learning about high performance matmul on cpu. the fastest kernels i've implemented
are blis and blis_12x8 in blis.h
best blis configs:
blis<N, 128, 64, 1024> // for N = 1024
blis_12x8<N, 96, 48, 960> // for N = 1920
to benchmark all (half-decent) kernels, make bench && ./bench. only the blis
kernels are multi threaded. remove -fopenmp from clags to limit to single thread.
compare against numpy with python test-numpy.py. change value of N as required.
initializing b (in c = ab) makes the baseline matmul go from 40 gflops to 15 gflops
on my computer. this does not happen on other people's computers. see the output of
baseline.cpp
cpu details:
Model name: AMD Ryzen 5 PRO 4650U with Radeon Graphics
Thread(s) per core: 2
Core(s) per socket: 6
Caches (sum of all):
L1d: 192 KiB (6 instances)
L1i: 192 KiB (6 instances)
L2: 3 MiB (6 instances)
L3: 8 MiB (2 instances)
i have randomly initialized b in the benchmarks otherwise the blis kernels are too fast to accurately judge their performance.
N = 1024
baseline: 17.8267 GFLOPS/s
layered: 40.7594 GFLOPS/s
layered2: 40.2947 GFLOPS/s
blis: 148.928 GFLOPS/s
N = 1920
baseline: 7.56156 GFLOPS/s
blis_12x8: 254.638 GFLOPS/s
gpu bench: (N = 2048)
baseline_cuda: 170.983 GFLOPS/s
gmem_coalesced: 1315.48 GFLOPS/s
smem_blocked: 1607.82 GFLOPS/s
smem_blocked2: 1649.84 GFLOPS/s
thread_blocked: 5399.37 GFLOPS/s
thread_blocked2: 3765.12 GFLOPS/s
goal: 200 gflops destroyed
150 gflops on N = 1024. numpy gets 210. \ 250 gflops on N = 1920. numpy gets 280.
currently the blis 12x8 kernel requires N to be divisible by 12, so i can't use it with N = 1024. if i figure out how to handle N not divisible by 12, i should be able to get a big boost on N = 1024. todo for now.