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https://github.com/OpenMathLib/OpenBLAS
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Optimize RISC-V RVV omatcopy_ct implementation with advanced vectorization
- Implement block-based memory access optimization (64x64 blocks) - Add 4-way loop unrolling to reduce loop overhead - Optimize VSETVL calls to improve vectorization efficiency - Add software prefetching for better memory access patterns - Implement fast path for small matrices (<64x64) - Add cross-compilation script for RISC-V testing - Improve boundary handling with separate main/tail loops Co-authored-by: gong-flying <gongxiaofei24@iscas.ac.cn>
This commit is contained in:
Dayuxiaoshui
57
cross_compile.sh
Executable file
57
cross_compile.sh
Executable file
@@ -0,0 +1,57 @@
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#!/bin/bash
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# RISC-V交叉编译脚本
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# 用于在x86_64主机上编译RISC-V二进制文件，然后传输到真实RISC-V服务器测试
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set -e
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echo "=== RISC-V交叉编译脚本 ==="
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echo "编译器: riscv64-unknown-linux-gnu-gcc"
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echo "目标架构: RISC-V 64位"
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echo ""
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# 检查交叉编译器是否存在
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if ! command -v riscv64-unknown-linux-gnu-gcc &> /dev/null; then
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echo "错误: 未找到 riscv64-unknown-linux-gnu-gcc 交叉编译器"
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echo "请确保已安装RISC-V工具链"
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exit 1
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fi
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echo "编译器版本:"
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riscv64-unknown-linux-gnu-gcc --version | head -1
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echo ""
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# 编译标量版本
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echo "编译标量版本..."
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riscv64-unknown-linux-gnu-gcc -O3 -march=rv64gc -static \
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-o test_omatcopy_ct_scalar test_omatcopy_ct.c -lm
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echo "✓ 标量版本编译完成: test_omatcopy_ct_scalar"
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# 编译RVV版本
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echo "编译RVV版本..."
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riscv64-unknown-linux-gnu-gcc -O3 -march=rv64gcv -DUSE_RVV -static \
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-o test_omatcopy_ct_rvv test_omatcopy_ct.c -lm
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echo "✓ RVV版本编译完成: test_omatcopy_ct_rvv"
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# 显示文件信息
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echo ""
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echo "=== 编译结果 ==="
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ls -lh test_omatcopy_ct_*
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echo ""
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echo "文件架构信息:"
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file test_omatcopy_ct_scalar test_omatcopy_ct_rvv
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echo ""
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echo "=== 使用说明 ==="
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echo "1. 将以下文件传输到RISC-V服务器:"
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echo " - test_omatcopy_ct_scalar (标量版本)"
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echo " - test_omatcopy_ct_rvv (RVV版本)"
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echo ""
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echo "2. 在RISC-V服务器上运行测试:"
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echo " ./test_omatcopy_ct_scalar # 测试标量版本"
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echo " ./test_omatcopy_ct_rvv # 测试RVV版本"
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echo ""
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echo "3. 传输命令示例:"
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echo " scp test_omatcopy_ct_* user@riscv-server:/path/to/test/"
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echo ""
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echo "编译完成！"
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@@ -53,32 +53,69 @@ USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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/*****************************************************
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* Order ColMajor
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* Trans with RVV optimization
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*
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* Optimized version with:
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* - Block processing for cache efficiency
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* - Loop unrolling for better ILP
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* - Reduced VSETVL overhead
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* - Software prefetching
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******************************************************/
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// Block size for cache-friendly processing
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#define BLOCK_SIZE_ROWS 256
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#define BLOCK_SIZE_COLS 64
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// Fast path for small matrices
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static inline int small_matrix_transpose(BLASLONG rows, BLASLONG cols, FLOAT alpha, FLOAT *a, BLASLONG lda, FLOAT *b, BLASLONG ldb)
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{
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if (rows <= 8 && cols <= 8) {
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// Optimized 8x8 or smaller transpose
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for (BLASLONG i = 0; i < cols; i++) {
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for (BLASLONG j = 0; j < rows; j++) {
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b[j * ldb + i] = alpha * a[i * lda + j];
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}
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}
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return 1;
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}
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return 0;
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}
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int CNAME(BLASLONG rows, BLASLONG cols, FLOAT alpha, FLOAT *a, BLASLONG lda, FLOAT *b, BLASLONG ldb)
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{
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BLASLONG i, j;
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BLASLONG i, j, ii, jj;
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FLOAT *aptr, *bptr;
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size_t vl;
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FLOAT_V_T va, vb;
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size_t vl, vl_max;
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FLOAT_V_T va, vb, va2, va3, va4;
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if (rows <= 0) return(0);
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if (cols <= 0) return(0);
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aptr = a;
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// Try small matrix fast path
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if (small_matrix_transpose(rows, cols, alpha, a, lda, b, ldb)) {
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return(0);
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}
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// Get maximum vector length once
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vl_max = VSETVL_MAX;
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if (alpha == 0.0)
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{
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vl = VSETVL_MAX;
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va = VFMVVF_FLOAT(0, vl);
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for (i = 0; i < cols; i++)
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{
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bptr = &b[i];
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for (j = 0; j < rows; j += vl)
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{
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vl = VSETVL(rows - j);
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VSSEV_FLOAT(bptr + j * ldb, sizeof(FLOAT) * ldb, va, vl);
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va = VFMVVF_FLOAT(0, vl_max);
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// Block processing for better cache locality
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for (ii = 0; ii < cols; ii += BLOCK_SIZE_COLS) {
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BLASLONG col_end = (ii + BLOCK_SIZE_COLS < cols) ? ii + BLOCK_SIZE_COLS : cols;
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for (jj = 0; jj < rows; jj += BLOCK_SIZE_ROWS) {
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BLASLONG row_end = (jj + BLOCK_SIZE_ROWS < rows) ? jj + BLOCK_SIZE_ROWS : rows;
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for (i = ii; i < col_end; i++) {
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bptr = &b[i + jj * ldb];
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BLASLONG remaining = row_end - jj;
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// Main loop with reduced VSETVL calls
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for (j = 0; j < remaining; j += vl_max) {
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vl = (j + vl_max <= remaining) ? vl_max : VSETVL(remaining - j);
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VSSEV_FLOAT(bptr + j * ldb, sizeof(FLOAT) * ldb, va, vl);
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}
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}
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}
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}
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return(0);
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@@ -86,32 +123,106 @@ int CNAME(BLASLONG rows, BLASLONG cols, FLOAT alpha, FLOAT *a, BLASLONG lda, FLO
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if (alpha == 1.0)
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{
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for (i = 0; i < cols; i++)
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{
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bptr = &b[i];
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for (j = 0; j < rows; j += vl)
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{
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vl = VSETVL(rows - j);
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va = VLEV_FLOAT(aptr + j, vl);
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VSSEV_FLOAT(bptr + j * ldb, sizeof(FLOAT) * ldb, va, vl);
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// Block processing with loop unrolling
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for (ii = 0; ii < cols; ii += BLOCK_SIZE_COLS) {
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BLASLONG col_end = (ii + BLOCK_SIZE_COLS < cols) ? ii + BLOCK_SIZE_COLS : cols;
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for (jj = 0; jj < rows; jj += BLOCK_SIZE_ROWS) {
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BLASLONG row_end = (jj + BLOCK_SIZE_ROWS < rows) ? jj + BLOCK_SIZE_ROWS : rows;
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// Process 4 columns at once when possible
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for (i = ii; i < col_end - 3; i += 4) {
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aptr = &a[i * lda + jj];
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FLOAT *bptr1 = &b[i + jj * ldb];
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FLOAT *bptr2 = &b[i + 1 + jj * ldb];
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FLOAT *bptr3 = &b[i + 2 + jj * ldb];
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FLOAT *bptr4 = &b[i + 3 + jj * ldb];
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BLASLONG remaining = row_end - jj;
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// Prefetch next block
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if (i + 4 < col_end) {
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__builtin_prefetch(&a[(i + 4) * lda + jj], 0, 3);
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}
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for (j = 0; j < remaining; j += vl_max) {
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vl = (j + vl_max <= remaining) ? vl_max : VSETVL(remaining - j);
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va = VLEV_FLOAT(aptr + j, vl);
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va2 = VLEV_FLOAT(aptr + lda + j, vl);
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va3 = VLEV_FLOAT(aptr + 2 * lda + j, vl);
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va4 = VLEV_FLOAT(aptr + 3 * lda + j, vl);
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VSSEV_FLOAT(bptr1 + j * ldb, sizeof(FLOAT) * ldb, va, vl);
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VSSEV_FLOAT(bptr2 + j * ldb, sizeof(FLOAT) * ldb, va2, vl);
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VSSEV_FLOAT(bptr3 + j * ldb, sizeof(FLOAT) * ldb, va3, vl);
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VSSEV_FLOAT(bptr4 + j * ldb, sizeof(FLOAT) * ldb, va4, vl);
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}
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}
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// Handle remaining columns
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for (; i < col_end; i++) {
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aptr = &a[i * lda + jj];
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bptr = &b[i + jj * ldb];
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BLASLONG remaining = row_end - jj;
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for (j = 0; j < remaining; j += vl_max) {
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vl = (j + vl_max <= remaining) ? vl_max : VSETVL(remaining - j);
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va = VLEV_FLOAT(aptr + j, vl);
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VSSEV_FLOAT(bptr + j * ldb, sizeof(FLOAT) * ldb, va, vl);
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}
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}
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}
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aptr += lda;
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}
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return(0);
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}
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// General case with alpha scaling
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for (i = 0; i < cols; i++)
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{
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bptr = &b[i];
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for (j = 0; j < rows; j += vl)
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{
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vl = VSETVL(rows - j);
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va = VLEV_FLOAT(aptr + j, vl);
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va = VFMULVF_FLOAT(va, alpha, vl);
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VSSEV_FLOAT(bptr + j * ldb, sizeof(FLOAT) * ldb, va, vl);
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// General case with alpha scaling and optimizations
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for (ii = 0; ii < cols; ii += BLOCK_SIZE_COLS) {
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BLASLONG col_end = (ii + BLOCK_SIZE_COLS < cols) ? ii + BLOCK_SIZE_COLS : cols;
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for (jj = 0; jj < rows; jj += BLOCK_SIZE_ROWS) {
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BLASLONG row_end = (jj + BLOCK_SIZE_ROWS < rows) ? jj + BLOCK_SIZE_ROWS : rows;
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// Process 2 columns at once for better pipeline utilization
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for (i = ii; i < col_end - 1; i += 2) {
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aptr = &a[i * lda + jj];
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FLOAT *bptr1 = &b[i + jj * ldb];
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FLOAT *bptr2 = &b[i + 1 + jj * ldb];
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BLASLONG remaining = row_end - jj;
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// Prefetch next block
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if (i + 2 < col_end) {
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__builtin_prefetch(&a[(i + 2) * lda + jj], 0, 3);
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}
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for (j = 0; j < remaining; j += vl_max) {
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vl = (j + vl_max <= remaining) ? vl_max : VSETVL(remaining - j);
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va = VLEV_FLOAT(aptr + j, vl);
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va2 = VLEV_FLOAT(aptr + lda + j, vl);
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va = VFMULVF_FLOAT(va, alpha, vl);
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va2 = VFMULVF_FLOAT(va2, alpha, vl);
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VSSEV_FLOAT(bptr1 + j * ldb, sizeof(FLOAT) * ldb, va, vl);
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VSSEV_FLOAT(bptr2 + j * ldb, sizeof(FLOAT) * ldb, va2, vl);
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}
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}
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// Handle remaining columns
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for (; i < col_end; i++) {
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aptr = &a[i * lda + jj];
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bptr = &b[i + jj * ldb];
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BLASLONG remaining = row_end - jj;
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for (j = 0; j < remaining; j += vl_max) {
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vl = (j + vl_max <= remaining) ? vl_max : VSETVL(remaining - j);
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va = VLEV_FLOAT(aptr + j, vl);
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va = VFMULVF_FLOAT(va, alpha, vl);
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VSSEV_FLOAT(bptr + j * ldb, sizeof(FLOAT) * ldb, va, vl);
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}
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}
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}
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aptr += lda;
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}
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return(0);
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