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Exploring code portability solutions for HEP with a particle tracking test code.

Hammad Ather1, Sophie Berkman2, Giuseppe Cerati3

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Summary
This summary is machine-generated.

High energy physics (HEP) experiments must leverage diverse computing resources like GPUs. Achieving optimal performance with code portability tools requires careful implementation, as results vary significantly based on factors like memory layout and compiler choice.

Keywords:
code portabilityheterogeneous architecturesheterogeneous computingparticle trackingportability solutions

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Area of Science:

  • High Energy Physics
  • Computational Science
  • Software Engineering

Background:

  • Traditional high energy physics (HEP) experiments heavily rely on x86 CPUs.
  • Upcoming experiments like DUNE and the High-Luminosity LHC will have substantially increased computing demands.
  • Utilizing diverse computing resources, including Graphics Processing Units (GPUs) from various vendors, is essential to meet these demands.

Purpose of the Study:

  • To evaluate the performance and implementation experience of different code portability solutions for HEP.
  • To compare portable code implementations against a reference x86 CPU version using a HEP tracking algorithm test case.
  • To identify factors influencing performance in portable code execution across multiple architectures.

Main Methods:

  • A test code from a HEP tracking algorithm was used to benchmark various portability solutions.
  • Performance was compared across different portability tools, including compiler pragma-based approaches and abstraction libraries.
  • Factors such as memory layout, memory pinning strategy, and compiler selection were analyzed for their impact on performance.

Main Results:

  • Portable implementations demonstrated performance close to the reference code in several instances.
  • Significant performance variations were observed, highly dependent on the specific implementation details.
  • Optimal performance was challenging to achieve, even for relatively simple test applications.

Conclusions:

  • Code portability tools offer viable solutions for HEP computing, but achieving peak performance requires careful consideration of implementation specifics.
  • Memory layout, pinning strategies, and compiler choices are critical factors influencing the efficiency of portable code.
  • Ongoing development in compilers and portability tools is crucial for their successful deployment in future HEP experiments.