diff options
author | Matti Picus <matti.picus@gmail.com> | 2020-12-11 08:34:50 +0200 |
---|---|---|
committer | GitHub <noreply@github.com> | 2020-12-11 08:34:50 +0200 |
commit | 9e26d1d2be7a961a16f8fa9ff7820c33b25415e2 (patch) | |
tree | 08abaed7fdb7f1ed4002b208f1938887f69255d0 | |
parent | 91d9bbebc159ada6cccd0e1fcec1926e26b19c6f (diff) | |
parent | f921f0d13bb34d82503bfa2b3bff24d095bb9385 (diff) | |
download | numpy-9e26d1d2be7a961a16f8fa9ff7820c33b25415e2.tar.gz |
Merge pull request #17782 from Qiyu8/einsum-muladd
SIMD: Optimize the performance of einsum's submodule multiply by using universal intrinsics
-rw-r--r-- | numpy/core/src/multiarray/einsum_sumprod.c.src | 183 |
1 files changed, 82 insertions, 101 deletions
diff --git a/numpy/core/src/multiarray/einsum_sumprod.c.src b/numpy/core/src/multiarray/einsum_sumprod.c.src index c58e74287..caba0e00a 100644 --- a/numpy/core/src/multiarray/einsum_sumprod.c.src +++ b/numpy/core/src/multiarray/einsum_sumprod.c.src @@ -17,7 +17,8 @@ #include "einsum_sumprod.h" #include "einsum_debug.h" - +#include "simd/simd.h" +#include "common.h" #ifdef NPY_HAVE_SSE_INTRINSICS #define EINSUM_USE_SSE1 1 @@ -41,6 +42,13 @@ #define EINSUM_IS_SSE_ALIGNED(x) ((((npy_intp)x)&0xf) == 0) +// ARM/Neon don't have instructions for aligned memory access +#ifdef NPY_HAVE_NEON + #define EINSUM_IS_ALIGNED(x) 0 +#else + #define EINSUM_IS_ALIGNED(x) npy_is_aligned(x, NPY_SIMD_WIDTH) +#endif + /**********************************************/ /**begin repeat @@ -56,6 +64,10 @@ * npy_ubyte, npy_ushort, npy_uint, npy_ulong, npy_ulonglong, * npy_float, npy_float, npy_double, npy_longdouble, * npy_float, npy_double, npy_longdouble# + * #sfx = s8, s16, s32, long, s64, + * u8, u16, u32, ulong, u64, + * half, f32, f64, longdouble, + * f32, f64, clongdouble# * #to = ,,,,, * ,,,,, * npy_float_to_half,,,, @@ -76,6 +88,10 @@ * 0*5, * 0,0,1,0, * 0*3# + * #NPYV_CHK = 0*5, + * 0*5, + * 0, NPY_SIMD, NPY_SIMD_F64, 0, + * 0*3# */ /**begin repeat1 @@ -250,115 +266,80 @@ static void @type@ *data0 = (@type@ *)dataptr[0]; @type@ *data1 = (@type@ *)dataptr[1]; @type@ *data_out = (@type@ *)dataptr[2]; - -#if EINSUM_USE_SSE1 && @float32@ - __m128 a, b; -#elif EINSUM_USE_SSE2 && @float64@ - __m128d a, b; -#endif - NPY_EINSUM_DBG_PRINT1("@name@_sum_of_products_contig_two (%d)\n", (int)count); - -/* This is placed before the main loop to make small counts faster */ -finish_after_unrolled_loop: - switch (count) { -/**begin repeat2 - * #i = 6, 5, 4, 3, 2, 1, 0# - */ - case @i@+1: - data_out[@i@] = @to@(@from@(data0[@i@]) * - @from@(data1[@i@]) + - @from@(data_out[@i@])); -/**end repeat2**/ - case 0: - return; - } - -#if EINSUM_USE_SSE1 && @float32@ + // NPYV check for @type@ +#if @NPYV_CHK@ /* Use aligned instructions if possible */ - if (EINSUM_IS_SSE_ALIGNED(data0) && EINSUM_IS_SSE_ALIGNED(data1) && - EINSUM_IS_SSE_ALIGNED(data_out)) { - /* Unroll the loop by 8 */ - while (count >= 8) { - count -= 8; - -/**begin repeat2 - * #i = 0, 4# - */ - a = _mm_mul_ps(_mm_load_ps(data0+@i@), _mm_load_ps(data1+@i@)); - b = _mm_add_ps(a, _mm_load_ps(data_out+@i@)); - _mm_store_ps(data_out+@i@, b); -/**end repeat2**/ - data0 += 8; - data1 += 8; - data_out += 8; + const int is_aligned = EINSUM_IS_ALIGNED(data0) && EINSUM_IS_ALIGNED(data1) && + EINSUM_IS_ALIGNED(data_out); + const int vstep = npyv_nlanes_@sfx@; + + /**begin repeat2 + * #cond = if(is_aligned), else# + * #ld = loada, load# + * #st = storea, store# + */ + @cond@ { + const npy_intp vstepx4 = vstep * 4; + for (; count >= vstepx4; count -= vstepx4, data0 += vstepx4, data1 += vstepx4, data_out += vstepx4) { + /**begin repeat3 + * #i = 0, 1, 2, 3# + */ + npyv_@sfx@ a@i@ = npyv_@ld@_@sfx@(data0 + vstep * @i@); + npyv_@sfx@ b@i@ = npyv_@ld@_@sfx@(data1 + vstep * @i@); + npyv_@sfx@ c@i@ = npyv_@ld@_@sfx@(data_out + vstep * @i@); + /**end repeat3**/ + /**begin repeat3 + * #i = 0, 1, 2, 3# + */ + npyv_@sfx@ abc@i@ = npyv_muladd_@sfx@(a@i@, b@i@, c@i@); + /**end repeat3**/ + /**begin repeat3 + * #i = 0, 1, 2, 3# + */ + npyv_@st@_@sfx@(data_out + vstep * @i@, abc@i@); + /**end repeat3**/ } - - /* Finish off the loop */ - goto finish_after_unrolled_loop; } -#elif EINSUM_USE_SSE2 && @float64@ - /* Use aligned instructions if possible */ - if (EINSUM_IS_SSE_ALIGNED(data0) && EINSUM_IS_SSE_ALIGNED(data1) && - EINSUM_IS_SSE_ALIGNED(data_out)) { - /* Unroll the loop by 8 */ - while (count >= 8) { - count -= 8; - -/**begin repeat2 - * #i = 0, 2, 4, 6# - */ - a = _mm_mul_pd(_mm_load_pd(data0+@i@), _mm_load_pd(data1+@i@)); - b = _mm_add_pd(a, _mm_load_pd(data_out+@i@)); - _mm_store_pd(data_out+@i@, b); -/**end repeat2**/ - data0 += 8; - data1 += 8; - data_out += 8; - } - - /* Finish off the loop */ - goto finish_after_unrolled_loop; + /**end repeat2**/ + for (; count > 0; count -= vstep, data0 += vstep, data1 += vstep, data_out += vstep) { + npyv_@sfx@ a = npyv_load_tillz_@sfx@(data0, count); + npyv_@sfx@ b = npyv_load_tillz_@sfx@(data1, count); + npyv_@sfx@ c = npyv_load_tillz_@sfx@(data_out, count); + npyv_store_till_@sfx@(data_out, count, npyv_muladd_@sfx@(a, b, c)); } -#endif - - /* Unroll the loop by 8 */ - while (count >= 8) { - count -= 8; - -#if EINSUM_USE_SSE1 && @float32@ -/**begin repeat2 - * #i = 0, 4# - */ - a = _mm_mul_ps(_mm_loadu_ps(data0+@i@), _mm_loadu_ps(data1+@i@)); - b = _mm_add_ps(a, _mm_loadu_ps(data_out+@i@)); - _mm_storeu_ps(data_out+@i@, b); -/**end repeat2**/ -#elif EINSUM_USE_SSE2 && @float64@ -/**begin repeat2 - * #i = 0, 2, 4, 6# - */ - a = _mm_mul_pd(_mm_loadu_pd(data0+@i@), _mm_loadu_pd(data1+@i@)); - b = _mm_add_pd(a, _mm_loadu_pd(data_out+@i@)); - _mm_storeu_pd(data_out+@i@, b); -/**end repeat2**/ + npyv_cleanup(); #else -/**begin repeat2 - * #i = 0, 1, 2, 3, 4, 5, 6, 7# - */ - data_out[@i@] = @to@(@from@(data0[@i@]) * - @from@(data1[@i@]) + - @from@(data_out[@i@])); -/**end repeat2**/ -#endif - data0 += 8; - data1 += 8; - data_out += 8; +#ifndef NPY_DISABLE_OPTIMIZATION + for (; count >= 4; count -= 4, data0 += 4, data1 += 4, data_out += 4) { + /**begin repeat2 + * #i = 0, 1, 2, 3# + */ + const @type@ a@i@ = @from@(data0[@i@]); + const @type@ b@i@ = @from@(data1[@i@]); + const @type@ c@i@ = @from@(data_out[@i@]); + /**end repeat2**/ + /**begin repeat2 + * #i = 0, 1, 2, 3# + */ + const @type@ abc@i@ = a@i@ * b@i@ + c@i@; + /**end repeat2**/ + /**begin repeat2 + * #i = 0, 1, 2, 3# + */ + data_out[@i@] = @to@(abc@i@); + /**end repeat2**/ } +#endif // !NPY_DISABLE_OPTIMIZATION + for (; count > 0; --count, ++data0, ++data1, ++data_out) { + const @type@ a = @from@(*data0); + const @type@ b = @from@(*data1); + const @type@ c = @from@(*data_out); + *data_out = @to@(a * b + c); + } +#endif // NPYV check for @type@ - /* Finish off the loop */ - goto finish_after_unrolled_loop; } /* Some extra specializations for the two operand case */ |