2026-09-19T03:05:47Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/117684 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Kale, Laxmikant V. 2022-12 application/pdf en text Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01 The student, Nikunj Gupta, accepted the attached license on 2022-12-02 at 10:09. The student, Nikunj Gupta, submitted this Thesis for approval on 2022-12-02 at 10:15. This Thesis was approved for publication on 2022-12-05 at 14:46. DSpace SAF Submission Ingestion Package generated from Vireo submission #18736 on 2023-04-12 at 08:14:04 Distributed dense linear algebra operations with Charm++ Gupta, Nikunj 2022-12-05 Charm++ Parallel And Distributed Computing Linear Algebra Array abstractions to represent linear algebra kernels and the growing popularity of Python for Data Science have increased interest in providing support for array types as first-class citizens. Similarly, scientific codes heavily rely on linear algebra kernels which are written primarily in C/C++ and Fortran with frameworks like Message Passing Interface (MPI) and OpenMP. These presents two extremes of the modern day linear algebra kernel implementations. Array abstractions while providing readability, fails to provide distributed scalability; and scientific codes implementing linear algebra while being performant, fails to be flexible and interoperable. This thesis introduces LibCharmTyles, a C++ library on top of Charm++ that supports array types as first-class citizens. Furthermore, LibCharmTyles supports both shared and distributed memory parallelism and provides linear scaling through over-decomposition, a key asset of Charm++. LibCharmTyles is conformant to Basic Linear Algebra Subprograms (BLAS) operations and supports scalar, vector, and matrix types. The thesis goes over the design of LibCharmTyles, and then explores its performance relative to single-node NumPy and cuNumeric, a drop-in replacement for NumPy supporting distributed memory. Thesis eng https://hdl.handle.net/2142/117684 Copyright 2022 Nikunj Gupta M.S. Thesis Computer Science University of Illinois at Urbana-Champaign Computer Science