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DTSTART:19700308T020000
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DTSTAMP:20200129T163559Z
LOCATION:502-503-504
DTSTART;TZID=America/Denver:20191118T120000
DTEND;TZID=America/Denver:20191118T123000
UID:submissions.supercomputing.org_SC19_sess115_ws_mlhpce104@linklings.com
SUMMARY:Scheduling Optimization of Parallel Linear Algebra Algorithms Usin
 g Supervised Learning
DESCRIPTION:Workshop\n\nScheduling Optimization of Parallel Linear Algebra
  Algorithms Using Supervised Learning\n\nLaberge, Shirzad, Diehl, Kaiser, 
 Prudhomme...\n\nLinear algebra algorithms are used widely in a variety of 
 domains, e.g. machine learning, numerical physics and video games graphics
 . For all these applications, loop-level parallelism is required to achiev
 e high performance. However, finding the optimal way to schedule the workl
 oad between threads is a non-trivial problem because it depends on the str
 ucture of the algorithm being parallelized and the hardware the executable
  is run on. In the realm of Asynchronous Many Task runtime systems, a key 
 aspect of the scheduling problem is predicting the proper chunk-size, wher
 e the chunk-size is defined as the number of iterations of a for-loop are 
 assigned to a thread as one task. In this paper, we study the applications
  of supervised learning models to predict the chunk-size which yields maxi
 mum performance on multiple parallel linear algebra operations using the H
 PX backend of Blaze's linear algebra library. More precisely, we generate 
 our training and tests sets by measuring performance of the application wi
 th different chunk-sizes for multiple linear algebra operations; vector-ad
 dition, matrix-vector-multiplication, matrix-matrix addition and matrix-ma
 trix-multiplication.  We compare the use of logistic regression, neural ne
 tworks and decision trees with a newly developed decision tree based model
  in order to predict the optimal value for chunk-size. Our results show th
 at classical decision trees and our custom decision tree model are able to
  forecast a chunk-size which results in good performance for the linear al
 gebra operations.\n\nTag: Workshop Reg Pass, Machine Learning\n\nRegistrat
 ion Category: Workshop Reg Pass, Machine Learning
URL:https://sc19.supercomputing.org/presentation/?id=ws_mlhpce104&sess=ses
 s115
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