The MPICH library is one of the most popular implementations of MPI.[i] Primarily developed at Argonne National Laboratory (ANL) with contributions from external collaborators, MPICH has adhered to the idea of delivering a high-performance MPI library by working closely with vendors in which the MPICH software provides the link between the MPI interface used by applications programmers and vendors who provide low-level hardware acceleration for their network devices. Yanfei Guo (Figure 1), the principal investigator (PI) of the Exascale MPI project in the Exascale Computing Project (ECP) and assistant computer scientist at ANL, is following this tradition. According to Guo, “The ECP MPICH team is working closely with vendors to add general optimizations—optimizations that will work in all situations—to speed MPICH and leverage the capabilities of accelerators, such as GPUs.”
Superior Performance Commits Kyoto University to CPUs Over GPUs
July 25, 2016 by
In this special guest feature, Rob Farber writes that a study done by Kyoto University Graduate School of Medicine shows that code modernization can help Intel Xeon processors outperform GPUs on machine learning code. “The Kyoto results demonstrate that modern multicore processing technology now matches or exceeds GPU machine-learning performance, but equivalently optimized software is required to perform a fair benchmark comparison. For historical reasons, many software packages like Theano lacked optimized multicore code as all the open source effort had been put into optimizing the GPU code paths.”
