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Memory Models for Architects and Compiler Writers

Memory Models for Architects and Compiler Writers
架构师和编译器编写者的内存模型
批准号:
9970735
负责人:
Professor Arvind
金额:
$29.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
1999
资助国家:
美国
项目状态:
已结题
起止时间:
1999-07-01 至 2003-09-30

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中文摘要
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英文摘要
A new memory model called Commit-Reconcile \& Fences (CRF) ispresented, and it is shown how other memory models can be described asrestricted versions of it. The CRF memory model has been designed sothat it is both easy for architects to implement, and rich enough thatcompilers for high-level parallel languages can use it as a targetmachine language. This proposal is for investigating 1. theintegration of CRF into modern microprocessors, 2. adaptive protocolsfor implementing CRF in Distributed Shared memory Systems (DSM) and3. efficient translation of high-level multithreaded programmingmodels (e.g. Cilk) into CRF. The impact of this research would be to eliminate a plethora ofimplementation-dependent memory models, and to provide a stableinterface for architects and compiler writers. The development ofmicro-architectures and cache-coherence protocols to support CRF couldhave an immediate impact on industry because these changes can beincorporated in PC's and servers without the loss of softwarecompatibility.Another theme in this proposal is the use of a new formal frameworkbased on Term Rewriting Systems (TRS) for describing computer systems.The biggest appeal of TRS's is its naturalness for designers. The TRSframework is useful in verification of system properties. It is alsopossible to synthesize hardware from TRS's. A unified synthesis andverification framework is likely to have a big impact on currentdesign practices.
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CPA-CPL: A hardware-design inspired methodology for parallel programming
Generating High-Quality Complex Digital Systems from High-Level Specifications
  • 批准号:
    0541164
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $55.0万
  • 财政年份:
    2006
  • 负责人:
    Professor Arvind
  • 依托单位:
Dataflow Computer Architecture
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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