Boosting the Efficiency of HPCG and Graph500 with Near-Data Processing

Abstract

HPCG and Graph500 can be regarded as the two most relevant benchmarks for high-performance computing systems. Existing supercomputer designs, however, tend to focus on floating-point peak performance, a metric less relevant for these two benchmarks, leaving resources underutilized, and resulting in little performance improvements, for these benchmarks, over time. In this work, we analyze the implementation of both benchmarks on a novel shared-memory near-data processing architecture. We study a number of aspects: 1. a system parameter design exploration, 2. software optimizations, and 3. the exploitation of unique architectural features like user-enhanced coherence as well as the exploitation of data-locality for inter near-data processor traffic.For the HPCG benchmark, we show a factor 2.5x application level speedup with respect to a CPU, and a factor 2.5x power-efficiency improvement with respect to a GPU. For the Graph500 benchmark, we show up to a factor 3.5x speedup with respect to a CPU. Furthermore, we show that, with many of the existing data-locality optimizations for this specific graph workload applied, local memory bandwidth is not the crucial parameter, and a high-bandwidth as well as low-latency interconnect are arguably more important, shining a new light on the near-data processing characteristics most relevant for this type of heavily optimized graph processing.