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authorGravatar Rasmus Munk Larsen <rmlarsen@google.com>2017-01-24 13:55:18 -0800
committerGravatar Rasmus Munk Larsen <rmlarsen@google.com>2017-01-24 13:55:18 -0800
commite6b10202218631be755f19c41fe01287b9a37f90 (patch)
treed4e71c511fb295d7038d26c7fe60bcd9c7dc418a /unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h
parent156e6234f1921987ab63321dbea885b75e6ae70b (diff)
Adds a fast memcpy function to Eigen. This takes advantage of the following:
1. For small fixed sizes, the compiler generates inline code for memcpy, which is much faster. 2. My colleague eriche at googl dot com discovered that for large sizes, memmove is significantly faster than memcpy (at least on Linux with GCC or Clang). See benchmark numbers measured on a Haswell (HP Z440) workstation here: https://docs.google.com/a/google.com/spreadsheets/d/1jLs5bKzXwhpTySw65MhG1pZpsIwkszZqQTjwrd_n0ic/pubhtml This is of course surprising since memcpy is a less constrained version of memmove. This stackoverflow thread contains some speculation as to the causes: http://stackoverflow.com/questions/22793669/poor-memcpy-performance-on-linux Below are numbers for copying and slicing tensors using the multithreaded TensorDevice. The numbers show significant improvements for memcpy of very small blocks and for memcpy of large blocks single threaded (we were already able to saturate memory bandwidth for >1 threads before on large blocks). The "slicingSmallPieces" benchmark also shows small consistent improvements, since memcpy cost is a fair portion of that particular computation. The benchmarks operate on NxN matrices, and the names are of the form BM_$OP_${NUMTHREADS}T/${N}. Measured improvements in wall clock time: Run on rmlarsen3.mtv (12 X 3501 MHz CPUs); 2017-01-20T11:26:31.493023454-08:00 CPU: Intel Haswell with HyperThreading (6 cores) dL1:32KB dL2:256KB dL3:15MB Benchmark Base (ns) New (ns) Improvement ------------------------------------------------------------------ BM_memcpy_1T/2 3.48 2.39 +31.3% BM_memcpy_1T/8 12.3 6.51 +47.0% BM_memcpy_1T/64 371 383 -3.2% BM_memcpy_1T/512 66922 66720 +0.3% BM_memcpy_1T/4k 9892867 6849682 +30.8% BM_memcpy_1T/5k 14951099 10332856 +30.9% BM_memcpy_2T/2 3.50 2.46 +29.7% BM_memcpy_2T/8 12.3 7.66 +37.7% BM_memcpy_2T/64 371 376 -1.3% BM_memcpy_2T/512 66652 66788 -0.2% BM_memcpy_2T/4k 6145012 6117776 +0.4% BM_memcpy_2T/5k 9181478 9010942 +1.9% BM_memcpy_4T/2 3.47 2.47 +31.0% BM_memcpy_4T/8 12.3 6.67 +45.8 BM_memcpy_4T/64 374 376 -0.5% BM_memcpy_4T/512 67833 68019 -0.3% BM_memcpy_4T/4k 5057425 5188253 -2.6% BM_memcpy_4T/5k 7555638 7779468 -3.0% BM_memcpy_6T/2 3.51 2.50 +28.8% BM_memcpy_6T/8 12.3 7.61 +38.1% BM_memcpy_6T/64 373 378 -1.3% BM_memcpy_6T/512 66871 66774 +0.1% BM_memcpy_6T/4k 5112975 5233502 -2.4% BM_memcpy_6T/5k 7614180 7772246 -2.1% BM_memcpy_8T/2 3.47 2.41 +30.5% BM_memcpy_8T/8 12.4 10.5 +15.3% BM_memcpy_8T/64 372 388 -4.3% BM_memcpy_8T/512 67373 66588 +1.2% BM_memcpy_8T/4k 5148462 5254897 -2.1% BM_memcpy_8T/5k 7660989 7799058 -1.8% BM_memcpy_12T/2 3.50 2.40 +31.4% BM_memcpy_12T/8 12.4 7.55 +39.1 BM_memcpy_12T/64 374 378 -1.1% BM_memcpy_12T/512 67132 66683 +0.7% BM_memcpy_12T/4k 5185125 5292920 -2.1% BM_memcpy_12T/5k 7717284 7942684 -2.9% BM_slicingSmallPieces_1T/2 47.3 47.5 +0.4% BM_slicingSmallPieces_1T/8 53.6 52.3 +2.4% BM_slicingSmallPieces_1T/64 491 476 +3.1% BM_slicingSmallPieces_1T/512 21734 18814 +13.4% BM_slicingSmallPieces_1T/4k 394660 396760 -0.5% BM_slicingSmallPieces_1T/5k 218722 209244 +4.3% BM_slicingSmallPieces_2T/2 80.7 79.9 +1.0% BM_slicingSmallPieces_2T/8 54.2 53.1 +2.0 BM_slicingSmallPieces_2T/64 497 477 +4.0% BM_slicingSmallPieces_2T/512 21732 18822 +13.4% BM_slicingSmallPieces_2T/4k 392885 390490 +0.6% BM_slicingSmallPieces_2T/5k 221988 208678 +6.0% BM_slicingSmallPieces_4T/2 80.8 80.1 +0.9% BM_slicingSmallPieces_4T/8 54.1 53.2 +1.7% BM_slicingSmallPieces_4T/64 493 476 +3.4% BM_slicingSmallPieces_4T/512 21702 18758 +13.6% BM_slicingSmallPieces_4T/4k 393962 404023 -2.6% BM_slicingSmallPieces_4T/5k 249667 211732 +15.2% BM_slicingSmallPieces_6T/2 80.5 80.1 +0.5% BM_slicingSmallPieces_6T/8 54.4 53.4 +1.8% BM_slicingSmallPieces_6T/64 488 478 +2.0% BM_slicingSmallPieces_6T/512 21719 18841 +13.3% BM_slicingSmallPieces_6T/4k 394950 397583 -0.7% BM_slicingSmallPieces_6T/5k 223080 210148 +5.8% BM_slicingSmallPieces_8T/2 81.2 80.4 +1.0% BM_slicingSmallPieces_8T/8 58.1 53.5 +7.9% BM_slicingSmallPieces_8T/64 489 480 +1.8% BM_slicingSmallPieces_8T/512 21586 18798 +12.9% BM_slicingSmallPieces_8T/4k 394592 400165 -1.4% BM_slicingSmallPieces_8T/5k 219688 208301 +5.2% BM_slicingSmallPieces_12T/2 80.2 79.8 +0.7% BM_slicingSmallPieces_12T/8 54.4 53.4 +1.8 BM_slicingSmallPieces_12T/64 488 476 +2.5% BM_slicingSmallPieces_12T/512 21931 18831 +14.1% BM_slicingSmallPieces_12T/4k 393962 396541 -0.7% BM_slicingSmallPieces_12T/5k 218803 207965 +5.0%
Diffstat (limited to 'unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h')
-rw-r--r--unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h4
1 files changed, 2 insertions, 2 deletions
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h b/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h
index 08eb5595a..f060191ab 100644
--- a/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h
+++ b/unsupported/Eigen/CXX11/src/Tensor/TensorFFT.h
@@ -253,7 +253,7 @@ struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, D
// get data into line_buf
const Index stride = m_strides[dim];
if (stride == 1) {
- memcpy(line_buf, &buf[base_offset], line_len*sizeof(ComplexScalar));
+ m_device.memcpy(line_buf, &buf[base_offset], line_len*sizeof(ComplexScalar));
} else {
Index offset = base_offset;
for (int j = 0; j < line_len; ++j, offset += stride) {
@@ -271,7 +271,7 @@ struct TensorEvaluator<const TensorFFTOp<FFT, ArgType, FFTResultType, FFTDir>, D
// write back
if (FFTDir == FFT_FORWARD && stride == 1) {
- memcpy(&buf[base_offset], line_buf, line_len*sizeof(ComplexScalar));
+ m_device.memcpy(&buf[base_offset], line_buf, line_len*sizeof(ComplexScalar));
} else {
Index offset = base_offset;
const ComplexScalar div_factor = ComplexScalar(1.0 / line_len, 0);