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DTSTAMP:20181221T160909Z
LOCATION:C155/156
DTSTART;TZID=America/Chicago:20181115T133000
DTEND;TZID=America/Chicago:20181115T150000
UID:submissions.supercomputing.org_SC18_sess288_pan104@linklings.com
SUMMARY:Approximating for Faster, Better and Cheaper Scientific Computing
DESCRIPTION:Panel\nTech Program Reg Pass\n\nApproximating for Faster, Bett
 er and Cheaper Scientific Computing\n\nLtaief, Biros, Dongarra, Foertter, 
 Li...\n\nHPC simulations and big data applications face similar challenges
  when solving extreme-scale scientific problems: high algorithmic complexi
 ty and large memory footprint. CMOS and memory technology scaling have con
 tinuously mitigated these challenges with an exponential growth in process
 or performance and a constant increase in speed and capacity, respectively
 . The free lunch is perhaps over but now artificial intelligence comes to 
 the rescue, with the sheer volume of data to be crunched.\n\nThis has caus
 ed the community to explore disruptive hardware and software solutions to 
 overcome both challenges. One of the trends consists of exploring numerica
 l approximations, e.g., mixed/low precision and hierarchical low-rank matr
 ix computations, with hardware support for a variety of precisions.\n\nThe
  panelists investigate alternatives to today’s state-of-the-art chips and 
 numerical algorithms. The main idea consists in trading-off accuracy for p
 erformance throughout the hardware and software ecosystems in the context 
 of exascale scientific computing.
URL:https://sc18.supercomputing.org/presentation/?id=pan104&sess=sess288
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