Computing QR decomposition many times in parallel

Specific Domains GPU
1

I’m new to GPU programming and don’t understand many details. Let’s say I have 2 matrices

A1 = CUDA.rand(1000,1000)
A2 = CUDA.rand(1000,1000)

and I would like to compute their QR decompositions on GPU in parallel (using CUDA.qr). For example, the code below

Q1,R1 = CUDA.qr(A1)
Q2,R2 = CUDA.qr(A2)

does it sequentially. Is there an easy way to do this in parallel?

2

Unlike matrix multiplication, matrix factorizations aren’t easily parallelizable using GPU hardware, and CUDA.qr already exploits device parallelism. For a large number of small input matrices, you may see some benefit by moving to a batched factorization (available in CUDA.jl as CUBLAS.geqrf_batched), but that won’t yield any gains when you only have two large input arrays.

3

Thanks, batched factorization for 100 of matrices of size 1000x1000 is faster than doing it sequentially using for loop.