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  4. GraphCL: A Framework for Execution of Data-Flow Graphs on Multi-Device Platforms
 
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09 March 2022
Conference Paper
Titel

GraphCL: A Framework for Execution of Data-Flow Graphs on Multi-Device Platforms

Abstract
This article introduces GraphCL, an automated system for seamlessly mapping multi-kernel applications to multiple computing devices. GraphCL consists of a C ++ API and a runtime that abstracts and simplifies the execution of multi-kernel applications on heterogeneous platforms across multiple devices. The GraphCL approach has three steps. First, the application designer provides a kernel graph. In the second phase, GraphCL computes the execution schedule. After the schedule has been computed, the runtime uses the execution schedule to enqueue in parallel the processing for all system processors. GraphCL takes the kernel dependencies and the processor performance differences into account during the schedule calculation process. By deciding on the schedule, GraphCL transparently manages the order of execution and data transfers for each processor. On two asymmetric workstations, GraphCL achieves an average acceleration of 1.8x compared to the fastest device. GraphCL achieves also for the set of multi-kernel benchmarks an average 24.5% energy reduction compared to the lazy partition heuristic, that uses all the system processors without considering their power usage.
Author(s)
Moren, Konrad
Fraunhofer-Institut fĂĽr Optronik, Systemtechnik und Bildauswertung IOSB
Göhringer, Diana
TU Dresden
Hauptwerk
30th Euromicro International Conference on Parallel, Distributed and Network-based Processing, PDP 2022. Proceedings
Konferenz
International Conference on Parallel, Distributed and Network-Based Processing 2022
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DOI
10.1109/pdp55904.2022.00026
Language
English
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Fraunhofer-Institut fĂĽr Optronik, Systemtechnik und Bildauswertung IOSB
Tags
  • Data-flow graphs

  • GPGPU

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