Fibers are not (P)Threads: The Case for Loose Coupling of Asynchronous Programming Models and MPI Through Continuations

Joseph Schuchart, Christoph Niethammer, J. Gracia
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引用次数: 3

Abstract

Asynchronous programming models (APM) are gaining more and more traction, allowing applications to expose the available concurrency to a runtime system tasked with coordinating the execution. While MPI has long provided support for multi-threaded communication and non-blocking operations, it falls short of adequately supporting APMs as correctly and efficiently handling MPI communication in different models is still a challenge. Meanwhile, new low-level implementations of light-weight, cooperatively scheduled execution contexts (fibers, aka user-level threads (ULT)) are meant to serve as a basis for higher-level APMs and their integration in MPI implementations has been proposed as a replacement for traditional POSIX thread support to alleviate these challenges. In this paper, we first establish a taxonomy in an attempt to clearly distinguish different concepts in the parallel software stack. We argue that the proposed tight integration of fiber implementations with MPI is neither warranted nor beneficial and instead is detrimental to the goal of MPI being a portable communication abstraction. We propose MPI Continuations as an extension to the MPI standard to provide callback-based notifications on completed operations, leading to a clear separation of concerns by providing a loose coupling mechanism between MPI and APMs. We show that this interface is flexible and interacts well with different APMs, namely OpenMP detached tasks, OmpSs-2, and Argobots.
纤维不是(P)线程:异步编程模型和MPI通过延续松散耦合的情况
异步编程模型(APM)正获得越来越多的关注,它允许应用程序向负责协调执行的运行时系统公开可用的并发性。虽然MPI长期以来一直支持多线程通信和非阻塞操作,但它无法充分支持apm,因为在不同模型中正确有效地处理MPI通信仍然是一个挑战。同时,轻量级的、协作调度的执行上下文(纤维,又名用户级线程(ULT))的新的低级实现旨在作为高级apm的基础,并且它们在MPI实现中的集成已经被提议作为传统POSIX线程支持的替代品来缓解这些挑战。在本文中,我们首先建立了一个分类法,试图清楚地区分并行软件堆栈中的不同概念。我们认为,将光纤实现与MPI紧密集成既不合理也无益,反而不利于MPI成为可移植通信抽象的目标。我们建议MPI Continuations作为MPI标准的扩展,在完成操作时提供基于回调的通知,通过在MPI和apm之间提供松耦合机制,实现关注点的清晰分离。我们表明,该接口是灵活的,并与不同的apm,即OpenMP分离任务,OmpSs-2和Argobots很好地交互。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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