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DTSTAMP:20260522T150121Z
LOCATION:D175
DTSTART;TZID=America/Chicago:20181111T171400
DTEND;TZID=America/Chicago:20181111T173000
UID:submissions.supercomputing.org_SC18_sess143_ws_drbsd111@linklings.com
SUMMARY:A Study on Checkpoints Compression for Adjoint Computation
DESCRIPTION:Kai-Yuan Hou (Northwestern University); Sri Hari Krishna Naray
 anan (Argonne National Laboratory); Daniel Goldberg (University of Edinbur
 gh); Navjot Kukreja (Imperial College, London); and Bogdan Nicolae and Pau
 l Hovland (Argonne National Laboratory)\n\nWhen we want to understand the 
 sensitivity of a simulation model with respect to an input value or to opt
 imize an objective function, the gradient usually provides a good hint. Th
 e adjoint state method is a widely used numerical method to compute the gr
 adient of a function. It decomposes functions into a sequence of basic ope
 rations. It performs a forward sweep to evaluate the function, followed by
  a backward sweep to calculate the gradient using the chain rule iterative
 ly. One limitation of the adjoint state method is that all intermediate va
 lues from the forward sweep are needed by the backward sweep. Usually, we 
 keep only a portion of those values, called checkpoints, in the memory bec
 ause of limited space. The remaining values are either stored on the hard 
 disk or recomputed from the nearest checkpoint whenever needed. In this wo
 rk, we seek to compress the intermediate values in order to better utilize
  limited space in the memory and to speed the I/O when checkpointing to th
 e hard disk.\n\nTag: Data Management, Hot Topics, Scientific Computing\n\n
 Registration Category: Workshop Reg Pass\n\n
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