fPIM: A Holistic Design to Optimize PIM Data Flow for High Execution Efficiency

IF 5.9 2区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

IEEE Transactions on Parallel and Distributed Systems Pub Date : 2026-03-01 Epub Date: 2026-03-03 DOI:10.1109/TPDS.2026.3670145

Nan Wang;Wenjie Liu;Qing Liu;Xubin He

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引用次数: 0

Abstract

As applications demand more bandwidth, the “memory wall” problem becomes increasingly severe. Therefore, the processing-in-memory (PIM) architecture has attracted significant research interest due to its ability to execute instructions offloaded by the processor. Existing works on PIM architectures are classified into two categories: regional offloading, where all instructions within a programmer-specified code region are offloaded, and selective offloading, where only instructions of interest are offloaded via hardware support. However, PIM architectures pose the amplified in-PIM traffic overhead challenge that endangers the performance of PIM and degrades the performance of the entire system. To address the challenge, we propose a PIM architecture, called fast PIM (fPIM), which integrates the PIM cache within each Channel Controller to optimize the data flow within the PIM. This design cooperates with the Processing Unit Load-balancer and Behavior-based Offloader to achieve high execution efficiency. To evaluate fPIM, we perform extensive experiments, and the results show that fPIM reduces the workload finish time by up to 88.6%, 87.5%, and 79.6% (with an average of 68.7%, 66.2%, and 59.8%), compared to three state-of-the-art PIM designs, PEI, Fafnir, and SpaceA, respectively.

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fPIM：优化PIM数据流以提高执行效率的整体设计

随着应用程序对带宽的需求越来越大，“内存墙”问题变得越来越严重。因此，内存中处理（PIM）体系结构由于能够执行由处理器卸载的指令而引起了极大的研究兴趣。PIM体系结构上的现有工作分为两类：区域性卸载，即卸载程序员指定的代码区域内的所有指令；选择性卸载，即通过硬件支持仅卸载感兴趣的指令。然而，PIM架构带来了放大的PIM内部流量开销挑战，危及PIM的性能并降低整个系统的性能。为了应对这一挑战，我们提出了一种PIM架构，称为快速PIM (fPIM)，它将PIM缓存集成到每个通道控制器中，以优化PIM内的数据流。该设计与处理单元负载平衡器和基于行为的卸载器配合使用，实现了较高的执行效率。为了评估fPIM，我们进行了大量的实验，结果表明，与PEI、Fafnir和SpaceA这三种最先进的PIM设计相比，fPIM将工作负载完成时间减少了88.6%、87.5%和79.6%（平均分别为68.7%、66.2%和59.8%）。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Parallel and Distributed Systems 工程技术-工程：电子与电气

CiteScore

11.00

自引率

9.40%

发文量

281

审稿时长

5.6 months

期刊介绍： IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.