A survey of software techniques to emulate heterogeneous memory systems in high-performance computing

IF 2 4区计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS Parallel Computing Pub Date : 2023-07-01 DOI:10.1016/j.parco.2023.103023

Clément Foyer, Brice Goglin, Andrès Rubio Proaño

{"title":"A survey of software techniques to emulate heterogeneous memory systems in high-performance computing","authors":"Clément Foyer, Brice Goglin, Andrès Rubio Proaño","doi":"10.1016/j.parco.2023.103023","DOIUrl":null,"url":null,"abstract":"<div>Heterogeneous memory will be involved in several upcoming platforms on the way to exascale. Combining technologies such as HBM, DRAM and/or NVDIMM allows to tackle the needs of different applications in terms of bandwidth, latency or capacity. And new memory interconnects such as CXL bring easy ways to attach these technologies to the processors.High-performance computing developers must prepare their runtimes and applications for these architectures, even before they are actually available. Hence, we survey software solutions for emulating them. First, we list many ways to modify the performance of platforms so that developers may test their code under different memory performance profiles. This is required to identify kernels and data buffers that are sensitive to memory performance.Then, we present several techniques for exposing fake heterogeneous memory information to the software stack. This is useful for adapting runtimes and applications to heterogeneous memory so that different kinds of memory are detected at runtime and so that buffers are allocated in the appropriate one.</div>","PeriodicalId":54642,"journal":{"name":"Parallel Computing","volume":"116 ","pages":"Article 103023"},"PeriodicalIF":2.0000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Parallel Computing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167819123000297","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}

引用次数: 1

Abstract

Heterogeneous memory will be involved in several upcoming platforms on the way to exascale. Combining technologies such as HBM, DRAM and/or NVDIMM allows to tackle the needs of different applications in terms of bandwidth, latency or capacity. And new memory interconnects such as CXL bring easy ways to attach these technologies to the processors.

High-performance computing developers must prepare their runtimes and applications for these architectures, even before they are actually available. Hence, we survey software solutions for emulating them. First, we list many ways to modify the performance of platforms so that developers may test their code under different memory performance profiles. This is required to identify kernels and data buffers that are sensitive to memory performance.

Then, we present several techniques for exposing fake heterogeneous memory information to the software stack. This is useful for adapting runtimes and applications to heterogeneous memory so that different kinds of memory are detected at runtime and so that buffers are allocated in the appropriate one.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

在高性能计算中模拟异构存储系统的软件技术综述

异构内存将在即将推出的几个平台中进行扩展。结合HBM、DRAM和/或NVDIMM等技术，可以满足不同应用程序在带宽、延迟或容量方面的需求。像CXL这样的新型内存互连提供了将这些技术连接到处理器上的简单方法。高性能计算开发人员必须为这些体系结构准备运行时和应用程序，甚至在它们真正可用之前。因此，我们调查了用于模拟它们的软件解决方案。首先，我们列出了许多修改平台性能的方法，以便开发人员可以在不同的内存性能配置文件下测试他们的代码。这是识别对内存性能敏感的内核和数据缓冲区所必需的。然后，我们提出了几种将伪造的异构内存信息暴露给软件堆栈的技术。这对于使运行时和应用程序适应异构内存非常有用，这样可以在运行时检测到不同类型的内存，并将缓冲区分配到适当的缓冲区中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Parallel Computing 工程技术-计算机：理论方法

CiteScore

3.50

自引率

7.10%

发文量

审稿时长

4.5 months

期刊介绍： Parallel Computing is an international journal presenting the practical use of parallel computer systems, including high performance architecture, system software, programming systems and tools, and applications. Within this context the journal covers all aspects of high-end parallel computing from single homogeneous or heterogenous computing nodes to large-scale multi-node systems. Parallel Computing features original research work and review articles as well as novel or illustrative accounts of application experience with (and techniques for) the use of parallel computers. We also welcome studies reproducing prior publications that either confirm or disprove prior published results. Particular technical areas of interest include, but are not limited to: -System software for parallel computer systems including programming languages (new languages as well as compilation techniques), operating systems (including middleware), and resource management (scheduling and load-balancing). -Enabling software including debuggers, performance tools, and system and numeric libraries. -General hardware (architecture) concepts, new technologies enabling the realization of such new concepts, and details of commercially available systems -Software engineering and productivity as it relates to parallel computing -Applications (including scientific computing, deep learning, machine learning) or tool case studies demonstrating novel ways to achieve parallelism -Performance measurement results on state-of-the-art systems -Approaches to effectively utilize large-scale parallel computing including new algorithms or algorithm analysis with demonstrated relevance to real applications using existing or next generation parallel computer architectures. -Parallel I/O systems both hardware and software -Networking technology for support of high-speed computing demonstrating the impact of high-speed computation on parallel applications

期刊最新文献

Towards resilient and energy efficient scalable Krylov solvers Seesaw: A 4096-bit vector processor for accelerating Kyber based on RISC-V ISA extensions Editorial Board FastPTM: Fast weights loading of pre-trained models for parallel inference service provisioning Distributed consensus-based estimation of the leading eigenvalue of a non-negative irreducible matrix