探索新兴混合存储器超级计算机的应用性能
Exploring Application Performance on Emerging Hybrid-Memory Supercomputers
摘要：
下一代超级计算机将具有更多的层次和异构内存系统，不同的内存技术并行工作。一个关键的问题是，在大规模现有的HPC应用程序和新兴的数据分析工作负载是否会在这些系统上提高或降低性能。我们提出了一个系统和公平的方法来确定新兴的混合存储器系统的应用程序性能的趋势。我们将下一代超级计算机的存储系统建模为“快”和“慢”存储器的组合。然后，我们分析了各种工作负载的性能和动态执行特性，从传统的科学应用到新兴的数据分析，以比较传统和混合内存系统。我们的研究结果表明，数据分析应用程序可以明显受益于新的系统设计，特别是在大规模。此外，混合存储器系统不会惩罚传统的科学应用程序，这也可能显示出性能改进。
英文摘要：
Next-generation supercomputers will feature more hierarchical and heterogeneous memory systems with different memory technologies working side-by-side. A critical question is whether at large scale existing HPC applications and emerging data-analytics workloads will have performance improvement or degradation on these systems. We propose a systematic and fair methodology to identify the trend of application performance on emerging hybrid-memory systems. We model the memory system of next-generation supercomputers as a combination of "fast" and "slow" memories. We then analyze performance and dynamic execution characteristics of a variety of workloads, from traditional scientific applications to emerging data analytics to compare traditional and hybrid-memory systems. Our results show that data analytics applications can clearly benefit from the new system design, especially at large scale. Moreover, hybrid-memory systems do not penalize traditional scientific applications, which may also show performance improvement.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
