CAREER: Holistic System Solutions for Empowering Parallel Programmers

职业:为并行程序员提供全面的系统解决方案

基本信息

项目摘要

The central vision of this project is to bring parallel programming, which continues to be a daunting task to this day even for experts, to mainstream programmers. The fundamental challenge is that the concurrency semantics defined by memory models of popular parallel languages are too weak. This project is investigating solutions that span from processors to languages to enable stronger memory consistency models starting with sequential consistency, but goes far further in achieving much stronger concurrency semantics such as determinism at the language-level. The end goal is to find solutions that provide the best of both worlds: sequential semantics and parallel performance. A set of correctness and performance enhancing tools that exploit the stronger concurrency semantics are also being developed.Advances in computing have relied on the exponential growth in processor performance. Unfortunately, unlike in the past, successive generations of processors can no longer improve sequential performance because of technology constraints. The burden has shifted to software developers who must now evolve to take advantage of parallelism in multi-core processors. Unfortunately, parallel programming continues to be a daunting task to this day even for experts. This project is developing solutions that can bring parallel programming to mainstream programmers by dramatically simplifying the process of developing, debugging, and maintaining parallel software. Programmer productivity tools developed as part of this research will be directly beneficial to students, software developers and researchers. By involving colleagues in industry, this project could serve as a catalyst to bring the software and processor industry together to solve a problem that is common to both: enabling mainstream programmers to take advantage of multi-core architectures.
这个项目的中心愿景是将并行编程带给主流程序员,并行编程直到今天仍然是一项艰巨的任务,即使对于专家来说也是如此。最根本的挑战是流行的并行语言的内存模型定义的并发语义太弱。该项目正在研究从处理器到语言的解决方案,以实现更强大的内存一致性模型,从顺序一致性开始,但在实现更强大的并发语义(如语言级别的确定性)方面走得更远。最终的目标是找到一个能提供两个世界最好的解决方案:顺序语义和并行性能。一组利用更强的并发语义的正确性和性能增强工具也正在开发中。计算的进步依赖于处理器性能的指数增长。不幸的是,与过去不同的是,由于技术限制,连续几代处理器不再能提高顺序性能。负担已经转移到软件开发人员身上,他们现在必须发展以利用多核处理器中的并行性。不幸的是,并行编程直到今天仍然是一项艰巨的任务,即使对专家来说也是如此。这个项目正在开发解决方案,通过极大地简化开发、调试和维护并行软件的过程,为主流程序员带来并行编程。 作为这项研究的一部分开发的程序员生产力工具将直接有益于学生,软件开发人员和研究人员。通过让业界的同事参与进来,这个项目可以作为一种催化剂,将软件和处理器行业结合在一起,解决一个共同的问题:使主流程序员能够利用多核架构。

项目成果

期刊论文数量(0)
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Satish Narayanasamy其他文献

mm2-gb: GPU Accelerated Minimap2 for Long Read DNA Mapping
mm2-gb:用于长读 DNA 映射的 GPU 加速 Minimap2
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Juechu Dong;Xueshen Liu;Harisankar Sadasivan;Sriranjani Sitaraman;Satish Narayanasamy
  • 通讯作者:
    Satish Narayanasamy
SECRET-GWAS: Confidential Computing for Population-Scale GWAS
SECRET-GWAS:人口规模 GWAS 的机密计算
  • DOI:
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Jonah Rosenblum;Juechu Dong;Satish Narayanasamy
  • 通讯作者:
    Satish Narayanasamy

Satish Narayanasamy的其他文献

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{{ truncateString('Satish Narayanasamy', 18)}}的其他基金

SHF: Medium: Custom Computing for Genome Sequencing
SHF:中:基因组测序的定制计算
  • 批准号:
    2403119
  • 财政年份:
    2024
  • 资助金额:
    $ 53.87万
  • 项目类别:
    Standard Grant
SHF: Medium: Optimistic Static Analysis
SHF:中:乐观静态分析
  • 批准号:
    1703931
  • 财政年份:
    2017
  • 资助金额:
    $ 53.87万
  • 项目类别:
    Standard Grant
SHF: Small: Accelerating Asynchronous Programs through Synergistic Hardware/Software Customization
SHF:小型:通过协同硬件/软件定制加速异步程序
  • 批准号:
    1527301
  • 财政年份:
    2015
  • 资助金额:
    $ 53.87万
  • 项目类别:
    Standard Grant
SHF: Small: Interleaving Constrained Parallel Runtime System for Tolerating Concurrency Bugs
SHF:小型:用于容忍并发错误的交错约束并行运行时系统
  • 批准号:
    0916770
  • 财政年份:
    2009
  • 资助金额:
    $ 53.87万
  • 项目类别:
    Standard Grant

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