Monitoring Heterogeneous Applications with the OpenMP Tools Interface

Michael Wagner (), Germán Llort, Antonio Filgueras, Daniel Jiménez-González, Harald Servat, Xavier Teruel, Estanislao Mercadal, Carlos Álvarez, Judit Giménez, Xavier Martorell, Eduard Ayguadé and Jesús Labarta
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Michael Wagner: Barcelona Supercomputing Center (BSC) and Universitat Politècnica de Catalunya (UPC)
Germán Llort: Barcelona Supercomputing Center (BSC) and Universitat Politècnica de Catalunya (UPC)
Antonio Filgueras: Barcelona Supercomputing Center (BSC) and Universitat Politècnica de Catalunya (UPC)
Daniel Jiménez-González: Barcelona Supercomputing Center (BSC) and Universitat Politècnica de Catalunya (UPC)
Harald Servat: Intel Corporation
Xavier Teruel: Barcelona Supercomputing Center (BSC) and Universitat Politècnica de Catalunya (UPC)
Estanislao Mercadal: Barcelona Supercomputing Center (BSC) and Universitat Politècnica de Catalunya (UPC)
Carlos Álvarez: Barcelona Supercomputing Center (BSC) and Universitat Politècnica de Catalunya (UPC)
Judit Giménez: Barcelona Supercomputing Center (BSC) and Universitat Politècnica de Catalunya (UPC)
Xavier Martorell: Barcelona Supercomputing Center (BSC) and Universitat Politècnica de Catalunya (UPC)
Eduard Ayguadé: Barcelona Supercomputing Center (BSC) and Universitat Politècnica de Catalunya (UPC)
Jesús Labarta: Barcelona Supercomputing Center (BSC) and Universitat Politècnica de Catalunya (UPC)

A chapter in Tools for High Performance Computing 2016, 2017, pp 41-57 from Springer

Abstract: Abstract Heterogeneous systems are gaining more importance in supercomputing, yet they are challenging to program and developers require support tools to understand how well their accelerated codes perform and how they can be improved. The OpenMP Tools Interface (OMPT) is a new performance monitoring interface that is being considered for integration into the OpenMP standard. OMPT allows monitoring the execution of heterogeneous OpenMP applications by revealing the activity of the runtime through a standardized API as well as facilitating the exchange of performance information between devices with accelerated codes, and the analysis tool. In this paper we describe our efforts implementing parts of the OMPT specification necessary to monitor accelerators. In particular, the integration of the OMPT features to our parallel runtime system and instrumentation framework helps to obtain detailed performance information about the execution of the accelerated tasks issued to the devices to allow an insightful analysis. As a result of this analysis, the parallel runtime of the programming model has been improved. We focus on the evaluation of monitoring FPGA devices studying the performance of a common kernel in scientific algorithms: matrix multiplication. Nonetheless, this development is as well applicable to monitor GPU accelerators and Intel®; Xeon PhiTM co-processors operating under the OmpSs programming model.

Keywords: Memory Transfer; Hardware Accelerator; Parallel Programming Model; Master Thread; Compiler Directive (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-56702-0_3

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