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LOCATION:Track 7
DTSTART;TZID=America/New_York:20201119T103000
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UID:submissions.supercomputing.org_SC20_sess292_drs107@linklings.com
SUMMARY:High Throughput Low Latency Online Image Processing by GPU/FPGA Co
 processors Using RDMA Techniques
DESCRIPTION:Doctoral Showcase\n\nHigh Throughput Low Latency Online Image 
 Processing by GPU/FPGA Coprocessors Using RDMA Techniques\n\nPonsard, Houz
 et, Janvier\n\nThe constant evolution of X-ray photon sources associated w
 ith the increasing performance of high-end X-ray detectors allows cutting-
 edge experiments generate large volumes of data that are challenging to ma
 nage and store. These data management challenges have still not been addre
 ssed in a fully satisfactory way as of today, and in any case, not in a ge
 neric manner.<br /><br />This thesis is part of the ESRF RASHPA project th
 at aims at developing an RDMA-based Acquisition System for High Performanc
 e Applications. One of the main characteristics of this framework is the d
 irect data placement, straight from the detector head (data producer) to t
 he processing computing infrastructure (data receiver), at the highest acc
 eptable throughput, using RDMA techniques. <br /><br />The work carried ou
 t in this thesis is a contribution to the RASHPA framework, enabling data 
 transfer directly to the internal memory of accelerator boards. A low-late
 ncy synchronisation mechanism is proposed to trigger data processing while
  keeping pace with the detector. Thus, a comprehensive solution fulfilling
  the online data analysis challenges is proposed on standard computer and 
 massively parallel coprocessors as well.<br /><br />Scalability and versat
 ility of the proposed approach is exemplified by detector emulators, lever
 aging RoCEv2 or PCI-e and RASHPA Processing Units (RPUs) such as GPUs and 
 FPGAs. Real-time data processing on FPGA, seldom adopted in X-ray science,
  is evaluated and the benefits of high level synthesis are exhibited. The 
 assessment of the proposed data analysis pipeline includes raw data pre-tr
 eatment for Jungfrau detector, image rejection using Bragg's peaks countin
 g and data compression to sparse matrix format.\n\nRegistration Category: 
 Tech Program Reg Pass
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