Application performance characterization and analysis on Blue Gene/Q

Abstract

This article consists of a collection of slides from the author's conference presentation. The author concludes that The Blue Gene/Q design, low-power simple cores, four hardware threads per core, resu lts in high instruction throughput, and thus exceptional power efficiency for applications. Can effectively fill in pipeline stalls and hide latencies in the memory subsystem. The consequence is low performance per thread, so a high degree of parallelization is required for high application performance. Traditional programming methods (MPI, OpenMP, Pthreads) hold up at very large scales. Memory costs can limit scaling when there are data-structures with size linear in the number of processes, threading helps by keeping the number of processes manageable. Detailed performance analysis is viable at > 10^6 processes but requires care. On-the-fly performance data reduction has merits.