Libfork:使用无堆栈协程的可移植连续窃取

IF 5.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Conor J. Williams;James Elliott
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引用次数: 0

摘要

完全严格的fork-join并行是共享内存编程的一个强大模型,因为它具有最佳的时间伸缩性和对内存伸缩性的强限制。后者很少实现,因为在传统的高性能计算(High Performance Computing, HPC)语言中实现连续窃取很困难——如果不修改编译器或采用不可移植的技术,这通常是不可能实现的。我们将演示无堆栈协程(c++中的新特性)如何实现完全可移植的延续窃取,并为libfork提供一个无等待的细粒度并行库,将协程与用户空间的几何分段堆栈相结合。我们展示了我们的方法能够在各种基准测试中实现最佳的时间/内存缩放,无论是理论上还是经验上。与openMP (libomp)相比,libfork平均快7.2倍,消耗的内存少10倍。同样,与英特尔的TBB相比,libfork平均快2.7倍,消耗的内存少6.2倍。此外,我们还为调度程序引入了非统一内存访问(NUMA)优化,以演示与繁忙等待调度程序匹配的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Libfork: Portable Continuation-Stealing With Stackless Coroutines
Fully-strict fork-join parallelism is a powerful model for shared-memory programming due to its optimal time-scaling and strong bounds on memory scaling. The latter is rarely achieved due to the difficulty of implementing continuation-stealing in traditional High Performance Computing (HPC) languages – where it is often impossible without modifying the compiler or resorting to non-portable techniques. We demonstrate how stackless-coroutines (a new feature in C++$\bm {20}$) can enable fully-portable continuation stealing and present libfork a wait-free fine-grained parallelism library, combining coroutines with user-space, geometric segmented-stacks. We show our approach is able to achieve optimal time/memory scaling, both theoretically and empirically, across a variety of benchmarks. Compared to openMP (libomp), libfork is on average $7.2\times$ faster and consumes $10\times$ less memory. Similarly, compared to Intel's TBB, libfork is on average $2.7\times$ faster and consumes $6.2\times$ less memory. Additionally, we introduce non-uniform memory access (NUMA) optimizations for schedulers that demonstrate performance matching busy-waiting schedulers.
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来源期刊
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems 工程技术-工程:电子与电气
CiteScore
11.00
自引率
9.40%
发文量
281
审稿时长
5.6 months
期刊介绍: IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to: a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing. b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems. c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation. d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
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