.png){kind=icon}
返回
SHMEM+: A Multilevel-PGAS Programming Model for Reconfigurable Supercomputing
.png){kind=icon}
PRE
AI
DOI：10.1145/2000832.2000838.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Reconfigurable Computing (RC) systems based on FPGAs are becoming an increasingly attractive solution to building parallel systems of the future. Applications targeting such systems have demonstrated superior performance and reduced energy consumption versus their traditional counterparts based on microprocessors. However, most of such work has been limited to small system sizes. Unlike traditional HPC systems, lack of integrated, system-wide, parallel-programming models and languages presents a significant design challenge for creating applications targeting scalable, reconfigurable HPC systems. In this article, we extend the traditional Partitioned Global Address Space (PGAS) model to provide a multilevel integration of memory, which simplifies development of parallel applications for such systems and improves developer productivity. The new multilevel-PGAS programming model captures the unique characteristics of reconfigurable HPC systems, such as the existence of multiple levels of memory hierarchy and heterogeneous computation resources. Based on this model, we extend and adapt the SHMEM communication library to become what we call SHMEM+, the first known SHMEM library enabling coordination between FPGAs and CPUs in a reconfigurable, heterogeneous HPC system. Applications designed with SHMEM+ yield improved developer productivity compared to current methods of multidevice RC design and exhibit a high degree of portability. In addition, our design of SHMEM+ library itself is portable and provides peak communication bandwidth comparable to vendor-proprietary versions of SHMEM. Application case studies are presented to illustrate the advantages of SHMEM+.
Keyword:
Design
Languages
Performance
Reconfigurable computing
parallel programming
programming language
programming model
productivity
portability
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.8
论文数:
599
被引数:
810
机构
引用论文
Factors Affecting Rapid Fluid Resuscitation with Large-bore Introducer Catheters影响大口径引导导管快速液体复苏的因素
.png){kind=icon}
PREAI
分享收藏Face detection technique of Humanoid Robot NAO for application in robotic assistive therapyNAO人形机器人的面部检测技术及其在机器人辅助治疗中的应用
OAAI
OAAI分享收藏
分享收藏分享收藏 没有更多内容
没有更多内容