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ParaSol: Fine-Grained Thread-Level Parallelism for Single-Threaded Performance

ParaSol: Fine-Grained Thread-Level Parallelism for Single-Threaded Performance
ParaSol:细粒度线程级并行性以实现单线程性能
批准号:
EP/W00576X/1
负责人:
Timothy Jones
金额:
$139.12万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --

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中文摘要
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英文摘要
Since the turn of the century, multicore processors have become commonplace in almost all computing domains. Instead of performance coming solely from the extraction of instruction-level parallelism (ILP), it now also requires software developers or compilers to break applications into multiple streams of instructions to exploit coarse-grained thread-level parallelism (TLP). Whilst extremely beneficial for a large class of programs, single-threaded performance still matters greatly, especially during sequential parts of an application where execution speed can dominate overall program performance (sometimes dubbed "Amdahl's cruel law"). In addition, improvements in single-threaded performance benefit all applications, as each thread experiences a performance uplift, thus impacting all parts of the code-sequential and parallel.However, improving single-threaded performance is hard. The move to multicore was driven by the power limitations of complex out-of-order hardware schemes to extract ILP (caused by the failure of Dennard scaling in the underlying transistor technologies). While designers do still increase the out-of-order instruction window, unfortunately this only makes a marginal difference and future designs are expected to be limited by Pollack's rule and the fundamental limits of ILP (the ILP wall). Conversely, although many applications would see a major performance boost from taking advantage of TLP, actually extracting it remains a challenge (John Hennessy said writing parallel code is "a problem that's as hard as any that computer science has faced").This project takes a radically different approach. Instead of going back to the future with elaborate schemes for out-of-order execution, it explores the space between ILP and the coarse-grained TLP exploited by modern multicores. In particular, it focuses on the extraction of fine-grained TLP from a single stream of instructions within and across cores. On the one hand it will investigate schemes to identify and spin-up independent short-running threads (hardware threadlets) transparently to the application, so as to boost single-threaded performance. On the other, it will research compiler techniques to indicate this parallelism, with the hardware able to exploit it within and across multiple tightly coupled cores. If successful, this project would lead to a step change in performance of high-performance cores, driven by increased utilisation of core resources and the ability to increase those resources in a scalable manner. It would also open up a broader design space, trading out-of-order pipeline complexity for ILP with increased TLP, to find better balances between area, efficiency and application-domain suitability.
期刊论文(2)
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会议论文
Decoupled Vector Runahead
解耦矢量超前运行
DOI: 10.1145/3613424.3614255
发表时间: 2023
期刊:
影响因子: --
作者: [Naithani A]
通讯作者: Naithani A
Vector Runahead for Indirect Memory Accesses
间接内存访问的向量超前运行
DOI: 10.1109/mm.2022.3163132
发表时间: 2022
期刊: IEEE Micro
影响因子: 3.6
作者: [Naithani A]
通讯作者: Naithani A
CAPcelerate: Capabilities for Heterogeneous Accelerators
  • 批准号:
    EP/V000381/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $153.2万
  • 财政年份:
    2020
  • 负责人:
    Timothy Jones
  • 依托单位:
Automatic Binary Parallelisation
  • 批准号:
    EP/P020011/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $108.33万
  • 财政年份:
    2017
  • 负责人:
    Timothy Jones
  • 依托单位:
Warwick MRC Proximity to Discovery - Industry Engagement Fund (WMIEF)
  • 批准号:
    MC_PC_15064
  • 项目类别:
    Intramural
  • 资助金额:
    $12.74万
  • 财政年份:
    2016
  • 负责人:
    Timothy Jones
  • 依托单位:
University of Warwick Experimental Equipment Proposal
  • 批准号:
    EP/M028186/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $65.72万
  • 财政年份:
    2015
  • 负责人:
    Timothy Jones
  • 依托单位:
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