Progress in Parallel Algorithms

Parallel computing offers the promise of increased performance over sequential computing, and parallel algorithms are one of its key components. There has been no aggregated or generalized comparative analysis of parallel algorithms. In this thesis, we investigate this field as a whole. We aim to understand the trends in algorithmic progress, improvement patterns, and the importance and interactions of various commonly used metrics. We collect parallel algorithms solving problems in our set and analyze them. We look at four major themes: how parallel algorithms have progressed, including in relationship to sequential algorithms and parallel hardware; how the work and span of algorithms influence performance; how problem size and available parallelism affect performance; and what researchers’ observable priorities look like. We find that more problems have had parallel improvements than sequential ones since the ’80s, that most parallel algorithms don’t improve algorithmic complexities, and much more. This research is important for us to understand how the field of parallel algorithms has changed throughout time, and what it looks like now.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Tontici, Damian

Advisors dc:contributor.advisor

• Lynch, Jayson
• Thompson, Neil

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/153852

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/153852