BinomialGPU
This package exports two functions rand_binomial and rand_binomial! that produce CuArrays with binomially distributed elements, analogous to CUDA.rand_poisson and CUDA.rand_poisson! for Poisson-distributed ones.
The sampling occurs natively on the GPU and is implemented using custom GPU kernels.
The performance of this implementation seems to be very competitive with other libraries. Sampling a 1024x1024 matrix on an RTX2070 GPU: BinomialGPU.jl 0.8ms, PyTorch 11ms, CuPy 18ms, tensorflow 400ms. Benchmarking results for other samplers are very welcome; please open an issue if you find one, especially if is faster than this package.
Installation
In a Julia 1.6 or 1.7 REPL, type ] to use the built-in package manager and then run:
pkg> add BinomialGPUUsage
Sample CuArrays with binomial random variates of various dimensions:
julia> using BinomialGPU
julia> rand_binomial(3, count = 10, prob = 0.5)
3-element CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}:
4
3
7
julia> rand_binomial(4, 4, count = 10, prob = 0.5)
4×4 CuArray{Int64, 2, CUDA.Mem.DeviceBuffer}:
5 5 6 4
5 7 6 7
6 4 4 6
7 2 4 5The function also supports arrays of parameters of suitable (compatible) sizes:
julia> counts = [5, 10, 20]
julia> probs = [0.3, 0.4, 0.8]
julia> rand_binomial(count = counts, prob = probs)
3-element CuArray{Int64, 1, CUDA.Mem.DeviceBuffer}:
0
7
19
julia> probs = CUDA.rand(3, 2);
julia> rand_binomial(count = counts, prob = probs)
3×2 CuArray{Int64, 2, CUDA.Mem.DeviceBuffer}:
3 1
4 0
3 18The function with exclamation mark samples random numbers in-place:
julia> using CUDA
julia> A = CUDA.zeros(Int, 4, 4);
julia> rand_binomial!(A, count = 10, prob = 0.5)
4×4 CuArray{Int64, 2, CUDA.Mem.DeviceBuffer}:
6 4 1 8
4 6 6 6
4 3 2 4
5 7 3 5This also allows for non-standard types to be preserved:
julia> A = CUDA.zeros(UInt16, 4, 4);
julia> rand_binomial!(A, count = 10, prob = 0.5)
4×4 CuArray{UInt16, 2, CUDA.Mem.DeviceBuffer}:
0x0005 0x0004 0x0003 0x0005
0x0006 0x0006 0x0006 0x0003
0x0006 0x0005 0x0006 0x0005
0x0007 0x0005 0x0006 0x0006Alternatively, pass the desired type as the first argument:
julia> rand_binomial(UInt32, 4, 4, count = 10, prob = 0.5)
4×4 CuArray{UInt32, 2, CUDA.Mem.DeviceBuffer}:
0x00000004 0x00000005 0x00000008 0x00000005
0x00000003 0x00000007 0x00000005 0x00000005
0x00000007 0x00000005 0x00000005 0x00000004
0x00000001 0x00000005 0x00000005 0x00000003