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DTSTAMP:20230831T095745Z
LOCATION:Sanada I
DTSTART;TZID=Europe/Stockholm:20230626T153000
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UID:submissions.pasc-conference.org_PASC23_sess171_msa154@linklings.com
SUMMARY:Radio-Astronomical Imaging Acceleration for Energy Efficiency
DESCRIPTION:Minisymposium\n\nStefano Corda (EPFL)\n\nThe Square Kilometre 
 Array (SKA) Telescope project, a highly complex initiative requiring four 
 supercomputer facilities to process radio-astronomical software pipelines,
  presents a significant challenge in energy consumption and environmental 
 issues' impact. Modern High-Performance Computing (HPC) clusters have intr
 oduced accelerators such as Graphics Processing Units (GPUs) and Field-Pro
 grammable Gate Arrays (FPGAs) that significantly improve energy efficiency
  compared to traditional CPUs by parallelizing scientific software. Imagin
 g is a critical component of the SKA Science Data Processors (SDPs) and pe
 rforms computations like Gridding and Degridding, which underperform on CP
 Us. We explore the possibility of using custom FPGA hardware to improve en
 ergy efficiency compared to CPUs and GPUs. Additionally, we employ the tec
 hnique of reduced precision to enhance performance and efficiency. Despite
  the potential of FPGAs, GPUs remain the preferred option for this type of
  computation. As GPU architectures continually evolve to adapt to new AI a
 nd HPC software, it is essential to evaluate the performance of various ha
 rdware options before procuring new computing systems. In this study, we a
 lso optimize the performance of imaging computing motifs on several GPU ar
 chitectures with interesting results. Optimizing GPU performance is critic
 al in reducing energy consumption and minimizing the environmental impact 
 of the SKA Telescope project.\n\nDomain: Climate, Weather and Earth Scienc
 es\n\nSession Chair: Peter Messmer (NVIDIA Inc.)
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