I’ve been away from Julia for several months, and have been rerunning GPU code from this past summer on a new installation of Jula 1.5.3 and CUDA.jl (previously I used the separate CUDA packages). My subset sum program now runs much slower (3 1/3 sec compared to 159 ms): julia> s = rand(1:10000000,…

OK, that looks like a bug then, please file an issue (with a fully reproducible example, etc). You could always try to reduce some more or profile individual operations to see where the issue is.

Performance regression with GPUArrays subset sum

Specific Domains GPU

maleadt November 25, 2020, 9:07pm 2

Did you disable scalar iteration? Workflow · CUDA.jl

1 Like

Topic		Replies	Views
InvalidIRError when running AcceleratedKernels.sum on a GPU SubArray (CuArray view) GPU cuda , arrays , views , kernelabstractions	2	51	November 20, 2025
Dot-product of CuArray views is slow GPU performance , memory-allocation , views	10	1576	May 11, 2021
Reduce memory allocated in array view and in place sum Performance question	12	796	November 10, 2023
How to avoid memory allocation while doing sum on a GPU? General Usage cuda , memory-allocation , cudajl	7	219	April 20, 2025
Sum is very slow (and I can't figure out why) GPU	4	982	January 4, 2021

Performance regression with GPUArrays subset sum

Related topics