search

bluss / matrixmultiply

General matrix multiplication of f32 and f64 matrices in Rust. Supports matrices with general strides.
https://docs.rs/matrixmultiply/
Apache License 2.0
209 stars 25 forks source link

Use one Vec for both packing buffers #7

Closed bluss closed 8 years ago

bluss commented 8 years ago

Use one Vec for both packing buffers

This removes a small overhead that is especially visible for small problem sizes.

Note: This library's primary focus is on large matrices, yet we will improve the small matrix multiplication problems when we can do so without penalty for the larger cases.

mat_mul_f32::m004  172                   142                              -30  -17.44%
mat_mul_f32::m005  230                   200                              -30  -13.04%
mat_mul_f32::m006  250                   215                              -35  -14.00%
mat_mul_f32::m007  274                   242                              -32  -11.68%
mat_mul_f32::m008  289                   251                              -38  -13.15%
mat_mul_f32::m009  499                   457                              -42   -8.42%
mat_mul_f32::m012  670                   606                              -64   -9.55%
mat_mul_f32::m016  956                   910                              -46   -4.81%
mat_mul_f32::m032  4,645                 4,595                            -50   -1.08%
mat_mul_f32::m064  27,923                28,205                           282    1.01%
mat_mul_f64::m004  178                   145                              -33  -18.54%
mat_mul_f64::m007  286                   257                              -29  -10.14%
mat_mul_f64::m008  319                   276                              -43  -13.48%
mat_mul_f64::m012  745                   678                              -67   -8.99%
bluss commented 8 years ago

@petevine Is it possible for you to bench this on ARM (NEON) again? I'm not certain there will be larger wins there, but one could hope..

MagaTailor commented 8 years ago

Sure, give me a minute.

MagaTailor commented 8 years ago 
running 41 tests
test mat_mul_f32::m004             ... bench:       1,635 ns/iter (+/- 27)
test mat_mul_f32::m005             ... bench:       2,220 ns/iter (+/- 32)
test mat_mul_f32::m006             ... bench:       2,515 ns/iter (+/- 89)
test mat_mul_f32::m007             ... bench:       2,752 ns/iter (+/- 315)
test mat_mul_f32::m008             ... bench:       2,864 ns/iter (+/- 44)
test mat_mul_f32::m009             ... bench:       5,879 ns/iter (+/- 383)
test mat_mul_f32::m012             ... bench:       7,680 ns/iter (+/- 71)
test mat_mul_f32::m016             ... bench:      11,891 ns/iter (+/- 545)
test mat_mul_f32::m032             ... bench:      68,891 ns/iter (+/- 1,118)
test mat_mul_f32::m064             ... bench:     511,802 ns/iter (+/- 11,075)
test mat_mul_f32::m127             ... bench:   4,011,067 ns/iter (+/- 80,360)
test mat_mul_f32::m256             ... bench:  32,911,345 ns/iter (+/- 244,070)
test mat_mul_f32::m512             ... bench: 262,133,757 ns/iter (+/- 410,091)
test mat_mul_f32::mix128x10000x128 ... bench: 336,832,987 ns/iter (+/- 624,222)
test mat_mul_f32::mix16x4          ... bench:      21,936 ns/iter (+/- 327)
test mat_mul_f32::mix32x2          ... bench:      18,080 ns/iter (+/- 322)
test mat_mul_f32::mix97            ... bench:   2,334,810 ns/iter (+/- 48,680)

test mat_mul_f64::m004             ... bench:       2,232 ns/iter (+/- 40)
test mat_mul_f64::m007             ... bench:       4,935 ns/iter (+/- 54)
test mat_mul_f64::m008             ... bench:       5,202 ns/iter (+/- 138)
test mat_mul_f64::m012             ... bench:      16,797 ns/iter (+/- 688)
test mat_mul_f64::m016             ... bench:      27,916 ns/iter (+/- 279)
test mat_mul_f64::m032             ... bench:     199,725 ns/iter (+/- 4,456)
test mat_mul_f64::m064             ... bench:   1,591,507 ns/iter (+/- 41,950)
test mat_mul_f64::m127             ... bench:  13,077,357 ns/iter (+/- 97,630)
test mat_mul_f64::m256             ... bench: 108,618,579 ns/iter (+/- 574,423)
test mat_mul_f64::m512             ... bench: 862,021,707 ns/iter (+/- 1,782,128)
test mat_mul_f64::mix128x10000x128 ... bench: 1,072,794,838 ns/iter (+/- 1,792,908)
test mat_mul_f64::mix16x4          ... bench:      41,538 ns/iter (+/- 2,493)
test mat_mul_f64::mix32x2          ... bench:      33,025 ns/iter (+/- 554)
test mat_mul_f64::mix97            ... bench:   7,830,734 ns/iter (+/- 94,210)

test ref_mat_mul_f32::m004         ... bench:         618 ns/iter (+/- 8)
test ref_mat_mul_f32::m005         ... bench:       1,070 ns/iter (+/- 19)
test ref_mat_mul_f32::m006         ... bench:       1,722 ns/iter (+/- 9)
test ref_mat_mul_f32::m007         ... bench:       2,605 ns/iter (+/- 18)
test ref_mat_mul_f32::m008         ... bench:       3,754 ns/iter (+/- 18)
test ref_mat_mul_f32::m009         ... bench:       5,548 ns/iter (+/- 56)
test ref_mat_mul_f32::m012         ... bench:      11,939 ns/iter (+/- 55)
test ref_mat_mul_f32::m016         ... bench:      27,278 ns/iter (+/- 255)
test ref_mat_mul_f32::m032         ... bench:     207,125 ns/iter (+/- 1,085)
test ref_mat_mul_f32::m064         ... bench:   1,655,207 ns/iter (+/- 23,070)
bluss commented 8 years ago

Thank you!

Let's put that through cargo-benchcmp and compare with the previous https://github.com/bluss/matrixmultiply/issues/6#issue-145536556 numbers, it looks like a similar speedup (maybe a bit more) for the small sizes. Diff for 64 might be a fluke.

name               neontwoalloc.log ns/iter  neononealloc.log ns/iter    diff ns/iter   diff %
mat_mul_f32::m004  2,026                     1,635                               -391  -19.30%
mat_mul_f32::m005  2,852                     2,220                               -632  -22.16%
mat_mul_f32::m006  2,955                     2,515                               -440  -14.89%
mat_mul_f32::m007  3,191                     2,752                               -439  -13.76%
mat_mul_f32::m008  3,404                     2,864                               -540  -15.86%
mat_mul_f32::m009  6,722                     5,879                               -843  -12.54%
mat_mul_f32::m012  8,575                     7,680                               -895  -10.44%
mat_mul_f32::m016  12,836                    11,891                              -945   -7.36%
mat_mul_f32::m032  72,610                    68,891                            -3,719   -5.12%
bluss commented 8 years ago

Either a fluke, or allocation overhead behaves differently on ARM.

MagaTailor commented 8 years ago

I guess you'd get more consistent results on aarch64. And besides, the Cortex-A5 processor is about low power/efficiency above all else so the results are not entirely representative of the benefits one would expect from NEON.

bluss commented 8 years ago

I don't know NEON at all, but I'd first look at the larger matrix cases to evaluate it. For small enough matrices, there's a lot of overhead.

MagaTailor commented 8 years ago

I suspected the overhead was related to 32-bit and it looks plausible; on i686:

test mat_mul_f32::m004             ... bench:         310 ns/iter (+/- 4)
test mat_mul_f32::m007             ... bench:         622 ns/iter (+/- 11)
test mat_mul_f32::m008             ... bench:         575 ns/iter (+/- 4)
test mat_mul_f32::m012             ... bench:       1,510 ns/iter (+/- 5)
test mat_mul_f32::m016             ... bench:       2,124 ns/iter (+/- 20)

test mat_mul_f64::m004             ... bench:         397 ns/iter (+/- 0)
test mat_mul_f64::m007             ... bench:         764 ns/iter (+/- 3)
test mat_mul_f64::m008             ... bench:         838 ns/iter (+/- 5)
test mat_mul_f64::m012             ... bench:       2,404 ns/iter (+/- 4)
test mat_mul_f64::m016             ... bench:       3,993 ns/iter (+/- 44)
bluss commented 8 years ago

@petevine Sorry, can you explain what I should compare that to? It looks good to me though.

MagaTailor commented 8 years ago

I thought you you were pointing to the much smaller difference for 64 on your platform. Never mind, it seems I misread your meaning, having omitted any 64 results before.

bluss commented 8 years ago

Oh I said “Diff for 64 might be a fluke.” apparently but I meant that the diff for mat_mul_f32::m032 might be a fluke. Off by a power of two.., sorry :smile:

MagaTailor commented 8 years ago

I've got another data point, coming from a 2GHz @64-bit ARM Cortex-A53 processor (compared with a 1.7GHz Cortex-A5, it should be about 50% faster, not counting 64-bit benefits):

running 41 tests
test mat_mul_f32::m004             ... bench:       1,098 ns/iter (+/- 12)
test mat_mul_f32::m005             ... bench:       1,587 ns/iter (+/- 22)
test mat_mul_f32::m006             ... bench:       1,718 ns/iter (+/- 21)
test mat_mul_f32::m007             ... bench:       1,923 ns/iter (+/- 9)
test mat_mul_f32::m008             ... bench:       2,138 ns/iter (+/- 9)
test mat_mul_f32::m009             ... bench:       4,260 ns/iter (+/- 33)
test mat_mul_f32::m012             ... bench:       5,484 ns/iter (+/- 40)
test mat_mul_f32::m016             ... bench:       8,621 ns/iter (+/- 74)
test mat_mul_f32::m032             ... bench:      48,623 ns/iter (+/- 183)
test mat_mul_f32::m064             ... bench:     328,603 ns/iter (+/- 1,405)
test mat_mul_f32::m127             ... bench:   2,387,223 ns/iter (+/- 34,915)
test mat_mul_f32::m256             ... bench:  20,490,803 ns/iter (+/- 180,891)
test mat_mul_f32::m512             ... bench: 164,162,031 ns/iter (+/- 545,316)
test mat_mul_f32::mix128x10000x128 ... bench: 216,476,447 ns/iter (+/- 358,523)
test mat_mul_f32::mix16x4          ... bench:      17,923 ns/iter (+/- 49)
test mat_mul_f32::mix32x2          ... bench:      15,748 ns/iter (+/- 62)
test mat_mul_f32::mix97            ... bench:   1,449,014 ns/iter (+/- 7,570)

test mat_mul_f64::m004             ... bench:       1,202 ns/iter (+/- 21)
test mat_mul_f64::m007             ... bench:       2,102 ns/iter (+/- 10)
test mat_mul_f64::m008             ... bench:       2,547 ns/iter (+/- 18)
test mat_mul_f64::m012             ... bench:       7,781 ns/iter (+/- 36)
test mat_mul_f64::m016             ... bench:      12,981 ns/iter (+/- 56)
test mat_mul_f64::m032             ... bench:      88,629 ns/iter (+/- 365)
test mat_mul_f64::m064             ... bench:     665,406 ns/iter (+/- 4,620)
test mat_mul_f64::m127             ... bench:   5,531,354 ns/iter (+/- 157,512)
test mat_mul_f64::m256             ... bench:  45,800,153 ns/iter (+/- 82,341)
test mat_mul_f64::m512             ... bench: 365,977,216 ns/iter (+/- 402,251)
test mat_mul_f64::mix128x10000x128 ... bench: 493,044,547 ns/iter (+/- 463,301)
test mat_mul_f64::mix16x4          ... bench:      20,585 ns/iter (+/- 62)
test mat_mul_f64::mix32x2          ... bench:      13,516 ns/iter (+/- 47)
test mat_mul_f64::mix97            ... bench:   3,202,931 ns/iter (+/- 74,201)

test ref_mat_mul_f32::m004         ... bench:         530 ns/iter (+/- 4)
test ref_mat_mul_f32::m005         ... bench:         941 ns/iter (+/- 2)
test ref_mat_mul_f32::m006         ... bench:       1,537 ns/iter (+/- 4)
test ref_mat_mul_f32::m007         ... bench:       2,689 ns/iter (+/- 30)
test ref_mat_mul_f32::m008         ... bench:       3,783 ns/iter (+/- 4)
test ref_mat_mul_f32::m009         ... bench:       5,307 ns/iter (+/- 66)
test ref_mat_mul_f32::m012         ... bench:      11,755 ns/iter (+/- 28)
test ref_mat_mul_f32::m016         ... bench:      26,824 ns/iter (+/- 42)
test ref_mat_mul_f32::m032         ... bench:     202,902 ns/iter (+/- 350)
test ref_mat_mul_f32::m064         ... bench:   1,584,415 ns/iter (+/- 3,420)