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  1. SAFe Program
  2. SP-1737

Introduce IDG as a gridding processing function

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    • Enabler
    • Should have
    • PI11
    • COM SDP SW
    • None
    • Data Processing
    • Hide
      • In the best case this will allow us to integrate IDG into a pipeline, demonstrating that we can switch out kernels without our processing pipeline
      • In either case we collectively learn about the characteristics of IDG in interaction with the SDP architecture
      Show
      In the best case this will allow us to integrate IDG into a pipeline, demonstrating that we can switch out kernels without our processing pipeline In either case we collectively learn about the characteristics of IDG in interaction with the SDP architecture
    • Hide
      • Provide IDG-specific interface elements for SP-1675
      • Especially provide tests covering IDG-specific functionality (w-terms, A-terms...)
      • Implement at least a proof-of-concept
        • Stretch: Integrate GPU solution
        • Stretch: Implement it as interface to libidg
      Show
      Provide IDG-specific interface elements for SP-1675 Especially provide tests covering IDG-specific functionality (w-terms, A-terms...) Implement at least a proof-of-concept Stretch: Integrate GPU solution Stretch: Implement it as interface to libidg
    • 3
    • 3
    • 7
    • Team_SCHAAP
    • Sprint 4
    • Hide
      • The (Python) interface to IDG was presented during a discussion on the gridder processing interface on Monday, July 26 and during System Demo 11.5 on Wednesday, August 18.
      • This demo included some tests, which are also available in the IDG repo, that demonstrate the capability of IDG to apply DD corrections as a continuous screen during gridding.
      • The Python interface provides access to the functions in libidg.
      • The use of GPU based processing can be turned on and off depending on the test scenario and available hardware.
      Show
      The (Python) interface to IDG was presented during a discussion on the gridder processing interface on Monday, July 26 and during System Demo 11.5 on Wednesday, August 18. This demo included some tests, which are also available in the IDG repo, that demonstrate the capability of IDG to apply DD corrections as a continuous screen during gridding. The Python interface provides access to the functions in libidg. The use of GPU based processing can be turned on and off depending on the test scenario and available hardware.
    • 14.2
    • Stories Completed, Outcomes Reviewed, NFRS met, Demonstrated, Satisfies Acceptance Criteria, Accepted by FO

    • PI22 - UNCOVERED

    Description

      Beyond more standard convolution-based gridders (see e.g. SP-1697), Image Domain Gridding is a very attractive gridding option, as it allows applying direction-dependent effects essentially without extra cost. This is clearly very relevant to the SKA, so we will want to see whether we can make IDG available as a processing function. We will especially want to be able to switch out implementations depending on the domain.

      This feature is to make a relatively low level attempt at this: Interface with IDG via a visibilities-to-grid-data interface. Details to be discussed in SP-1675.

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          Feature - A Feature is a service that fulfills a stakeholder need. Each feature includes a benefit hypothesis and acceptance criteria, and is sized or split as necessary to be delivered by a single Agile Release Train (ART) in a Program Increment (PI). SP-1697 Establish pipeline demonstrating a (possibly GPU) processing function

          • Should have - Items labelled as "Should have" are important but not necessary for delivery in the current delivery timebox. While "Should have" items can be as important as "Must have", they are often not as time-critical or there may be another way to satisfy the requirement so that it can be held back until a future delivery timebox.
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                p.wortmann Wortmann, Peter
                f.graser Graser, Ferdl
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                  Story Point Burn-up: (100.00%)

                  Feature Estimate: 3.0

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