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  1. SAFe Program
  2. SP-3376

Investigate methods for generating a quasi real-time data stream for benchmarking the PSS Single Pulse Search pipeline

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    • Spike
    • Should have
    • PI19
    • COM PSS SW
    • None
    • Data Processing
    • Hide

      Ensures that near-realistic data rates and data blocks are used as input to the CPU implementation of the PSS Single Pulse Search pipeline in testing the pipeline's performance.

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      Ensures that near-realistic data rates and data blocks are used as input to the CPU implementation of the PSS Single Pulse Search pipeline in testing the pipeline's performance.
    • Hide

      Document the steps required to generate quasi real-time data for benchmarking, including how we will implement this approach (if the software for it is not already present).

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      Document the steps required to generate quasi real-time data for benchmarking, including how we will implement this approach (if the software for it is not already present).
    • 1
    • 1
    • 0
    • Team_PSS
    • Sprint 2
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      This work has been written up here:
      https://www.overleaf.com/read/qcvkpdxzwwrn

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      This work has been written up here: https://www.overleaf.com/read/qcvkpdxzwwrn
    • 19.3
    • Stories Completed, Outcomes Reviewed, Satisfies Acceptance Criteria, Accepted by FO
    • PSS-G1

    Description

      In order to benchmark the Single Pulse Search (SPS) pipeline, a quasi real-time data stream of time-frequency blocks is required. This Spike aims at researching the different options for generating these blocks, such that the data rate is representative of an SKA1 PSS beam. The options will be documented and the most appropriate one will be chosen for an implementation of the benchmarking tests (see SP-3377).

      The data will mimic both SKA-MID and SKA-LOW scenarios, but will not simulate voltages, rather be scaled to SKA-LOW data rates.

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              A.Noutsos Noutsos, Aristeidis
              L.Levin-Preston Levin-Preston, Lina
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              Feature Progress

                Story Point Burn-up: (100.00%)

                Feature Estimate: 1.0

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                To Do00.0
                In Progress   00.0
                Complete18.0
                Total18.0

                Dates

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                  Resolved:

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