在性能轨迹:量化丰富的3d堆叠缓存对高性能计算工作负载的影响

IF 1.5 3区 计算机科学 Q4 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE ACM Transactions on Architecture and Code Optimization Pub Date : 2023-10-25 DOI:10.1145/3629520
Jens Domke, Emil Vatai, Balazs Gerofi, Yuetsu Kodama, Mohamed Wahib, Artur Podobas, Sparsh Mittal, Miquel Pericàs, Lingqi Zhang, Peng Chen, Aleksandr Drozd, Satoshi Matsuoka
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引用次数: 0

摘要

在过去的三十年中,内存子系统的创新主要是为了克服数据移动瓶颈。在本文中,我们专注于存储技术的特定市场趋势:3d堆叠内存和缓存。我们研究了扩展片上存储能力对未来以高性能计算为重点的处理器的影响,特别是通过3d堆叠SRAM。首先,我们提出了一种与内存子系统无关的方法来衡量消除数据移动成本后性能改进的上限。然后,使用gem5模拟器,我们模拟了假设的大缓存处理器(LARC)的两种变体,该处理器由1.5 nm制造,并富含高容量3d堆叠缓存。通过大量涉及代理应用程序和基准测试的实验,我们的目标是揭示HPC CPU性能将如何发展,并得出结论,在每个芯片的基础上,对缓存敏感的HPC应用程序的平均提升为9.56倍。此外,我们详尽地记录了我们的方法探索,以激励HPC中心通过增强的协同设计来推动他们自己的技术议程。
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At the Locus of Performance: Quantifying the Effects of Copious 3D-Stacked Cache on HPC Workloads
Over the last three decades, innovations in the memory subsystem were primarily targeted at overcoming the data movement bottleneck. In this paper, we focus on a specific market trend in memory technology: 3D-stacked memory and caches. We investigate the impact of extending the on-chip memory capabilities in future HPC-focused processors, particularly by 3D-stacked SRAM. First, we propose a method oblivious to the memory subsystem to gauge the upper-bound in performance improvements when data movement costs are eliminated. Then, using the gem5 simulator, we model two variants of a hypothetical LARge Cache processor (LARC), fabricated in 1.5 nm and enriched with high-capacity 3D-stacked cache. With a volume of experiments involving a broad set of proxy-applications and benchmarks, we aim to reveal how HPC CPU performance will evolve, and conclude an average boost of 9.56x for cache-sensitive HPC applications, on a per-chip basis. Additionally, we exhaustively document our methodological exploration to motivate HPC centers to drive their own technological agenda through enhanced co-design.
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来源期刊
ACM Transactions on Architecture and Code Optimization
ACM Transactions on Architecture and Code Optimization 工程技术-计算机:理论方法
CiteScore
3.60
自引率
6.20%
发文量
78
审稿时长
6-12 weeks
期刊介绍: ACM Transactions on Architecture and Code Optimization (TACO) focuses on hardware, software, and system research spanning the fields of computer architecture and code optimization. Articles that appear in TACO will either present new techniques and concepts or report on experiences and experiments with actual systems. Insights useful to architects, hardware or software developers, designers, builders, and users will be emphasized.
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