Evaluation of FPGA Partitioning Schemes for Time and Space Sharing of Heterogeneous Tasks

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    Whilst FPGAs have been integrated in cloud ecosystems, strict constraints for mapping hardware to spatially diverse distribution of heterogeneous resources at run-time, makes their utilization for shared multi tasking challenging. This work aims at analyzing the effects of such constraints on the achievable compute density, i.e the efficiency in uti- lization of available compute resources. A hypothesis is proposed and uses static off-line partitioning and mapping of heterogeneous tasks to improve space sharing on FPGA. The hypothetical approach allows the FPGA resource to be treated as a service from higher level and supports multi-task processing, without the need for low level infrastructure sup- port. To evaluate the effects of existing constraints on our hypothesis, we implement a relatively comprehensive suite of ten real high perfor- mance computing tasks and produce multiple bitstreams per task for fair evaluation of the various schemes. We then evaluate and compare our proposed partitioning scheme to previous work in terms of achieved system throughput. The simulated results for large queues of mixed in- tensity (compute and memory) tasks show that the proposed approach can provide higher than 3× system speedup. The execution on the Nal- latech 385 FPGA card for selected cases suggest that our approach can provide on average 2.9× and 2.3× higher system throughput for compute and mixed intensity tasks while 0.2× lower for memory intensive tasks.


    • Evaluation of FPGA Partitioning Schemes for Time and Space Sharing of Heterogeneous Tasks

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      Accepted author manuscript, 4 MB, PDF-document

      Embargo ends: 20/01/2020

    Original languageEnglish
    Title of host publicationInternational Symposium on Applied Reconfigurable Computing TU Darmstadt 09 - 11 April 2019
    Publication statusPublished - 31 Mar 2019

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743

    ID: 167171658