课题基金 / 基金详情

I-Corps: Mistos-Enabling Write-once Run-everywhere High Performance Software

I-Corps: Mistos-Enabling Write-once Run-everywhere High Performance Software
I-Corps:Mistos 支持一次写入、到处运行的高性能软件
批准号:
1462365
负责人:
Scott Mahlke
金额:
$5.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-12-01 至 2015-05-31

项目摘要

项目成果

Scott Mahlke的其他基金

相关文献

中文摘要
翻译
将传统处理器与强大的图形处理单元(gpu)相结合的计算机系统已经成为所有平台的标准,从云服务器到笔记本电脑和平板电脑,甚至手机。gpu通过其独特的设计可以产生高达300倍的性能或节能。然而,开发利用这些系统的应用程序是一项艰巨的挑战,需要专门的硬件专业知识和繁琐的性能优化,这使得大部分软件开发人员无法使用这些系统。为了克服这一挑战,该项目将探索为异构计算机系统透明地生成高性能软件的源到源优化系统的商业化机会。这个I-CORPS团队开发了一个名为Mistos的自动并行化工具,可以帮助程序员为这些高性能系统编写代码。使用Mistos,程序员只需要关注功能,而不是硬件细节和性能调优。Mistos将使各种计算平台的用户,包括移动应用程序开发人员、云服务和科学界的成员,能够以无缝和高效的方式利用异构GPU系统的计算能力。
英文摘要
Computer systems that combine traditional processors with powerful graphics processing units (GPUs) have become the standard in all platforms ranging from cloud servers, to laptops and tablets, and even cell phones. GPUs can yield performance or energy savings of up to 300x through their unique design. However, developing applications to exploit these systems is a difficult challenge, requiring specialized hardware expertise and tedious performance optimizations that make them unusable by a large fraction of software developers.To overcome this challenge, this project will explore the commercialization opportunities for a source-to-source optimization system that transparently generates high performance software for heterogeneous computer systems. This I-CORPS team has developed an automatic parallelization tool called Mistos that can help programmers write code for these high performance systems. With Mistos, programmers need to only focus on functionality, rather than hardware details and performance tuning. Mistos will enable users of all kinds of computing platforms, including mobile application developers, cloud services, and members of the scientific community, the ability to harness the computing power of heterogeneous GPU systems in a seamless and efficient manner.
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XPS: FULL: CCA: Scalable Approximate Computing for Data Parallel Applications
SHF: Small: Scaling the Compute Efficiency of General-Purpose Processors
CSR: Medium: Collaborative Research: Scaling the Implicitly Parallel Programming Model with Lifelong Thread Extraction and Dynamic Adaptation
SHF: Small: An Adaptive Architecture Fabric for Constructing Resilient Multicore Systems