A Prototype Implementation of OpenMP Task Dependency Support

Ghosh, Priyanka; Eachempati, Deepak; Chapman, Barbara

doi:10.1007/978-3-642-40698-0_10

Cited by 12 publications

(6 citation statements)

References 11 publications

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“…Other dependency system implementations have been described in previous literature, such as the implementation for the OpenUH compiler [16]. The GOMP library and LLVM's OpenMP runtime are also available online as Open Source software [17] [23].…”

Section: Related Workmentioning

confidence: 99%

Advanced synchronization techniques for task-based runtime systems

Álvarez

Sala

Maroñas

et al. 2021

Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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Task-based programming models like OmpSs-2 and OpenMP provide a flexible data-flow execution model to exploit dynamic, irregular and nested parallelism. Providing an efficient implementation that scales well with small granularity tasks remains a challenge, and bottlenecks can manifest in several runtime components. In this paper, we analyze the limiting factors in the scalability of a task-based runtime system and propose individual solutions for each of the challenges, including a wait-free dependency system and a novel scalable scheduler design based on delegation. We evaluate how the optimizations impact the overall performance of the runtime, both individually and in combination. We also compare the resulting runtime against state of the art OpenMP implementations, showing equivalent or better performance, especially for fine-grained tasks.CCS Concepts: • Computing methodologies → Parallel programming languages.

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Section: Related Workmentioning

confidence: 99%

Advanced synchronization techniques for task-based runtime systems

Álvarez

Sala

Maroñas

et al. 2021

Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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“…Perez (2014) extends OMPSs's runtime system to support overlapped array sections, and finds that in the worst cases, task management overhead can be 2.3 times higher compared to section-unaware runtime. Many compiling systems (Ghosh et al 2013;Podobas et al 2014) choose to realize section-unaware runtime.…”

Section: Related Workmentioning

confidence: 99%

AceMesh: a structured data driven programming language for high performance computing

Chen

Tang

et al. 2020

CCF Trans. HPC

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Asynchronous task-based programming models are gaining popularity to address the programmability and performance challenges of contemporary large scale high performance computing systems. In this paper we present AceMesh, a taskbased, data-driven language extension targeting legacy MPI applications. Its language features include data-centric parallelizing template, aggregated task dependence for parallel loops. These features not only relieve the programmer from tedious refactoring details but also provide possibility for structured execution of complex task graphs, data locality exploitation upon data tile templates, and reducing system complexity incurred by complex array sections. We present the prototype implementation, including task shifting, data management and communication-related analysis and transformations. The language extension is evaluated on two supercomputing platforms. We compare the performance of AceMesh with existing programming models, and the results show that NPB/MG achieves at most 1.2X and 1.85X speedups on TaihuLight and TH-2, respectively, and the Tend_lin benchmark attains more than 2X speedup on average and attain at most 3.0X and 2.2X speedups on the two platforms, respectively.

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“…Meanwhile even though the performance and efficiency advantages of task-based dataflow programming models [19] and runtimes [41] are well-established, to the best of our knowledge, there has been no research investigating selective replication in these programming models. As consequence, we strive to leverage the resilience advantages of such frameworks to develop fully automatic runtime selective task replication heuristics.…”

Section: Related Workmentioning

confidence: 99%

A Runtime Heuristic to Selectively Replicate Tasks for Application-Specific Reliability Targets

Subaşi

Yalcin

Zyulkyarov

et al. 2016

2016 IEEE International Conference on Cluster Computing (CLUSTER)

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Abstract-In this paper we propose a runtime-based selective task replication technique for task-parallel high performance computing applications. Our selective task replication technique is automatic and does not require modification/recompilation of OS, compiler or application code. Our heuristic, we call App FIT, selects tasks to replicate such that the specified reliability target for an application is achieved. In our experimental evaluation, we show that App FIT selective replication heuristic is low-overhead and highly scalable. In addition, results indicate that complete task replication is overkill for achieving reliability targets. We show that with App FIT, we can tolerate pessimistic exascale error rates with only 53% of the tasks being replicated.

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A Prototype Implementation of OpenMP Task Dependency Support

Cited by 12 publications

References 11 publications

Advanced synchronization techniques for task-based runtime systems

Advanced synchronization techniques for task-based runtime systems

AceMesh: a structured data driven programming language for high performance computing

A Runtime Heuristic to Selectively Replicate Tasks for Application-Specific Reliability Targets

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