rand

README

rand

This example tests the slrand random number generation functions. The go test in slrand itself tests the Go version of the code against known values, and this example tests the GPU WGSL versions against the Go versions.

The output shows the first 5 sets of random numbers, with the CPU on the first line and the GPU on the second line. If there are any * on any of the lines, then there is a difference between the two, and an error message will be reported at the bottom.

The total time to generate 1 million random numbers is shown at the end, comparing time on the CPU vs. GPU. On a Mac Book Pro M3 Max laptop, the CPU took roughly 95 times longer to generate the random numbers than the GPU (and with 10 million, 140 times).

Building

There is a //go:generate comment directive in main.go that calls the local build of gosl on the relevant files, so you can do go generate followed by go build to run it. You must do go build in main gosl dir before this will work.

The generated files go into the shaders/ subdirectory.

Ignore the type alignment checking errors about Uint2 and Vector2 not being an even multiple of 16 bytes -- we have put in the necessary padding.

Results

Running on GPU: Apple M3 Max
Group: 0 Group0
    Role: Storage
        Var: 0:	Counter	Struct	(size: 8)	Values: 1
        Var: 1:	Data	Struct[0xF4240]	(size: 64)	Values: 1

Index	Dif(Ex,Tol)	   CPU   	  then GPU
0	   	U: ff1dae59	6cd10df2	F: 0.86274576	0.37199184	F11: 0.018057441	0.24895307	G: 0.7749991	0.05054265
		U: ff1dae59	6cd10df2	F: 0.86274576	0.37199184	F11: 0.018057441	0.24895307	G: 0.7749991	0.050542645
1	   	U: 936f52f3	5daa6164	F: 0.87315667	0.9577325	F11: 0.7201687	0.93292534	G: 0.82841516	2.2875004
		U: 936f52f3	5daa6164	F: 0.87315667	0.9577325	F11: 0.7201687	0.93292534	G: 0.82841516	2.2875004
2	   	U: 1a9351a6	5109a5a6	F: 0.7390654	0.13888454	F11: 0.87044024	0.66876566	G: -1.4226444	0.024453443
		U: 1a9351a6	5109a5a6	F: 0.7390654	0.13888454	F11: 0.87044024	0.66876566	G: -1.4226445	0.024453443
3	   	U: 19b1b6d2	310630c9	F: 0.8936864	0.29176176	F11: 0.5634876	0.43976986	G: 0.07859113	0.07448565
		U: 19b1b6d2	310630c9	F: 0.8936864	0.29176176	F11: 0.5634876	0.43976986	G: 0.07859113	0.07448566
4	   	U: 41556b7f	eeb8e52c	F: 0.46174365	0.28119206	F11: 0.00079108716	0.87918425	G: 0.2611614	-1.3341242
		U: 41556b7f	eeb8e52c	F: 0.46174365	0.28119206	F11: 0.00079108716	0.87918425	G: 0.26116177	-1.3341241
5	   	U: c034b0a6	7188ed5e	F: 0.9694913	0.6442756	F11: 0.071927555	0.5161969	G: 0.6914865	0.32279235
		U: c034b0a6	7188ed5e	F: 0.9694913	0.6442756	F11: 0.071927555	0.5161969	G: 0.6914865	0.32279235

N: 1000000	 CPU: 104.698542ms	 GPU: 1.095417ms	 Full: 27.570417ms	 CPU/GPU:  95.58