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DTSTART;TZID=America/Chicago:20211115T110000
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UID:submissions.supercomputing.org_SC21_sess350_ws_ia103@linklings.com
SUMMARY:Greatly Accelerated Scaling of Streaming Problems with a Migrating
  Thread Architecture
DESCRIPTION:Workshop\n\nGreatly Accelerated Scaling of Streaming Problems 
 with a Migrating Thread Architecture\n\nPage, Kogge\n\nApplications where 
 continuous streams of data are passed through large data structures are in
 creasing in importance. However, their execution on conventional architect
 ures, is highly inefficient. The primary issue is often the need to stream
  large numbers of disparate data items through the equivalent of very larg
 e hash tables distributed across many nodes. This paper builds on prior wo
 rk on the Firehose streaming benchmark where an emerging architecture usin
 g threads that can migrate has shown to be much more efficient at such pro
 blems. This paper extends that work to use a second generation system to n
 ot only show that same improved efficiency (~10X) for larger core counts, 
 but even significantly higher raw performance (with FPGA-based cores runni
 ng at 1/10th the clock of conventional systems). Further, this additional 
 data makes a reasonable projection that an architecture with current techn
 ology would lead to 10X performance gain on an apples-to-apples basis with
  conventional systems.\n\nTag: Algorithms, Architectures, Big Data, Data A
 nalytics, Memory Systems, Numerical Algorithms\n\nRegistration Category: W
 orkshop Reg Pass
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