2017-01-15 17:30:00 +01:00
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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 contains all the common code to perform (partial) matrix inverting. This code is based
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// on the TRSM implementation in the CUDA version of Magma version 2.2.0 and the poster "Triangular
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// Linear System Solver for GPU with CUDA and OpenCL" by Peng Du, Stanimire Tomov, Piotr Luszczek,
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// and Jack Dongarra.
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//
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// =================================================================================================
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#include "routines/levelx/xinvert.hpp"
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#include <string>
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#include <vector>
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#include <assert.h>
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namespace clblast {
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// =================================================================================================
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// Constructor: forwards to base class constructor
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template <typename T>
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Xinvert<T>::Xinvert(Queue &queue, EventPointer event, const std::string &name):
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Routine(queue, event, name, {"Invert"}, PrecisionValue<T>(), {}, {
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2017-01-18 21:29:59 +01:00
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#include "../../kernels/level3/level3.opencl"
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2017-01-15 17:30:00 +01:00
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#include "../../kernels/level3/invert_diagonal_blocks.opencl"
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}) {
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}
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// =================================================================================================
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// Inverts diagonal square blocks of a matrix
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template <typename T>
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void Xinvert<T>::InvertMatrixDiagonalBlocks(const Layout layout, const Triangle triangle, const Diagonal diag,
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const size_t n, const size_t block_size,
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const Buffer<T> &src, const size_t offset, const size_t ld_src,
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Buffer<T> &dest) {
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2017-02-22 20:31:33 +01:00
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// Makes sure all dimensions are larger than zero
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if ((block_size == 0) || (n == 0)) {
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2017-01-15 17:30:00 +01:00
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throw BLASError(StatusCode::kInvalidDimension);
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}
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// Helper variables
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const auto internal_block_size = static_cast<size_t>(db_["INTERNAL_BLOCK_SIZE"]);
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assert(internal_block_size == 16);
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const auto num_blocks = CeilDiv(n, block_size);
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const auto num_internal_blocks = CeilDiv(n, internal_block_size);
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const auto unit_diagonal = (diag == Diagonal::kUnit) ? true : false;
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// This routine only supports block sizes which are a multiple of the internal block size and
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// block sizes up to and including 128
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if ((block_size % internal_block_size != 0) || (block_size > 128)) {
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2017-02-22 20:31:33 +01:00
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throw BLASError(StatusCode::kUnknownError);
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2017-01-15 17:30:00 +01:00
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}
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// Checks for validity of the source and destination matrices
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TestMatrixA(n, n, src, offset, ld_src);
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TestMatrixB(block_size, num_blocks * block_size, dest, 0, block_size);
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// Determines which kernels to run based on the layout (the kernels assume column-major as
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// default) and on whether we are dealing with an upper or lower triangle of the triangular matrix
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const bool is_upper = ((triangle == Triangle::kUpper && layout != Layout::kRowMajor) ||
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(triangle == Triangle::kLower && layout == Layout::kRowMajor));
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const auto name_postfix = (is_upper) ? "Upper" : "Lower";
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// Fills the output buffer with zeros
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2017-02-05 22:18:59 +01:00
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auto event_wait_list = std::vector<Event>();
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2017-01-15 17:30:00 +01:00
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auto fill_matrix_event = Event();
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FillMatrix(queue_, device_, program_, db_, fill_matrix_event.pointer(), event_wait_list,
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2017-02-26 14:31:05 +01:00
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block_size, num_blocks * block_size, block_size, 0, dest, ConstantZero<T>());
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2017-01-15 17:30:00 +01:00
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event_wait_list.push_back(fill_matrix_event);
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// Inverts the diagonal IB by IB inner blocks of the matrix: one block per work-group
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2017-02-05 22:18:59 +01:00
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auto kernel = Kernel(program_, "InvertDiagonalBlock");
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kernel.SetArgument(0, static_cast<int>(n));
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kernel.SetArgument(1, src());
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kernel.SetArgument(2, static_cast<int>(offset));
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kernel.SetArgument(3, static_cast<int>(ld_src));
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kernel.SetArgument(4, dest());
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kernel.SetArgument(5, static_cast<int>(block_size));
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kernel.SetArgument(6, static_cast<int>(unit_diagonal));
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kernel.SetArgument(7, static_cast<int>(is_upper));
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const auto local = std::vector<size_t>{internal_block_size};
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const auto global = std::vector<size_t>{num_internal_blocks * internal_block_size};
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auto base_kernel_event = Event();
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2017-01-18 21:29:59 +01:00
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auto base_kernel_event_pointer = (internal_block_size == block_size) ? event_ : base_kernel_event.pointer();
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RunKernel(kernel, queue_, device_, global, local, base_kernel_event_pointer, event_wait_list);
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if (internal_block_size == block_size) { event_wait_list.push_back(base_kernel_event); }
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2017-01-15 17:30:00 +01:00
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// Builds up block_size x block_size blocks. For example, internal_block_size=16:
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// use 16 x 16 blocks to build 32 x 32 blocks, 1 x (1 x npages) grid, 4 x 4 threads;
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// then 32 x 32 blocks to build 64 x 64 blocks, 1 x (2 x npages) grid, 8 x 4 threads;
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// then 64 x 64 blocks to build 128 x 128 blocks, 1 x (4 x npages) grid, 16 x 4 threads;
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for (auto current_size = internal_block_size; current_size < block_size; current_size *= 2) {
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assert(current_size == 16 || current_size == 32 || current_size == 64);
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// Emulates a 3D grid: NX * (NY * npages)
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const auto npages = CeilDiv(n, current_size*2);
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const auto local0 = (current_size <= 32) ? current_size/4 : 16;
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const auto local = std::vector<size_t>{local0, 4};
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const auto global = std::vector<size_t>{(current_size/local[1]), npages*(current_size/16)*local[1]};
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// Part 1
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2017-02-05 22:18:59 +01:00
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auto kernel1 = Kernel(program_, "TripleMatMul" + ToString(current_size) + "Part1" + name_postfix);
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2017-01-15 17:30:00 +01:00
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kernel1.SetArgument(0, static_cast<int>(n));
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kernel1.SetArgument(1, src());
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kernel1.SetArgument(2, static_cast<int>(offset));
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kernel1.SetArgument(3, static_cast<int>(ld_src));
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kernel1.SetArgument(4, dest());
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kernel1.SetArgument(5, static_cast<int>(current_size));
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kernel1.SetArgument(6, static_cast<int>(npages));
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kernel1.SetArgument(7, static_cast<int>(block_size));
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auto kernel1_event = Event();
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RunKernel(kernel1, queue_, device_, global, local, kernel1_event.pointer(), event_wait_list);
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event_wait_list.push_back(kernel1_event);
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// Part 2
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const bool is_last_kernel = (current_size * 2 >= block_size);
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2017-02-05 22:18:59 +01:00
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auto kernel2 = Kernel(program_, "TripleMatMul" + ToString(current_size) + "Part2" + name_postfix);
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kernel2.SetArgument(0, static_cast<int>(n));
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kernel2.SetArgument(1, dest());
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kernel2.SetArgument(2, static_cast<int>(current_size));
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kernel2.SetArgument(3, static_cast<int>(npages));
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kernel2.SetArgument(4, static_cast<int>(block_size));
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auto kernel2_event = Event();
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2017-01-18 21:29:59 +01:00
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auto kernel2_event_pointer = (is_last_kernel) ? event_ : kernel2_event.pointer();
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RunKernel(kernel2, queue_, device_, global, local, kernel2_event_pointer, event_wait_list);
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2017-01-15 17:30:00 +01:00
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if (!is_last_kernel) { event_wait_list.push_back(kernel2_event); }
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// Exit in case we reach beyond the bounds of the input matrix
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if (current_size*2 >= n) { break; }
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}
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}
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// =================================================================================================
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// Compiles the templated class
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template class Xinvert<half>;
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template class Xinvert<float>;
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template class Xinvert<double>;
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template class Xinvert<float2>;
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template class Xinvert<double2>;
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// =================================================================================================
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} // namespace clblast
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