Auto-tunable GPU BLAS
In this paper, we present our implementation of an Auto tuning system, written in C++, which incorporate the use of OpenCL kernels. We deploy this approach on different GPU architectures, evaluating the performance of the approach. Our main focus is to easily generate tuned code, that would otherwise require a large amount of empirical testing, and then run it on any kind of device. This is achieved through the auto tuning framework, which will create different kernels, compile and run them on the device and output the best performing kernel on the given platform.
BLAS is much used in performance critical applications, and is a good candidate for execution on GPUs due to its potential performance increase. Our implementation was benchmarked on various of test environments, with different GPUs, where we achieved comparable results to the ViennaCL library. We also tested against the native vendor specific BLAS libraries from AMD and NVIDIA.
Place, publisher, year, edition, pages
Institutt for datateknikk og informasjonsvitenskap , 2012. , 63 p.
ntnudaim:5976, MIT informatikk, Komplekse datasystemer
IdentifiersURN: urn:nbn:no:ntnu:diva-18411Local ID: ntnudaim:5976OAI: oai:DiVA.org:ntnu-18411DiVA: diva2:565905
Elster, Anne Cathrine, Førsteamanuensis