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Ublas : |
Subject: Re: [ublas] Matrix multiplication performance
From: Michael Lehn (michael.lehn_at_[hidden])
Date: 2016-02-01 11:52:55
Thanks, for doing the benchmarks!
Could you add what parameters MR, NR, MC, KC, NC you used? Did you
try MR=4, NR=8 on
machines with AVX and MR=4, NR=12 on machines with FMA?
Cheers,
Michael
Quoting palik imre <imre_palik_at_[hidden]>:
> On Haswell, with gcc4.8
>
> My kernel:
>
> $ ./matprod5
> # m n k uBLAS: t1 MFLOPS Blocked: t2
> MFLOPS Diff nrm1
> 100 100 100 0.000448612 4458.2 0.000247042
> 8095.79 0
> 200 200 200 0.00465023 3440.69 0.00125286
> 12770.8 0
> 300 300 300 0.0149776 3605.38 0.00356466
> 15148.7 0
> 400 400 400 0.0310101 4127.69 0.00667965
> 19162.7 0
> 500 500 500 0.051142 4888.35 0.010724
> 23312.2 0
> 600 600 600 0.0768814 5619.04 0.0171774
> 25149.3 8.50317e-18
> 700 700 700 0.120575 5689.42 0.0257486
> 26642.3 5.79178e-18
> 800 800 800 0.180436 5675.14 0.0368777
> 27767.5 3.6748e-18
> 900 900 900 0.255451 5707.54 0.0542328
> 26884.1 2.34099e-18
> 1000 1000 1000 0.351663 5687.26 0.0728293
> 27461.5 1.53613e-18
> 1100 1100 1100 0.46676 5703.15 0.102033
> 26089.7 1.03026e-18
> 1200 1200 1200 0.605393 5708.69 0.124625
> 27731.3 7.12511e-19
> 1300 1300 1300 0.77733 5652.68 0.155321
> 28289.8 5.07235e-19
> 1400 1400 1400 0.987963 5554.86 0.197656
> 27765.4 3.69152e-19
> 1500 1500 1500 1.30308 5180.03 0.245556
> 27488.7 2.74421e-19
>
>
> your assembly kernel:
>
> $ ./matprod
> # m n k uBLAS: t1 MFLOPS Blocked: t2
> MFLOPS Diff nrm1
> 100 100 100 0.000472525 4232.58 0.000214793
> 9311.29 0
> 200 200 200 0.00468344 3416.29 0.00114834
> 13933.1 0
> 300 300 300 0.0159694 3381.46 0.00284525
> 18979 0
> 400 400 400 0.0329509 3884.57 0.00521382
> 24550.1 0
> 500 500 500 0.0553715 4514.96 0.00857488
> 29154.9 0
> 600 600 600 0.086432 4998.15 0.0136662
> 31610.7 8.52599e-18
> 700 700 700 0.135785 5052.1 0.0206901
> 33155.9 5.81226e-18
> 800 800 800 0.209187 4895.14 0.0302865
> 33810.5 3.67676e-18
> 900 900 900 0.298945 4877.15 0.0421455
> 34594.4 2.34282e-18
> 1000 1000 1000 0.408444 4896.63 0.0571358
> 35004.3 1.53606e-18
> 1100 1100 1100 0.548342 4854.64 0.0797517
> 33378.6 1.0315e-18
> 1200 1200 1200 0.709745 4869.36 0.0965715
> 35787 7.11162e-19
> 1300 1300 1300 0.90209 4870.91 0.126747
> 34667.5 5.07616e-19
> 1400 1400 1400 1.14301 4801.35 0.152455
> 35997.4 3.69018e-19
> 1500 1500 1500 1.4692 4594.34 0.189549
> 35610.9 2.7435e-19
>
> your vectorised kernel:
>
> $ ./matprod
> # m n k uBLAS: t1 MFLOPS Blocked: t2
> MFLOPS Diff nrm1
> 100 100 100 0.000467843 4274.94 0.000234705
> 8521.34 0
> 200 200 200 0.00494453 3235.9 0.00115772
> 13820.3 0
> 300 300 300 0.0171149 3155.14 0.002855
> 18914.2 1.9068e-16
> 400 400 400 0.0347875 3679.48 0.00526618
> 24306 8.31453e-17
> 500 500 500 0.0571516 4374.33 0.00791231
> 31596.3 3.47186e-17
> 600 600 600 0.087803 4920.11 0.0130765
> 33036.4 1.62512e-17
> 700 700 700 0.138488 4953.5 0.0200052
> 34291 8.37735e-18
> 800 800 800 0.211362 4844.77 0.0303868
> 33698.9 4.68247e-18
> 900 900 900 0.303289 4807.29 0.0418178
> 34865.6 2.77558e-18
> 1000 1000 1000 0.414966 4819.67 0.0563484
> 35493.5 1.74855e-18
> 1100 1100 1100 0.548834 4850.29 0.0793732
> 33537.7 1.14261e-18
> 1200 1200 1200 0.70661 4890.96 0.0972836
> 35525 7.73102e-19
> 1300 1300 1300 0.906953 4844.79 0.128163
> 34284.4 5.41372e-19
> 1400 1400 1400 1.14789 4780.95 0.153824
> 35677.1 3.88091e-19
> 1500 1500 1500 1.45357 4643.73 0.190417
> 35448.6 2.84443e-19
>
>
> I guess I should install gcc 5.* and rerun.
>
>
> Could somebody run these tests on non-x86 architectures?
>
> If nobody has anything around, I can try to revive my old arm32
> laptop, but I'm not sure how relevant is that these days.
>
> Cheers,
>
> Imre
>
>
>
>
>
>
> On Saturday, 30 January 2016, 13:49, Michael Lehn
> <michael.lehn_at_[hidden]> wrote:
> Ok, and this version for MR=4, NR=12 even beats the asm kernel on Haswell:
>
> //-- Micro Kernel
> --------------------------------------------------------------
> template <typename Index>
> typename std::enable_if<std::is_convertible<Index, std::int64_t>::value
> && BlockSize<double>::MR==4
> && BlockSize<double>::NR==12
> && BlockSize<double>::align==32,
> void>::type
> ugemm(Index kc, double alpha,
> const double *A, const double *B,
> double beta,
> double *C, Index incRowC, Index incColC)
> {
> static const Index MR = BlockSize<double>::MR;
> static const Index NR = BlockSize<double>::NR;
> typedef double vx __attribute__((vector_size (4*sizeof(double))));
>
> A = (const double*) __builtin_assume_aligned (A, 32);
> B = (const double*) __builtin_assume_aligned (B, 32);
>
> vx P0_03 = {}; vx P0_47 = {}; vx P0_811 = {};
> vx P1_03 = {}; vx P1_47 = {}; vx P1_811 = {};
> vx P2_03 = {}; vx P2_47 = {}; vx P2_811 = {};
> vx P3_03 = {}; vx P3_47 = {}; vx P3_811 = {};
>
> for (Index l=0; l<kc; ++l) {
> const vx *b = (const vx *)B;
>
> P0_03 += A[0]*b[0]; P0_47 += A[0]*b[1]; P0_811 += A[0]*b[2];
> P1_03 += A[1]*b[0]; P1_47 += A[1]*b[1]; P1_811 += A[1]*b[2];
> P2_03 += A[2]*b[0]; P2_47 += A[2]*b[1]; P2_811 += A[2]*b[2];
> P3_03 += A[3]*b[0]; P3_47 += A[3]*b[1]; P3_811 += A[3]*b[2];
> A += MR;
> B += NR;
> }
>
> P0_03 *= alpha; P0_47 *= alpha; P0_811 *= alpha;
> P1_03 *= alpha; P1_47 *= alpha; P1_811 *= alpha;
> P2_03 *= alpha; P2_47 *= alpha; P2_811 *= alpha;
> P3_03 *= alpha; P3_47 *= alpha; P3_811 *= alpha;
>
> if (beta!=double(1)) {
> for (Index i=0; i<MR; ++i) {
> for (Index j=0; j<NR; ++j) {
> C[i*incRowC+j*incColC] *= beta;
> }
> }
> }
>
> const double *p = (const double *) &P0_03;
> C[0*incRowC+0*incColC] += p[0];
> C[0*incRowC+1*incColC] += p[1];
> C[0*incRowC+2*incColC] += p[2];
> C[0*incRowC+3*incColC] += p[3];
>
> p = (const double *) &P0_47;
> C[0*incRowC+4*incColC] += p[0];
> C[0*incRowC+5*incColC] += p[1];
> C[0*incRowC+6*incColC] += p[2];
> C[0*incRowC+7*incColC] += p[3];
>
> p = (const double *) &P0_811;
> C[0*incRowC+8*incColC] += p[0];
> C[0*incRowC+9*incColC] += p[1];
> C[0*incRowC+10*incColC] += p[2];
> C[0*incRowC+11*incColC] += p[3];
>
> p = (const double *) &P1_03;
> C[1*incRowC+0*incColC] += p[0];
> C[1*incRowC+1*incColC] += p[1];
> C[1*incRowC+2*incColC] += p[2];
> C[1*incRowC+3*incColC] += p[3];
>
> p = (const double *) &P1_47;
> C[1*incRowC+4*incColC] += p[0];
> C[1*incRowC+5*incColC] += p[1];
> C[1*incRowC+6*incColC] += p[2];
> C[1*incRowC+7*incColC] += p[3];
>
> p = (const double *) &P1_811;
> C[1*incRowC+8*incColC] += p[0];
> C[1*incRowC+9*incColC] += p[1];
> C[1*incRowC+10*incColC] += p[2];
> C[1*incRowC+11*incColC] += p[3];
>
> p = (const double *) &P2_03;
> C[2*incRowC+0*incColC] += p[0];
> C[2*incRowC+1*incColC] += p[1];
> C[2*incRowC+2*incColC] += p[2];
> C[2*incRowC+3*incColC] += p[3];
>
> p = (const double *) &P2_47;
> C[2*incRowC+4*incColC] += p[0];
> C[2*incRowC+5*incColC] += p[1];
> C[2*incRowC+6*incColC] += p[2];
> C[2*incRowC+7*incColC] += p[3];
>
> p = (const double *) &P2_811;
> C[2*incRowC+8*incColC] += p[0];
> C[2*incRowC+9*incColC] += p[1];
> C[2*incRowC+10*incColC] += p[2];
> C[2*incRowC+11*incColC] += p[3];
>
> p = (const double *) &P3_03;
> C[3*incRowC+0*incColC] += p[0];
> C[3*incRowC+1*incColC] += p[1];
> C[3*incRowC+2*incColC] += p[2];
> C[3*incRowC+3*incColC] += p[3];
>
> p = (const double *) &P3_47;
> C[3*incRowC+4*incColC] += p[0];
> C[3*incRowC+5*incColC] += p[1];
> C[3*incRowC+6*incColC] += p[2];
> C[3*incRowC+7*incColC] += p[3];
>
> p = (const double *) &P3_811;
> C[3*incRowC+8*incColC] += p[0];
> C[3*incRowC+9*incColC] += p[1];
> C[3*incRowC+10*incColC] += p[2];
> C[3*incRowC+11*incColC] += p[3];
>
> }
>
> [lehn_at_node042 session5]$ g++ -mfma -Wall -Ofast -I ../boost_1_60_0/
> -std=c++11 -DHAVE_GCCVEC -DNDEBUG -DBS_D_NR=12 -DBS_D_NC=4092
> matprod.cc
> [lehn_at_node042 session5]$ ./a.out
> # m n k uBLAS: t1 MFLOPS Blocked: t2
> MFLOPS Res
> 100 100 100 0.0010737 1862.72 0.00035514
> 5631.58 0
> 200 200 200 0.00450262 3553.49 0.0015222
> 10511.1 0
> 300 300 300 0.0149401 3614.43 0.00197623
> 27324.7 1.91339e-16
> 400 400 400 0.0306213 4180.1 0.00441727
> 28977.2 8.30501e-17
> 500 500 500 0.0521503 4793.83 0.00755924
> 33072.1 3.47577e-17
> 600 600 600 0.0829816 5205.97 0.0124722
> 34637.1 1.60972e-17
> 700 700 700 0.129317 5304.79 0.0192483
> 35639.4 8.37943e-18
> 800 800 800 0.192606 5316.55 0.0296521
> 34533.8 4.66971e-18
> 900 900 900 0.274752 5306.61 0.0404913
> 36007.7 2.77631e-18
> 1000 1000 1000 0.379118 5275.41 0.054979
> 36377.5 1.74802e-18
> 1100 1100 1100 0.500672 5316.85 0.0742024
> 35874.8 1.141e-18
> 1200 1200 1200 0.646887 5342.51 0.0927714
> 37252.9 7.7394e-19
> 1300 1300 1300 0.823663 5334.7 0.119398
> 36801.2 5.41738e-19
> 1400 1400 1400 1.02766 5340.3 0.147005
> 37332.1 3.88125e-19
> 1500 1500 1500 1.26277 5345.39 0.176592
> 38223.6 2.85178e-19
> 1600 1600 1600 1.59651 5131.18 0.22395
> 36579.7 2.12873e-19
> 1700 1700 1700 2.66806 3682.82 0.259462
> 37870.7 1.62096e-19
> 1800 1800 1800 3.34888 3482.96 0.311059
> 37497.7 1.25272e-19
> 1900 1900 1900 4.09763 3347.79 0.371178
> 36958 9.81479e-20
>
>
>
>
>
> On 30 Jan 2016, at 13:33, Michael Lehn <michael.lehn_at_[hidden]> wrote:
>
>> On my Haswell your code performs very well. For N=M=K it reaches
>> single threaded 32GFLOPS
>> compared to 37GFLOPS with my FMA kernel. So I would say this is close!
>>
>> I did the flowing two things:
>>
>> 1) Compiled with ?-mfma?
>> 2) Used MR=4 NR=8 and the other defaults
>>
>> Here the results:
>>
>> (A) Your code
>>
>> [lehn_at_node042 session5]$ g++ -mfma -Wall -Ofast -I ../boost_1_60_0/
>> -std=c++11 -DHAVE_GCCVEC -DNDEBUG matprod.cc
>> [lehn_at_node042 session5]$ ./a.out
>> # m n k uBLAS: t1 MFLOPS Blocked: t2
>> MFLOPS Res
>> 100 100 100 0.000993595 2012.89 0.000402098
>> 4973.91 0
>> 200 200 200 0.00508121 3148.86 0.0016022
>> 9986.3 0
>> 300 300 300 0.0148755 3630.13 0.00250342
>> 21570.5 1.92273e-16
>> 400 400 400 0.0304433 4204.53 0.00562129
>> 22770.6 8.3039e-17
>> 500 500 500 0.0515749 4847.32 0.00889942
>> 28091.7 3.47892e-17
>> 600 600 600 0.0821662 5257.63 0.0144308
>> 29936 1.6149e-17
>> 700 700 700 0.128187 5351.55 0.0224022
>> 30622 8.38824e-18
>> 800 800 800 0.190724 5369.01 0.0340685
>> 30057.1 4.69155e-18
>> 900 900 900 0.272908 5342.46 0.0474595
>> 30720.9 2.78366e-18
>> 1000 1000 1000 0.375997 5319.19 0.0635467
>> 31472.9 1.75117e-18
>> 1100 1100 1100 0.504189 5279.76 0.0856357
>> 31085.2 1.14159e-18
>> 1200 1200 1200 0.646057 5349.37 0.110654
>> 31232.5 7.74152e-19
>> 1300 1300 1300 0.828805 5301.61 0.137722
>> 31904.8 5.43016e-19
>> 1400 1400 1400 1.0273 5342.18 0.171288
>> 32039.6 3.87778e-19
>> 1500 1500 1500 1.26946 5317.24 0.208467
>> 32379.3 2.85337e-19
>> 1600 1600 1600 1.58969 5153.21 0.264495
>> 30972.3 2.13061e-19
>> 1700 1700 1700 2.67863 3668.3 0.30419
>> 32302.2 1.62006e-19
>> ^C
>>
>> (B) My FMA Kernel
>>
>> [lehn_at_node042 session5]$ g++ -mfma -Wall -Ofast -I ../boost_1_60_0/
>> -std=c++11 -DHAVE_FMA -DNDEBUG matprod.cc
>> [lehn_at_node042 session5]$ ./a.out
>> # m n k uBLAS: t1 MFLOPS Blocked: t2
>> MFLOPS Res
>> 100 100 100 0.0010306 1940.61 0.000378977
>> 5277.37 7.66934
>> 200 200 200 0.0049895 3206.74 0.00101729
>> 15728.1 0.236755
>> 300 300 300 0.0150318 3592.39 0.00294536
>> 18333.9 0
>> 400 400 400 0.0306356 4178.14 0.00495991
>> 25806.9 0.00179347
>> 500 500 500 0.052443 4767.08 0.00784496
>> 31867.6 0.000927111
>> 600 600 600 0.0824758 5237.9 0.0127618
>> 33850.9 8.49298e-18
>> 700 700 700 0.128639 5332.73 0.0192815
>> 35578.1 0.0107258
>> 800 800 800 0.192102 5330.51 0.0294848
>> 34729.7 0.00548943
>> 900 900 900 0.272509 5350.28 0.0407217
>> 35804 2.34506e-18
>> 1000 1000 1000 0.372414 5370.37 0.0557306
>> 35886.9 0.00180032
>> 1100 1100 1100 0.495172 5375.91 0.0759043
>> 35070.5 0.00111911
>> 1200 1200 1200 0.643628 5369.56 0.0936622
>> 36898.5 7.13231e-19
>> 1300 1300 1300 0.831523 5284.28 0.119804
>> 36676.5 0.000485489
>> 1400 1400 1400 1.07165 5121.07 0.148407
>> 36979.5 0.000334856
>> 1500 1500 1500 1.26523 5334.99 0.179347
>> 37636.6 2.74426e-19
>> 1600 1600 1600 1.57777 5192.13 0.224779
>> 36444.7 0.00017181
>> 1700 1700 1700 2.67335 3675.54 0.259066
>> 37928.6 0.000126808
>> 1800 1800 1800 3.35908 3472.38 0.305459
>> 38185.1 1.22716e-19
>> ^C
>>
>> (C) Your code with the parameters you suggested.
>>
>> [lehn_at_node042 session5]$ g++ -mfma -Wall -Ofast -I ../boost_1_60_0/
>> -std=c++11 -DHAVE_GCCVEC -DBS_D_MR=2 -DBS_D_NR=16 -DBS_D_KC=512
>> -DNDEBUG matprod.cc
>> [lehn_at_node042 session5]$ ./a.out
>> # m n k uBLAS: t1 MFLOPS Blocked: t2
>> MFLOPS Res
>> 100 100 100 0.000991205 2017.75 0.000371863
>> 5378.32 0
>> 200 200 200 0.00492544 3248.44 0.000893081
>> 17915.5 0
>> 300 300 300 0.0149147 3620.6 0.00288725
>> 18702.9 0
>> 400 400 400 0.0304561 4202.77 0.00533248
>> 24003.9 0
>> 500 500 500 0.0519717 4810.31 0.0094423
>> 26476.6 0
>> 600 600 600 0.0815292 5298.72 0.0160959
>> 26839.2 8.50004e-18
>> 700 700 700 0.127243 5391.25 0.0241644
>> 28388.9 5.82176e-18
>> 800 800 800 0.190188 5384.16 0.0379423
>> 26988.3 3.67296e-18
>> 900 900 900 0.270269 5394.62 0.0526334
>> 27701.1 2.34234e-18
>> 1000 1000 1000 0.369451 5413.44 0.0710608
>> 28144.9 1.53537e-18
>> 1100 1100 1100 0.491269 5418.62 0.0953915
>> 27906 1.03025e-18
>> 1200 1200 1200 0.638369 5413.8 0.119802
>> 28847.5 7.13779e-19
>> 1300 1300 1300 0.822215 5344.1 0.15489
>> 28368.5 5.07376e-19
>> 1400 1400 1400 1.03643 5295.12 0.191441
>> 28666.8 3.69364e-19
>> 1500 1500 1500 1.26753 5325.32 0.231101
>> 29208 2.74895e-19
>> 1600 1600 1600 1.60965 5089.31 0.289249
>> 28321.6 2.0631e-19
>> 1700 1700 1700 2.67171 3677.79 0.333099
>> 29498.7 1.57713e-19
>> 1800 1800 1800 3.34254 3489.56 0.397267
>> 29360.6 1.2262e-19
>> ^C
>> [lehn_at_node042 session5]$
>>
>>
>>
>> Cheers,
>>
>> Michael
>>
>>
>> On 29 Jan 2016, at 23:31, palik imre <imre_palik_at_[hidden]> wrote:
>>
>>> Here is a more sane kernel using gcc simd vectors:
>>>
>>> template <typename Index, typename T>
>>> typename std::enable_if<std::is_floating_point<T>::value,
>>> void>::type
>>> ugemm(Index kc, T alpha,
>>> const T *A, const T *B,
>>> T beta,
>>> T *C, Index incRowC, Index incColC)
>>> {
>>> static const Index MR = BlockSize<T>::MR;
>>> static const Index NR = BlockSize<T>::NR;
>>> static const unsigned div = 32/sizeof(T);
>>> typedef T vx __attribute__((vector_size (32)));
>>>
>>> vx P[MR*NR/div] __attribute__ ((aligned (128))) = {};
>>> const vx *B_ = (vx *)B;
>>> for (Index l=0; l<kc; ++l) {
>>> for (Index j=0; j<(NR/div); ++j) {
>>> for (Index i=0; i<MR; ++i) {
>>> P[i * NR/div + j] += A[i + l * MR]*B_[l*(NR/div)+j];
>>> }
>>> }
>>> }
>>>
>>> T *P_ = (T *)P;
>>> for (Index i=0; i<MR; ++i) {
>>> for (Index j=0; j<NR; ++j) {
>>> C[i*incRowC+j*incColC] *= beta;
>>> C[i*incRowC+j*incColC] += alpha*P_[i * NR + j];
>>> }
>>> }
>>> }
>>>
>>>
>>> this plugs nicely to Michael's code, and slightly faster than my
>>> previous try (but still nowhere near to Michael's hand crafted
>>> kernel).
>>>
>>> The good blocksize for double is MR=2, NR=16, KC=512
>>> multiply MR and NR by 2 for float.
>>>
>>>
>>> Cheers,
>>>
>>> Imre
>>>
>>
>> _______________________________________________
>> ublas mailing list
>> ublas_at_[hidden]
>> http://lists.boost.org/mailman/listinfo.cgi/ublas
>> Sent to: michael.lehn_at_[hidden]
>
>>
>
>