_size; ++i) {
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auto a_value = T{1.0};
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if (args.diagonal == Diagonal::kNonUnit) {
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if (i + block_id * block_size < args.n) {
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if (buffers_host.a_mat[i * a_ld + i + a_offset] == T{0.0}) { return StatusCode::kUnknownError; }
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a_value = T{1.0} / buffers_host.a_mat[i * a_ld + i + a_offset];
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}
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}
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buffers_host.b_mat[i * b_ld + i + b_offset] = a_value;
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}
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// Inverts the upper triangle row by row
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if (is_upper) {
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for (int i = static_cast<int>(block_size) - 2; i >= 0; --i) {
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for (auto j = static_cast<int>(block_size) - 1; j > i; --j) {
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auto sum = T{0.0};
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for (auto k = i + 1; k <= j; ++k) {
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auto a_value = T{0.0};
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if ((i + block_id * block_size < args.n) && (k + block_id * block_size < args.n)) {
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a_value = buffers_host.a_mat[k * a_ld + i + a_offset];
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}
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sum += a_value * buffers_host.b_mat[j * b_ld + k + b_offset];
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}
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buffers_host.b_mat[j * b_ld + i + b_offset] = - sum * buffers_host.b_mat[i * b_ld + i + b_offset];
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}
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}
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}
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// Inverts the lower triangle row by row
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else {
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for (auto i = size_t{1}; i < block_size; ++i) {
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for (auto j = size_t{0}; j < i; ++j) {
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auto sum = T{0.0};
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for (auto k = j; k < i; ++k) {
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auto a_value = T{0.0};
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if ((i + block_id * block_size < args.n) && (k + block_id * block_size < args.n)) {
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a_value = buffers_host.a_mat[k * a_ld + i + a_offset];
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}
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sum += a_value * buffers_host.b_mat[j * b_ld + k + b_offset];
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}
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buffers_host.b_mat[j * b_ld + i + b_offset] = - sum * buffers_host.b_mat[i * b_ld + i + b_offset];
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}
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}
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}
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}
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return StatusCode::kSuccess;
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}
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// Half-precision version calling the above reference implementation after conversions
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template <>
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StatusCode RunReference<half>(const Arguments<half> &args, BuffersHost<half> &buffers_host) {
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auto a_buffer2 = HalfToFloatBuffer(buffers_host.a_mat);
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auto b_buffer2 = HalfToFloatBuffer(buffers_host.b_mat);
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auto dummy = std::vector<float>(0);
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auto buffers2 = BuffersHost<float>{dummy, dummy, a_buffer2, b_buffer2, dummy, dummy, dummy};
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auto args2 = Arguments<float>();
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args2.a_size = args.a_size; args2.b_size = args.b_size;
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args2.a_ld = args.a_ld; args2.m = args.m; args2.n = args.n;
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args2.a_offset = args.a_offset;
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args2.layout = args.layout; args2.triangle = args.triangle; args2.diagonal = args.diagonal;
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auto status = RunReference(args2, buffers2);
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FloatToHalfBuffer(buffers_host.b_mat, b_buffer2);
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return status;
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}
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// =================================================================================================
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// See comment at top of file for a description of the class
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template <typename T>
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class TestXinvert {
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public:
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// The BLAS level: 4 for the extra routines
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static size_t BLASLevel() { return 4; }
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// The list of arguments relevant for this routine
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static std::vector<std::string> GetOptions() {
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return {kArgN, kArgM,
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kArgLayout, kArgTriangle, kArgDiagonal,
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kArgALeadDim, kArgAOffset};
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}
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static std::vector<std::string> BuffersIn() { return {kBufMatA, kBufMatB}; }
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static std::vector<std::string> BuffersOut() { return {kBufMatB}; }
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// Describes how to obtain the sizes of the buffers
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static size_t GetSizeA(const Arguments<T> &args) {
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return args.n * args.a_ld + args.a_offset;
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}
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static size_t GetSizeB(const Arguments<T> &args) {
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const auto block_size = args.m;
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const auto num_blocks = CeilDiv(args.n, block_size);
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return num_blocks * block_size * block_size;
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}
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// Describes how to set the sizes of all the buffers
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static void SetSizes(Arguments<T> &args) {
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args.a_size = GetSizeA(args);
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args.b_size = GetSizeB(args);
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}
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// Describes what the default values of the leading dimensions of the matrices are
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static size_t DefaultLDA(const Arguments<T> &args) { return args.n; }
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static size_t DefaultLDB(const Arguments<T> &) { return 1; } // N/A for this routine
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static size_t DefaultLDC(const Arguments<T> &) { return 1; } // N/A for this routine
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// Describes which omatcopyose options are relevant for this routine
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using Transposes = std::vector<Transpose>;
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static Transposes GetATransposes(const Transposes &) { return {}; } // N/A for this routine
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static Transposes GetBTransposes(const Transposes &) { return {}; } // N/A for this routine
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// Describes how to prepare the input data
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static void PrepareData(const Arguments<T>&, Queue&, const int, std::vector<T>&,
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std::vector<T>&, std::vector<T>&, std::vector<T>&, std::vector<T>&,
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std::vector<T>&, std::vector<T>&) {} // N/A for this routine
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// Describes how to run the CLBlast routine
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static StatusCode RunRoutine(const Arguments<T> &args, Buffers<T> &buffers, Queue &queue) {
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try {
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auto event = cl_event{};
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auto inverter = Xinvert<T>(queue, &event);
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inverter.InvertMatrixDiagonalBlocks(args.layout, args.triangle, args.diagonal,
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args.n, args.m,
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buffers.a_mat, args.a_offset, args.a_ld,
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buffers.b_mat);
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clWaitForEvents(1, &event);
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clReleaseEvent(event);
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} catch (...) { return DispatchException(); }
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return StatusCode::kSuccess;
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}
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// Describes how to run a naive version of the routine (for correctness/performance comparison).
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// Note that a proper clBLAS or CPU BLAS comparison is not available for non-BLAS routines.
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static StatusCode RunReference1(const Arguments<T> &args, Buffers<T> &buffers, Queue &queue) {
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auto buffers_host = BuffersHost<T>();
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DeviceToHost(args, buffers, buffers_host, queue, BuffersIn());
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const auto status = RunReference(args, buffers_host);
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HostToDevice(args, buffers, buffers_host, queue, BuffersOut());
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return status;
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}
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static StatusCode RunReference2(const Arguments<T> &args, BuffersHost<T> &buffers_host, Queue&) {
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return RunReference(args, buffers_host);
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}
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static StatusCode RunReference3(const Arguments<T> &args, BuffersCUDA<T> &buffers, Queue &) {
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return StatusCode::kUnknownError;
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}
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// Describes how to download the results of the computation (more importantly: which buffer)
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static std::vector<T> DownloadResult(const Arguments<T> &args, Buffers<T> &buffers, Queue &queue) {
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std::vector<T> result(args.b_size, static_cast<T>(0));
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buffers.b_mat.Read(queue, args.b_size, result);
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return result;
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}
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// Describes how to compute the indices of the result buffer
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static size_t ResultID1(const Arguments<T> &args) { return args.m; }
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static size_t ResultID2(const Arguments<T> &args) { return Ceil(args.n, args.m); }
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static size_t GetResultIndex(const Arguments<T> &args, const size_t id1, const size_t id2) {
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return id1 * Ceil(args.n, args.m) + id2;
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}
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// Describes how to compute performance metrics
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static size_t GetFlops(const Arguments<T> &args) {
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const auto block_size = args.m;
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const auto num_blocks = CeilDiv(args.n, block_size);
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return num_blocks * (block_size * (block_size / 2) * (block_size / 2));
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}
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static size_t GetBytes(const Arguments<T> &args) {
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return (args.a_size * args.b_size) * sizeof(T);
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}
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};
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// =================================================================================================
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} // namespace clblast
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// CLBLAST_TEST_ROUTINES_XINVERT_H_
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#endif
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