Self-Adaptive OmpSs Tasks in Heterogeneous Environments

Planas, Judit; Badía, Rosa M.; Ayguadé, Eduard; Labarta, Jesús

doi:10.1109/ipdps.2013.53

Cited by 48 publications

(28 citation statements)

References 17 publications

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“…When targeting performance observation, performance monitoring software is either generating data to be used online [1,3,14,15,22] or offline [2,3,11,14]. In other words, whether the collected data are going to be used, while the application still runs or after its execution.…”

Section: Performance Monitoringmentioning

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: Performance Monitoringmentioning

confidence: 99%

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

et al. 2018

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“…A well-known runtime selector is the versioning scheduler [9] from the OmpSs programming framework [7]. This scheduler chooses the most appropriate task version among those marked as implementation alternatives.…”

Section: Related Workmentioning

confidence: 99%

“…These scheduling policies are implemented as independent modules that are dynamically loaded at runtime. An example of module supporting heterogeneity is the versioning scheduler [9]. This module allows selecting the most appropriate implementation of a same task depending on the target device or the execution circumstances.…”

Section: The Ompss Programming Modelmentioning

confidence: 99%

Probabilistic-Based Selection of Alternate Implementations for Heterogeneous Platforms

Fernández¹,

Cuadrado²,

Astorga³

et al. 2017

Algorithms and Architectures for Parallel Processing

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Abstract. Over the last years, heterogeneous architectures have become a de facto approach for improving the performance of numerous scientific and industrial applications. However, developing for these architectures is not straightforward: each processor demands its specific programming paradigm and, often, certain applications are only well-suited to run on a particular processing unit. Therefore, a major challenge arises when programming for these platforms: to select the most suitable device and routine implementation to solve a given problem. To deal with this issue, this paper proposes a novel probabilistic-based selector that uses the problem size to automatically choose the most appropriate version of a same kernel. In order to analyze this approach, we have developed this selector within the OmpSs programming framework and evaluated its accuracy and performance gains when executing different implementations of the general matrix-matrix multiplication. Finally, we also demonstrate how this solution delivers a comparable performance with respect to a runtime approach from the state-of-the-art.

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“…The OmpSs runtime system has demonstrated the ability to schedule an appropriate kernel implementation based on available heterogeneous hardware choices [13,28]. In this implementation, DGEMM tasks are scheduled on either CPU or GPU resources depending on the input size, available hardware, and prior performance results.…”

Section: Related Workmentioning

confidence: 99%

An Autonomic Performance Environment for Exascale

Huck

Porterfield²,

Chaimov

et al. 2015

JSFI

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Exascale systems will require new approaches to performance observation, analysis, and runtime decision-making to optimize for performance and efficiency. The standard "first-person" model, in which multiple operating system processes and threads observe themselves and record first-person performance profiles or traces for offline analysis, is not adequate to observe and capture interactions at shared resources in highly concurrent, dynamic systems. Further, it does not support mechanisms for runtime adaptation. Our approach, called APEX (Autonomic Performance Environment for eXascale), provides mechanisms for sharing information among the layers of the software stack, including hardware, operating and runtime systems, and application code, both new and legacy. The performance measurement components share information across layers, merging first-person data sets with information collected by third-person tools observing shared hardware and software states at node-and global-levels. Critically, APEX provides a policy engine designed to guide runtime adaptation mechanisms to make algorithmic changes, re-allocate resources, or change scheduling rules when appropriate conditions occur.

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Self-Adaptive OmpSs Tasks in Heterogeneous Environments

Cited by 48 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

Probabilistic-Based Selection of Alternate Implementations for Heterogeneous Platforms

An Autonomic Performance Environment for Exascale

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