In this bimonthly feature, HPCwire highlights newly published research in the high-performance computing community and related domains
"Optimizing irregular-shaped matrix-matrix multiplication on GPUs
Linear algebra is a common application in big data and computational science on HPC. These operations are well-optimized when handling regularly shaped matrix inputs with GPUs, but comparatively little literature has discussed irregularly shaped matrix inputs with GPUs. In this paper (written by a team from the University of Alabama, Oak Ridge National Laboratory, the University of California, Riverside, and the University of Sydney), the authors propose two matrix-matrix multiplication algorithms for irregularly shaped inputs on GPUs. They demonstrate a speedup of up to 3.5x along with greater efficiencies in resource usage..."