面向异构多核加速器集群的软件支持向量机系统

Jaejin Lee, Jun Lee, Sangmin Seo, Jungwon Kim, Seungkyun Kim, Zehra Sura
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引用次数: 10

摘要

在本文中,我们提出了一个具有显式管理内存层次的异构多核加速器集群的软件共享虚拟内存(SVM)系统。目标集群由单个管理节点和多个计算节点组成。管理节点包含一个通用处理器和较大的主存,每个计算节点包含一个异构多核处理器和较小的主存。这些节点通过互连网络连接,例如千兆以太网。每个计算节点中的异构多核处理器由一个通用处理器元素(GPE)和多个加速处理器元素(ape)组成。GPE运行操作系统,多个ape专用于计算密集型工作负载。GPE通常由深度片上缓存层次结构和硬件缓存一致性支持。另一方面,类人猿有小的显式寻址的本地内存,而不是缓存。这个APE本地内存与主内存不一致。加速器集群中的不同主内存和本地内存单元可以看作是一个显式管理的内存层次结构:全局内存、节点本地内存和APE本地内存。由于以往软件支持向量机的一致性协议不能有效处理这种内存层次结构,我们提出了一种新的一致性协议,称为层次集中释放一致性(HCRC)。我们的软件SVM系统是建立在HCRC和软件管理缓存之上的。我们评估了我们的软件支持向量机系统在32节点异构多核集群(共192个类群)上的有效性和性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
COMIC++: A software SVM system for heterogeneous multicore accelerator clusters
In this paper, we propose a software shared virtual memory (SVM) system for heterogeneous multicore accelerator clusters with explicitly managed memory hierarchies. The target cluster consists of a single manager node and many compute nodes. The manager node contains a generalpurpose processor and larger main memory, and each compute node contains a heterogeneous multicore processor and smaller main memory. These nodes are connected with an interconnection network, such as Gigabit Ethernet. The heterogeneous multicore processor in each compute node consists of a general-purpose processor element (GPE) and multiple accelerator processor elements (APEs). The GPE runs an OS and the multiple APEs are dedicated to compute-intensive workloads. The GPE is typically backed by a deep on-chip cache hierarchy and hardware cache coherence. On the other hand, the APEs have small explicitly-addressed local memory instead of caches. This APE local memory is not coherent with the main memory. Different main and local memory units in the accelerator cluster can be viewed as an explicitly managed memory hierarchy: global memory, node local memory, and APE local memory. Since coherence protocols of previous software SVM proposals cannot effectively handle such a memory hierarchy, we propose a new coherence and consistency protocol, called hierarchical centralized release consistency (HCRC). Our software SVM system is built on top of HCRC and software-managed caches. We evaluate the effectiveness and analyze the performance of our software SVM system on a 32-node heterogeneous multicore cluster (a total of 192 APEs).
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