说明
本文作为上一篇 矩阵转置 transpose 复现的补充测试。来看看 streaming store 到底什么实力。
性能测试
FORCE_INLINE void transpose_8x8_store_contiguous(const uint8_t* src0, const uint8_t* src1, const uint8_t* src2, const uint8_t* src3, const uint8_t* src4, const uint8_t* src5, const uint8_t* src6, const uint8_t* src7, uint8_t* pDst) { __m128i r0 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src0)); __m128i r1 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src1)); __m128i r2 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src2)); __m128i r3 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src3)); __m128i r4 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src4)); __m128i r5 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src5)); __m128i r6 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src6)); __m128i r7 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(src7)); __m128i t0 = _mm_unpacklo_epi8(r0, r1); __m128i t1 = _mm_unpacklo_epi8(r2, r3); __m128i t2 = _mm_unpacklo_epi8(r4, r5); __m128i t3 = _mm_unpacklo_epi8(r6, r7); __m128i t4 = _mm_unpacklo_epi16(t0, t1); __m128i t5 = _mm_unpacklo_epi16(t2, t3); __m128i t6 = _mm_unpackhi_epi16(t0, t1); __m128i t7 = _mm_unpackhi_epi16(t2, t3); __m128i c0 = _mm_unpacklo_epi32(t4, t5); __m128i c1 = _mm_unpackhi_epi32(t4, t5); __m128i c2 = _mm_unpacklo_epi32(t6, t7); __m128i c3 = _mm_unpackhi_epi32(t6, t7); _mm_store_si128(reinterpret_cast<__m128i*>(pDst + 0), c0); _mm_store_si128(reinterpret_cast<__m128i*>(pDst + 16), c1); _mm_store_si128(reinterpret_cast<__m128i*>(pDst + 32), c2); _mm_store_si128(reinterpret_cast<__m128i*>(pDst + 48), c3); } template <bool UseStream> FORCE_INLINE void transpose_64x64_tile_impl(const uint8_t* pSrc, unsigned int srcStep, uint8_t* pDst, unsigned int dstStep) { alignas(64) uint8_t tmp[64 * 64]; uint8_t* tmpPtr = tmp; size_t srcStep8 = (size_t)srcStep * 8; const uint8_t* s0 = pSrc; for (int y = 0; y < 64; y += 8) { transpose_8x8_store_contiguous(s0, s0+srcStep, s0+srcStep*2, s0+srcStep*3, s0+srcStep*4, s0+srcStep*5, s0+srcStep*6, s0+srcStep*7, tmpPtr); tmpPtr += 64; s0 += srcStep8; } for (int colBlock = 0; colBlock < 8; ++colBlock) { const uint8_t* bBase = tmp + colBlock * 64; for (int r = 0; r < 8; ++r) { int laneOffset = r * 8; __m128i b0 = _mm_loadl_epi64((const __m128i*)(bBase + 0 * 512 + laneOffset)); __m128i b1 = _mm_loadl_epi64((const __m128i*)(bBase + 1 * 512 + laneOffset)); __m128i b2 = _mm_loadl_epi64((const __m128i*)(bBase + 2 * 512 + laneOffset)); __m128i b3 = _mm_loadl_epi64((const __m128i*)(bBase + 3 * 512 + laneOffset)); __m128i b4 = _mm_loadl_epi64((const __m128i*)(bBase + 4 * 512 + laneOffset)); __m128i b5 = _mm_loadl_epi64((const __m128i*)(bBase + 5 * 512 + laneOffset)); __m128i b6 = _mm_loadl_epi64((const __m128i*)(bBase + 6 * 512 + laneOffset)); __m128i b7 = _mm_loadl_epi64((const __m128i*)(bBase + 7 * 512 + laneOffset)); __m128i v0 = _mm_unpacklo_epi64(b0, b1); __m128i v1 = _mm_unpacklo_epi64(b2, b3); __m128i v2 = _mm_unpacklo_epi64(b4, b5); __m128i v3 = _mm_unpacklo_epi64(b6, b7); uint8_t* dstRowPtr = pDst + (colBlock * 8 + r) * dstStep; if (UseStream) { _mm_stream_si128(reinterpret_cast<__m128i*>(dstRowPtr + 0), v0); _mm_stream_si128(reinterpret_cast<__m128i*>(dstRowPtr + 16), v1); _mm_stream_si128(reinterpret_cast<__m128i*>(dstRowPtr + 32), v2); _mm_stream_si128(reinterpret_cast<__m128i*>(dstRowPtr + 48), v3); } else { _mm_storeu_si128(reinterpret_cast<__m128i*>(dstRowPtr + 0), v0); _mm_storeu_si128(reinterpret_cast<__m128i*>(dstRowPtr + 16), v1); _mm_storeu_si128(reinterpret_cast<__m128i*>(dstRowPtr + 32), v2); _mm_storeu_si128(reinterpret_cast<__m128i*>(dstRowPtr + 48), v3); } } } } // 8x8 直接转置 FORCE_INLINE void transpose_8x8_u8_to_strided(const uint8_t* pSrc, unsigned int srcStep, uint8_t* pDst, unsigned int dstStep) { __m128i r0 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 0 * srcStep)); __m128i r1 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 1 * srcStep)); __m128i r2 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 2 * srcStep)); __m128i r3 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 3 * srcStep)); __m128i r4 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 4 * srcStep)); __m128i r5 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 5 * srcStep)); __m128i r6 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 6 * srcStep)); __m128i r7 = _mm_loadl_epi64(reinterpret_cast<const __m128i*>(pSrc + 7 * srcStep)); __m128i t0 = _mm_unpacklo_epi8(r0, r1); __m128i t1 = _mm_unpacklo_epi8(r2, r3); __m128i t2 = _mm_unpacklo_epi8(r4, r5); __m128i t3 = _mm_unpacklo_epi8(r6, r7); __m128i t4 = _mm_unpacklo_epi16(t0, t1); __m128i t5 = _mm_unpacklo_epi16(t2, t3); __m128i t6 = _mm_unpackhi_epi16(t0, t1); __m128i t7 = _mm_unpackhi_epi16(t2, t3); __m128i c0 = _mm_unpacklo_epi32(t4, t5); __m128i c1 = _mm_unpackhi_epi32(t4, t5); __m128i c2 = _mm_unpacklo_epi32(t6, t7); __m128i c3 = _mm_unpackhi_epi32(t6, t7); _mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 0 * dstStep), c0); _mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 1 * dstStep), _mm_srli_si128(c0, 8)); _mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 2 * dstStep), c1); _mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 3 * dstStep), _mm_srli_si128(c1, 8)); _mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 4 * dstStep), c2); _mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 5 * dstStep), _mm_srli_si128(c2, 8)); _mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 6 * dstStep), c3); _mm_storel_epi64(reinterpret_cast<__m128i*>(pDst + 7 * dstStep), _mm_srli_si128(c3, 8)); } // 如果内存是64字节对齐速度会更快 // void aligned_free_wrapper(void* ptr) { _aligned_free(ptr); } // // using AlignedUniquePtr = std::unique_ptr<uint8_t[], void(*)(void*)>; // // AlignedUniquePtr make_aligned_buffer(size_t size, size_t alignment) { // size_t remainder = size % alignment; // size_t alloc_size = (remainder == 0) ? size : (size + alignment - remainder); // // void* ptr = nullptr; // // ptr = _aligned_malloc(alloc_size, alignment); // // return AlignedUniquePtr(static_cast<uint8_t*>(ptr), aligned_free_wrapper); // } // // class TransposeFixture : public benchmark::Fixture { // public: // AlignedUniquePtr src_owner{nullptr, std::free}; // AlignedUniquePtr dst_owner{nullptr, std::free}; // // uint8_t* src = nullptr; // uint8_t* dst = nullptr; // // const int width = 4096; // const int height = 4096; // size_t step; // // void SetUp(const benchmark::State& state) override { // step = width; // size_t total_bytes = step * height; // size_t alignment = 64; // // src_owner = make_aligned_buffer(total_bytes, alignment); // dst_owner = make_aligned_buffer(total_bytes, alignment); // // if (!src_owner || !dst_owner) { // const_cast<benchmark::State&>(state).SkipWithError("Memory allocation failed!"); // return; // } // // src = src_owner.get(); // dst = dst_owner.get(); // // std::memset(src, 128, total_bytes); // std::memset(dst, 0, total_bytes); // } // // void TearDown(const benchmark::State& state) override { // } // }; class TransposeFixture : public benchmark::Fixture { public: // Changed to standard unique_ptr array std::unique_ptr<uint8_t[]> src_owner; std::unique_ptr<uint8_t[]> dst_owner; uint8_t* src = nullptr; uint8_t* dst = nullptr; const int width = 4096; const int height = 4096; size_t step; void SetUp(const benchmark::State& state) override { step = width; size_t total_bytes = step * height; // Removed alignment logic, using standard new[] try { src_owner = std::make_unique<uint8_t[]>(total_bytes); dst_owner = std::make_unique<uint8_t[]>(total_bytes); } catch (const std::bad_alloc&) { const_cast<benchmark::State&>(state).SkipWithError("Memory allocation failed!"); return; } src = src_owner.get(); dst = dst_owner.get(); std::memset(src, 128, total_bytes); std::memset(dst, 0, total_bytes); } void TearDown(const benchmark::State& state) override { } }; // 空跑的基准测试，目的是在实际测试开始前唤醒 CPU 到高频状态 void CPU_WarmUp(benchmark::State& state) { for (auto _ : state) { // 进行一些简单的浮点运算以消耗 CPU 周期 volatile double x = 1.0; for (int i = 0; i < 1000; ++i) { x = x * 1.0001 + 0.001; } benchmark::DoNotOptimize(x); } } // 强制 WarmUp 至少运行 1 秒，并排在最前面 BENCHMARK(CPU_WarmUp)->MinTime(1.0); BENCHMARK_F(TransposeFixture, Std_Memcpy)(benchmark::State& state) { size_t size = size_t(width) * height; for (auto _ : state) { std::memcpy(dst, src, size); benchmark::DoNotOptimize(dst); } state.SetBytesProcessed(int64_t(state.iterations()) * int64_t(width) * int64_t(height) * 2); } BENCHMARK_F(TransposeFixture, Buffered_64x64_Stream)(benchmark::State& state) { for (auto _ : state) { // 按 64x64 块遍历 for (int y = 0; y < height; y += 64) { for (int x = 0; x < width; x += 64) { const uint8_t* sTile = src + y * step + x; uint8_t* dTile = dst + x * step + y; transpose_64x64_tile_impl<true>(sTile, step, dTile, step); } } _mm_sfence(); } state.SetBytesProcessed(int64_t(state.iterations()) * int64_t(width) * int64_t(height) * 2); } BENCHMARK_F(TransposeFixture, Direct_8x8_StoreU)(benchmark::State& state) { for (auto _ : state) { for (int y = 0; y < height; y += 8) { const uint8_t* src_row_ptr = src + y * step; for (int x = 0; x < width; x += 8) { uint8_t* dst_block_ptr = dst + x * step + y; transpose_8x8_u8_to_strided(src_row_ptr + x, step, dst_block_ptr, step); } } } state.SetBytesProcessed(int64_t(state.iterations()) * int64_t(width) * int64_t(height) * 2); } BENCHMARK_MAIN(); 测试结果
TransposeFixture/Std_Memcpy 602332 ns 593750 ns 1000 bytes_per_second=52.6316Gi/s TransposeFixture/Buffered_64x64_Stream 922109 ns 920348 ns 747 bytes_per_second=33.9545Gi/s TransposeFixture/Direct_8x8_StoreU 12036794 ns 12187500 ns 50 bytes_per_second=2.5641Gi/s 标准库的 memcpy 在 4Kx4K 这种完全顺序、完全对齐、完全不用做重排的场景里，是一个很好的上限参考。
显然，streaming store 在这个场景下确实已经遥遥领先。当然前提是用对。
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