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DTSTAMP:20181221T160910Z
LOCATION:C141/143/149
DTSTART;TZID=America/Chicago:20181114T153000
DTEND;TZID=America/Chicago:20181114T170000
UID:submissions.supercomputing.org_SC18_sess215@linklings.com
SUMMARY:Arithmetic and Optimization
DESCRIPTION:Paper\nAlgorithms, Applications, Architectures, Compiler Analy
 sis and Optimization, Floating Point, Performance, Precision, Programming 
 Systems, Tools, Tech Program Reg Pass\n\nADAPT: Algorithmic Differentiatio
 n Applied to Floating-Point Precision Tuning\n\nMenon, Lam, Osei-Kuffuor, 
 Schordan, Lloyd...\n\nHPC applications extensively use floating point arit
 hmetic operations to solve computational problems in various domains. Mixe
 d precision computing, use of lowest precision data type sufficient to ach
 ieve a desired accuracy, have been explored to improve performance, reduce
  power consumption and dat...\n\n---------------------\nAssociative Instru
 ction Reordering to Alleviate Register Pressure\n\nRawat, Sukumaran-Rajam,
  Rountev, Rastello, Pouchet...\n\nRegister allocation is generally conside
 red a practically solved problem. For most applications, the register allo
 cation strategies in production compilers are very effective in controllin
 g the number of loads/stores and register spills. However, existing regist
 er allocation strategies are not effec...\n\n---------------------\nHarnes
 sing GPU's Tensor Cores Fast FP16 Arithmetic to Speedup Mixed-Precision It
 erative Refinement Solvers\n\nHaidar, Tomov, Dongarra, Higham\n\nThe use o
 f low-precision arithmetic in computing methods has been a powerful tool t
 o accelerate numerous scientific computing applications including Artifici
 al Intelligence. We present an investigation showing that other HPC applic
 ations can harness this power too, and in particular, the general HPC...\n
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