The plugin was not getting built as the build_generic_elf64 macro assumes the LLVM triple processor name matches the CMake processor name, which is unfortunately not the case for SystemZ. Fix this by providing two separate arguments instead. Actually building the plugin exposed a number of other issues causing various test failures. Specifically, I've had to add the SystemZ target to - CompilerInvocation::ParseLangArgs - linkDevice in ClangLinuxWrapper.cpp - OMPContext::OMPContext (to set the device_kind_cpu trait) - LIBOMPTARGET_ALL_TARGETS in libomptarget/CMakeLists.txt - a check_plugin_target call in libomptarget/src/CMakeLists.txt Finally, I've had to set a number of test cases to UNSUPPORTED on s390x-ibm-linux-gnu; all these tests were already marked as UNSUPPORTED for x86_64-pc-linux-gnu and aarch64-unknown-linux-gnu and are failing on s390x for what seem to be the same reason. In addition, this also requires support for BE ELF files in plugins-nextgen: https://github.com/llvm/llvm-project/pull/85246
158 lines
4.8 KiB
C++
158 lines
4.8 KiB
C++
// RUN: %libomptarget-compilexx-generic -O3 && %libomptarget-run-generic
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// RUN: %libomptarget-compilexx-generic -O3 -ffast-math && \
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// RUN: %libomptarget-run-generic
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// RUN: %libomptarget-compileoptxx-generic -O3 && %libomptarget-run-generic
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// RUN: %libomptarget-compileoptxx-generic -O3 -ffast-math && \
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// RUN: %libomptarget-run-generic
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// UNSUPPORTED: x86_64-pc-linux-gnu
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// UNSUPPORTED: x86_64-pc-linux-gnu-LTO
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// UNSUPPORTED: aarch64-unknown-linux-gnu
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// UNSUPPORTED: aarch64-unknown-linux-gnu-LTO
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// UNSUPPORTED: s390x-ibm-linux-gnu
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// UNSUPPORTED: s390x-ibm-linux-gnu-LTO
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// UNSUPPORTED: amdgcn-amd-amdhsa
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// UNSUPPORTED: nvptx64-nvidia-cuda
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// UNSUPPORTED: nvptx64-nvidia-cuda-LTO
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#include <cassert>
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#include <cmath>
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#include <iostream>
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#include <limits>
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#include <memory>
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#include <vector>
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class BlockMatrix {
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private:
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const int rowsPerBlock;
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const int colsPerBlock;
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const long nRows;
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const long nCols;
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const int nBlocksPerRow;
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const int nBlocksPerCol;
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std::vector<std::vector<std::unique_ptr<float[]>>> Blocks;
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public:
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BlockMatrix(const int _rowsPerBlock, const int _colsPerBlock,
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const long _nRows, const long _nCols)
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: rowsPerBlock(_rowsPerBlock), colsPerBlock(_colsPerBlock), nRows(_nRows),
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nCols(_nCols), nBlocksPerRow(_nRows / _rowsPerBlock),
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nBlocksPerCol(_nCols / _colsPerBlock), Blocks(nBlocksPerCol) {
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for (int i = 0; i < nBlocksPerCol; i++) {
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for (int j = 0; j < nBlocksPerRow; j++) {
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Blocks[i].emplace_back(new float[_rowsPerBlock * _colsPerBlock]);
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}
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}
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};
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// Initialize the BlockMatrix from 2D arrays
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void Initialize(const std::vector<float> &matrix) {
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for (int i = 0; i < nBlocksPerCol; i++)
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for (int j = 0; j < nBlocksPerRow; j++) {
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float *CurrBlock = GetBlock(i, j);
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for (int ii = 0; ii < colsPerBlock; ++ii)
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for (int jj = 0; jj < rowsPerBlock; ++jj) {
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int curri = i * colsPerBlock + ii;
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int currj = j * rowsPerBlock + jj;
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CurrBlock[ii + jj * colsPerBlock] = matrix[curri + currj * nCols];
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}
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}
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}
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void Compare(const std::vector<float> &matrix) const {
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for (int i = 0; i < nBlocksPerCol; i++)
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for (int j = 0; j < nBlocksPerRow; j++) {
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float *CurrBlock = GetBlock(i, j);
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for (int ii = 0; ii < colsPerBlock; ++ii)
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for (int jj = 0; jj < rowsPerBlock; ++jj) {
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int curri = i * colsPerBlock + ii;
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int currj = j * rowsPerBlock + jj;
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float m_value = matrix[curri + currj * nCols];
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float bm_value = CurrBlock[ii + jj * colsPerBlock];
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assert(std::fabs(bm_value - m_value) <
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std::numeric_limits<float>::epsilon());
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}
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}
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}
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float *GetBlock(int i, int j) const {
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assert(i < nBlocksPerCol && j < nBlocksPerRow && "Accessing outside block");
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return Blocks[i][j].get();
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}
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};
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constexpr const int BS = 16;
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constexpr const int N = 256;
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int BlockMatMul_TargetNowait(BlockMatrix &A, BlockMatrix &B, BlockMatrix &C) {
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#pragma omp parallel
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#pragma omp master
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for (int i = 0; i < N / BS; ++i)
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for (int j = 0; j < N / BS; ++j) {
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float *BlockC = C.GetBlock(i, j);
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for (int k = 0; k < N / BS; ++k) {
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float *BlockA = A.GetBlock(i, k);
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float *BlockB = B.GetBlock(k, j);
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// clang-format off
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#pragma omp target depend(in: BlockA[0], BlockB[0]) depend(inout: BlockC[0]) \
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map(to: BlockA[:BS * BS], BlockB[:BS * BS]) \
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map(tofrom: BlockC[:BS * BS]) nowait
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// clang-format on
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#pragma omp parallel for
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for (int ii = 0; ii < BS; ii++)
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for (int jj = 0; jj < BS; jj++) {
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for (int kk = 0; kk < BS; ++kk)
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BlockC[ii + jj * BS] +=
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BlockA[ii + kk * BS] * BlockB[kk + jj * BS];
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}
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}
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}
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return 0;
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}
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void Matmul(const std::vector<float> &a, const std::vector<float> &b,
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std::vector<float> &c) {
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for (int i = 0; i < N; ++i) {
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for (int j = 0; j < N; ++j) {
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float sum = 0.0;
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for (int k = 0; k < N; ++k) {
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sum = sum + a[i * N + k] * b[k * N + j];
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}
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c[i * N + j] = sum;
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}
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}
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}
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int main(int argc, char *argv[]) {
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std::vector<float> a(N * N);
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std::vector<float> b(N * N);
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std::vector<float> c(N * N, 0.0);
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for (int i = 0; i < N; ++i) {
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for (int j = 0; j < N; ++j) {
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a[i * N + j] = b[i * N + j] = i + j % 100;
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}
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}
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auto BlockedA = BlockMatrix(BS, BS, N, N);
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auto BlockedB = BlockMatrix(BS, BS, N, N);
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auto BlockedC = BlockMatrix(BS, BS, N, N);
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BlockedA.Initialize(a);
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BlockedB.Initialize(b);
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BlockedC.Initialize(c);
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BlockedA.Compare(a);
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BlockedB.Compare(b);
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BlockedC.Compare(c);
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Matmul(a, b, c);
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BlockMatMul_TargetNowait(BlockedA, BlockedB, BlockedC);
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BlockedC.Compare(c);
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std::cout << "PASS\n";
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return 0;
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}
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// CHECK: PASS
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