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DTSTART:19700308T020000
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DTSTAMP:20210402T160055Z
LOCATION:Track 4
DTSTART;TZID=America/New_York:20201118T103000
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UID:submissions.supercomputing.org_SC20_sess167_pap456@linklings.com
SUMMARY:Tuning Floating-Point Precision Using Dynamic Program Information 
 and Temporal Locality
DESCRIPTION:Paper\n\nTuning Floating-Point Precision Using Dynamic Program
  Information and Temporal Locality\n\nBrunie, Iancu, Ibrahim, Brisk, Cook\
 n\nWe present a methodology for precision tuning of full applications. The
 se techniques must select a search space composed of either variables or i
 nstructions and provide a scalable search strategy.  In full application s
 ettings one cannot assume compiler support for practical reasons. Thus, an
  additional important challenge is enabling code refactoring. We argue for
  an instruction-based search space and we show; 1) how to exploit dynamic 
 program information based on call stacks, and 2) how to exploit the iterat
 ive nature of scientific codes, combined with temporal locality. We applie
 d the methodology to tune the implementation of scientific codes written i
 n a combination of Python, CUDA, C++ and Fortran, tuning calls to math exp
  library functions. The iterative search refinement always reduces the sea
 rch complexity and the number of steps to solution. Dynamic program inform
 ation increases search efficacy. Using this approach, we obtain  applicati
 on runtime performance improvements up to 27%.\n\nTag: Algorithms, Floatin
 g Point\n\nRegistration Category: Tech Program Reg Pass
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