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CAREER: Scalable Compiler Optimizations for New Software

CAREER: Scalable Compiler Optimizations for New Software
职业:新软件的可扩展编译器优化
批准号:
0243657
负责人:
Rastislav Bodik
金额:
$24.15万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2002
资助国家:
美国
项目状态:
已结题
起止时间:
2002-08-01 至 2007-05-31

项目摘要

项目成果

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中文摘要
翻译
该研究项目的目标是为新兴的软件应用程序开发编译器优化技术。与目前的实践状态相比,新的软件应用程序将以类型安全语言(例如Java)编写,将包含数百万行代码,通常将由分布在多台机器上的组件构建,并将在具有多个控制线程的处理器上运行,但需要数百个周期来访问主存储器。现代软件将需要现代优化,这些优化需要以三种方式进行扩展:程序的大小、分布式组件的数量以及不断增长的内存延迟。这项研究建议通过利用全新的推测、分析、需求驱动分析、运行时检查和硬件支持的形式来实现可伸缩性。为了开发这些技术,将进行三项具体工作:消除数组边界检查、需求驱动的指针分析和基于指针的数据结构的预取。
英文摘要
The goal of this research program is to develop compiler optimization technology for emerging software applications. Compared to the current state-of-the-practice, the new software applications will be written in type-safe languages (e.g., Java), will contain millions of lines of code, will often be built from components distributed across multiple machines, and will run on processors that will have multiple threads of control, but will take hundreds of cycles to access the main memory. Modern software will require modern optimizations that will need to be scalable in three ways: with the size of the program, with the number of distributed components, and with the growing memory latency. This research proposes to achieve scalability by exploiting radically novel forms of speculation, profiling, demand-driven analysis, run-time checks, and hardware support. To develop these technologies, three specific efforts will be undertaken: elimination of array-bounds checks, demand-driven pointer analysis, and prefetching for pointer-based data structures.
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Collaborative Research: FMitF: Track I: End-usser Programming for CAD Systems via Language Design and Synthesis
  • 批准号:
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  • 项目类别:
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  • 资助金额:
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  • 财政年份:
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  • 负责人:
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  • 依托单位:
FMitF: Track I: End-User Programming with Synthesis-Guided Interaction Models
  • 批准号:
    2122950
  • 项目类别:
    Standard Grant
  • 资助金额:
    $74.97万
  • 财政年份:
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  • 依托单位:
RAPID: Collecting Reliable COVID-19 Datasets in Crisis Conditions
  • 批准号:
    2029457
  • 项目类别:
    Standard Grant
  • 资助金额:
    $7.0万
  • 财政年份:
    2020
  • 负责人:
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  • 依托单位:
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  • 批准号:
    1918027
  • 项目类别:
    Standard Grant
  • 资助金额:
    $9.89万
  • 财政年份:
    2019
  • 负责人:
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  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis