Qthreads: An API for programming with millions of lightweight threads

Wheeler, Kyle Bruce; Murphy, Richard C.; Thain, Douglas

doi:10.1109/ipdps.2008.4536359

Cited by 157 publications

(92 citation statements)

References 17 publications

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“…In addition to languages and extensions, industry-standard and wellsupported parallel libraries based on task parallelism have emerged, such as Intel Cilk Plus [23] or Intel TBB [28]. There are also runtimes specifically designed to improve shared memory performance of existing language extensions, such as Qthreads [27] or Argobots [24]; this topic is of significant importance, considering the increase in many-core processors in recent years and, consequently, the importance of efficient lightweight runtimes. Task-based environments for heterogeneous hardware have also naturally developed with the emergence of accelerator and GPU computing; StarPU [2] is an example of such an environment.…”

Section: Stefano Markidis Markidis@kthsementioning

confidence: 99%

A taxonomy of task-based parallel programming technologies for high-performance computing

et al. 2018

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Task-based programming models for shared memory-such as Cilk Plus and OpenMP 3-are well established and documented. However, with the increase in parallel, many-core, and heterogeneous systems, a number of research-driven projects have developed more diversified task-based support, employing various programming and runtime features. Unfortunately, despite the fact that dozens of different task-based systems exist today and are actively used for parallel and high-performance computing (HPC), no comprehensive overview or classification of task-based technologies for HPC exists. In this paper, we provide an initial task-focused taxonomy for HPC technologies, which covers both programming interfaces and runtime mechanisms. We

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Section: Stefano Markidis Markidis@kthsementioning

confidence: 99%

A taxonomy of task-based parallel programming technologies for high-performance computing

et al. 2018

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“…This is the case of Converse Threads [8] for Charm++ [9] and Nanos++ LWTs [10] for task parallelism in OmpSs [11]. Moreover, there are general-purpose solutions such as MassiveThreads [12], Qthreads [13], and Argobots [14]; and solutions that abstract the LWT facilities such as Cilk [15], Intel TBB [16], and Go [17]. In addition, other solutions like Stackless Python [18] and Protothreads [19] are more focused on stackless threads.…”

Section: Introductionmentioning

confidence: 99%

A Review of Lightweight Thread Approaches for High Performance Computing

Castelló

Peña

Seo

et al. 2016

2016 IEEE International Conference on Cluster Computing (CLUSTER)

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Abstract-High-level, directive-based solutions are becoming the programming models (PMs) of the multi/many-core architectures. Several solutions relying on operating system (OS) threads perfectly work with a moderate number of cores. However, exascale systems will spawn hundreds of thousands of threads in order to exploit their massive parallel architectures and thus conventional OS threads are too heavy for that purpose. Several lightweight thread (LWT) libraries have recently appeared offering lighter mechanisms to tackle massive concurrency. In order to examine the suitability of LWTs in high-level runtimes, we develop a set of microbenchmarks consisting of commonlyfound patterns in current parallel codes. Moreover, we study the semantics offered by some LWT libraries in order to expose the similarities between different LWT application programming interfaces. This study reveals that a reduced set of LWT functions can be sufficient to cover the common parallel code patterns and that those LWT libraries perform better than OS threads-based solutions in cases where task and nested parallelism are becoming more popular with new architectures.

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“…Thread Building Block (TBB) [37], Qthread [38] are also used on different platforms. Using these low level thread libraries to implement parallel applications requires lots of effort by users.…”

Section: Related Workmentioning

confidence: 99%

“…The overall framework software stack is shown as Figure 3 In our design we use threads as the basic execution unit, so we need the support of thread libraries. Various thread libraries can be used [4,38,37]. Here we choose the Pthreads library as our underlying library runtime.…”

Section: Framework Runtime System Designmentioning

confidence: 99%

“…With a user-level threads library we can change thread scheduling mechanisms dynamically and complete large amounts of thread management work with less overhead compared to system-level threads. There is some research about using a user-level threads library for better performance [38,63]. Also, using other threads library like Intel TBB [37] may get better performance when running on Intel CPUs.…”

Section: User-level and Other Threads Librariesmentioning

confidence: 99%

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United tasks and conduits for programming on heterogeneous computing platforms

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Qthreads: An API for programming with millions of lightweight threads

Cited by 157 publications

References 17 publications

A taxonomy of task-based parallel programming technologies for high-performance computing

A taxonomy of task-based parallel programming technologies for high-performance computing

A Review of Lightweight Thread Approaches for High Performance Computing

United tasks and conduits for programming on heterogeneous computing platforms

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