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  1. SAFe Program
  2. SP-1762

Investigate CPU Implementation of FLDO

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    • Spike
    • Could have
    • PI11
    • COM PSS SW
    • None
    • Data Processing
    • Hide

      Compute resources optimisation

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      Compute resources optimisation
    • Hide

      Produce a proof-of-concept code that performs folding on multi-core CPUs, efficiently and in parallel ("folding" here refers to the first half of FLDO). Produce a document that describes the algorithmic model and key implementation details of the above code. The document will include speed benchmarks, and a comparison with recent benchmarks of the folding stage of the existing GPU implementation (Origami).

      Show
      Produce a proof-of-concept code that performs folding on multi-core CPUs, efficiently and in parallel ("folding" here refers to the first half of FLDO). Produce a document that describes the algorithmic model and key implementation details of the above code. The document will include speed benchmarks, and a comparison with recent benchmarks of the folding stage of the existing GPU implementation (Origami).
    • 1
    • 1
    • 43
    • Team_PSS
    • Sprint 2
    • Hide

      Insert links to relevant docs and/or code here:
      https://gitlab.com/vmorello/skald also see atteched pictures in AT4-523

      See document:
      https://www.overleaf.com/read/hqcvqvgpfmrr

      Show
      Insert links to relevant docs and/or code here: https://gitlab.com/vmorello/skald also see atteched pictures in AT4-523 See document: https://www.overleaf.com/read/hqcvqvgpfmrr
    • 11.3
    • Stories Completed, Outcomes Reviewed, NFRS met, Satisfies Acceptance Criteria, Accepted by FO

    Description

      Investigate using CPU to implement FLDO. From the results it will be possible to establish whether folding can be realistically done in real-time on a recent multi-core CPU, assuming the data dimension and number of candidates for an SKA scenario. Implementing and benchmarking a CPU optimisation stage (the second half of FLDO) would be investigated later.

       

       

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                A.Noutsos Noutsos, Aristeidis
                m.pearson Pearson, Mike [X] (Inactive)
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                Feature Progress

                  Story Point Burn-up: (100.00%)

                  Feature Estimate: 1.0

                  IssuesStory Points
                  To Do00.0
                  In Progress   00.0
                  Complete28.0
                  Total28.0

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                    Updated:
                    Resolved:

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