Tasking in accelerators: performance evaluation
Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
European Commission's projectECO-H-MEM - Advanced Ecosystem for Broad Heterogeneous Memory Usage (EC-H2020-749516)
EPEEC - European joint Effort toward a Highly Productive Programming Environment for Heterogeneous Exascale Computing (EPEEC) (EC-H2020-801051)
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.
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CitationToledo, L. [et al.]. Tasking in accelerators: performance evaluation. A: International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). "2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT): Gold Coast, Australia: 5-7 December 2019: proceeding". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 127-132.