Tasking in accelerators: performance evaluation

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this work, we analyze the implications and results of implementing dynamic parallelism, concurrent kernels and CUDA Graphs to solve task-oriented problems. As a benchmark we propose three different methods for solving DGEMM operation on tiled-matrices; which might be the most popular benchmark for performance analysis. For the algorithms that we study, we present significant differences in terms of data dependencies, synchronization and granularity. The main contribution of this work is determining which of the previous approaches work better for having multiple task running concurrently in a single GPU, as well as stating the main limitations and benefits of every technique. Using dynamic parallelism and CUDA Streams we were able to achieve up to 30% speedups and for CUDA Graph API up to 25x acceleration outperforming state of the art results.This project has received funding from the EPEEC project from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 801051, from the Spanish Ministry of Economy and Competitiveness under the project Computación de Altas Prestaciones VII ( TIN2015-65316-P ) and the Departament d’Innovació, Universitats i Empresa de la Generalitat de Catalunya, under project MPEXPAR: Models de Pro-gramació i Entorns d’Execució Paral·lels (2014-SGR-1051 ). Finally, this project also received funding from the Spanish Ministry of Economy and Competitiveness under the Juan de la Cierva Grant Agreement No IJCI-2017-33511 , and from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska Curie grant agreement No. 749516 .Peer Reviewe