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DTSTAMP:20220812T074335Z
LOCATION:Singapore Room
DTSTART;TZID=Europe/Stockholm:20220629T140000
DTEND;TZID=Europe/Stockholm:20220629T143000
UID:submissions.pasc-conference.org_PASC22_sess155_msa205@linklings.com
SUMMARY:Machine Learning at Scale using ALP/GraphBLAS
DESCRIPTION:Minisymposium\n\nMachine Learning at Scale using ALP/GraphBLAS
 \n\nYzelman\n\nWith Algebraic Programming (ALP), primitives require explic
 it algebraic structures given by programmers when given data-centric opera
 tions on containers. When considering linear algebraic relations and the d
 uality between graphs and sparse matrices, GraphBLAS emerges as an algebra
 ic programming model for high-performance scalable graph computing.<br /><
 br />Our open-source ALP/GraphBLAS framework allows for the automatic vect
 orisation, fusion, and parallelisation of algebraic programs over shared- 
 and distributed-memory parallel architectures. It includes performance sem
 antics that allows for the systematic characterisation of workloads behavi
 our at scale, while also helping algorithm designers to implement algorith
 ms in the most performant possible way.<br /><br />After introducing our f
 ramework, we discuss modifications that allow covering computations beyond
  graph and sparse matrix computations. These modifications span two direct
 ions: 1) extensions of the supported algebraic structures, 2) addition of 
 novel primitives, and 3) exposing non-algebraic programming interfaces tha
 t compile-time translate into ALP programs. Concretely, we discuss extensi
 ons to cover dense linear algebra, traditional ML workloads, as well as ve
 rtex- and edge-centric programming. Some of these extensions tightly inter
 act with compiler technologies, here touching on just-in-time compilation 
 and the use of existing and novel MLIR dialects.<br /><br />The talk close
 s by demonstrating the resulting ALP framework on various workloads and di
 scusses their performance at various scales.\n\nDomain: Computer Science a
 nd Applied Mathematics
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