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DTSTAMP:20220812T074335Z
LOCATION:Singapore Room
DTSTART;TZID=Europe/Stockholm:20220629T110000
DTEND;TZID=Europe/Stockholm:20220629T113000
UID:submissions.pasc-conference.org_PASC22_sess156_msa117@linklings.com
SUMMARY:RACE: Speeding up Spectral Clustering and Iterative Solvers using 
 Level-Based Parallelization and Blocking Techniques
DESCRIPTION:Minisymposium\n\nRACE: Speeding up Spectral Clustering and Ite
 rative Solvers using Level-Based Parallelization and Blocking Techniques\n
 \nAlappat, Pasadakis, Hager, Schenk, Wellein\n\nClustering and linear iter
 ative solvers are indispensable for large-scale simulations in the area of
  machine learning and mathematical optimization. In this talk, we present 
 methods to accelerate existing spectral clustering techniques and solvers 
 by using the concept of levels as developed in the context of our RACE (Re
 cursive Algebraic Coloring Engine) library framework. Levels are construct
 ed using breadth-first-search (BFS) on the graph related to the underlying
  sparse matrix. These levels are then used to implement cache blocking of 
 the matrix elements for high spatial and temporal reuse. The approach find
 s its use in kernels like sparse-matrix-power vector multiplication and Ch
 ebyshev polynomial iterations, which perform repetitive back-to-back spars
 e-matrix-vector multiplication (SpMV)-type iterations without global synch
 ronizations in between. The method is highly effective and achieves perfor
 mance levels of 50-100 GF/s on a single modern Intel or AMD multicore chip
 , providing speedups of typically 2x - 4x compared to a highly optimized c
 lassical SpMV implementation. <br /><br />After introducing the optimizati
 on strategy, we discuss the application of the method to a highly scalable
  power iteration graph clustering (PIC) algorithm and the coupling of RACE
  with the Trilinos framework to speed up a class of linear iterative solve
 rs commonly found in optimization algorithms.\n\nDomain: Computer Science 
 and Applied Mathematics
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