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Algorithm Architecture Co-design for Dense and Sparse Matrix Computations

Abstract With the end of Dennard scaling and Moore's law, architects have moved towards

heterogeneous designs consisting of specialized cores to achieve higher performance

and energy efficiency for a target application domain. Applications of linear algebra

are ubiquitous in the field of scientific computing, machine learning, statistics,

etc. with matrix computations being fundamental to these linear algebra based solutions.

Design of multiple dense (or sparse) matrix computation routines on the

same platform is quite challenging. Added to the complexity is the fact that dense

and sparse matrix computations have large differences in their storage and access

patterns and are difficult to optimize on the same architecture. This thesis add... (more)
Created Date 2018
Contributor Animesh, Saurabh (Author) / Chakrabarti, Chaitali (Advisor) / Brunhaver, John (Committee member) / Ren, Fengbo (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering / accelerator / linear algebra / matrix / multicore / reconfigurable / sparse
Type Masters Thesis
Extent 79 pages
Language English
Note Masters Thesis Computer Engineering 2018
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis