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DTSTART:19700308T020000
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DTSTAMP:20181221T160731Z
LOCATION:D168
DTSTART;TZID=America/Chicago:20181116T093500
DTEND;TZID=America/Chicago:20181116T095000
UID:submissions.supercomputing.org_SC18_sess144_ws_pawatm101@linklings.com
SUMMARY:Comparison of the HPC and Big Data Java Libraries Spark, PCJ and A
 PGAS
DESCRIPTION:Workshop\nParallel Programming Languages, Libraries, and Model
 s, Productivity, Workshop Reg Pass\n\nComparison of the HPC and Big Data J
 ava Libraries Spark, PCJ and APGAS\n\nPosner, Fohry, Reitz\n\nAlthough Jav
 a is rarely used in HPC, there are a few notable libraries. Use of Java ma
 y help to bridge the gap between HPC and big data processing.\n\nThis pape
 r compares the big data library Spark, and the HPC libraries PCJ and APGAS
 , regarding productivity and performance.  We refer to Java versions for a
 ll libraries. For APGAS, we include both the original version and an own e
 xtension by locality-flexible tasks. We consider three benchmarks: Calcula
 tion of pi from HPC, Unbalanced Tree Search (UTS) from HPC, and WordCount 
 from the big data domain.\n\nIn performance measurements with up to 144~wo
 rkers, the extended APGAS library was the clear winner. With 144 workers, 
 APGAS programs were up to a factor of more than two faster than Spark prog
 rams, and up to about 30% faster than PCJ programs. Regarding productivity
 , the extended APGAS programs consistently needed the lowest number of dif
 ferent library constructs. Spark ranged second in productivity, and PCJ th
 ird.
URL:https://sc18.supercomputing.org/presentation/?id=ws_pawatm101&sess=ses
 s144
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