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DTSTART;TZID=America/Denver:20231117T111000
DTEND;TZID=America/Denver:20231117T113000
UID:submissions.supercomputing.org_SC23_sess459_ws_h2rc109@linklings.com
SUMMARY:OctoRay: Framework for Scalable FPGA Cluster Acceleration of Pytho
 n Big Data Applications
DESCRIPTION:Zaid Al-Ars, Jakoba Petri-Koenig, Joost Hoozemans, Luc Dierick
 , and H. Peter Hofstee (Delft University of Technology)\n\nWe introduce Oc
 toRay, a Python framework which allows users to easily combine several FPG
 A and Python libraries to run their big data analytics pipelines in parall
 el on FPGA-enabled clusters. We show that OctoRay provides users with mult
 iple levels of freedom, regarding the type of FPGA, the choice of the appl
 ication and the number of available FPGAs per node, while illustrating the
  simplicity of the usage of the framework. Additionally, we evaluate the p
 erformance of the framework on two use cases that represent typical stages
  of many big data analytics pipelines. We show that the FPGA implementatio
 n outperforms the software baseline by 2x to 12x as the number of FPGAs sc
 ales from 1 to 6. This demonstrates that the speedup scales linearly with 
 the number of FPGAs, indicating the speedup potential OctoRay offers and t
 he low overhead of the framework.\n\nTag: Architecture and Networks\n\nReg
 istration Category: Workshop Reg Pass\n\nSession Chairs: Jason Bakos (Univ
 ersity of South Carolina); Franck Cappello (Argonne National Laboratory (A
 NL), University of Illinois); Torsten Hoefler (ETH Zürich, Microsoft Corpo
 ration); Kenneth O'Brien (Advanced Micro Devices, Inc. (AMD)); and Christi
 an Plessl (Paderborn University, Germany)\n\n
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