In recent years, graph-processing has become an essential class of workloads with applications in a rapidly growing number of fields. Graph-processing typically uses large input sets, often in multi-gigabyte scale, and data-dependent graph traversal methods exhibiting irregular memory access patterns. Recent work demonstrates that, due to the highly irregular memory access patterns of data-dependent graph traversals, state-of-the-art graph-processing workloads spend up to 80 % of the total execution time waiting for memory accesses to be served by the DRAM. The vast disparity between the Last Level Cache (LLC) and main memory latencies is a problem that has been addressed for years in computer architecture. One of the prevailing approaches when it comes to mitigating this performance gap between modern CPUs and DRAM is cache replacement policies. In this work, we characterize the challenges drawn by graph-processing workloads and evaluate the most relevant cache replacement policies.

翻译：近年来，图处理已成为一类重要的工作负载，在快速增长的领域中得到广泛应用。图处理通常使用大型输入数据集（规模常达数GB级），并采用数据依赖性图遍历方法，表现出不规则的内存访问模式。近期研究表明，由于数据依赖性图遍历的高度不规则内存访问模式，最先进的图处理工作负载在执行过程中，高达80%的执行时间用于等待内存访问从DRAM中完成服务。末级缓存与主存延迟之间的巨大差距，是计算机体系结构领域长期致力于解决的问题。缩小现代CPU与DRAM之间这一性能差距的主流方法之一便是缓存替换策略。本研究刻画了图处理工作负载所带来的挑战，并评估了最具相关性的缓存替换策略。