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Title:Communication avoiding parallel algorithms for amorphous problems
Author(s):Maleki, Saeed
Director of Research:Padua, David
Doctoral Committee Chair(s):Padua, David
Doctoral Committee Member(s):Garzaran, Maria J.; Kale, Laxmikant; Pingali, Keshav; Musuvathi, Madanlal
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Amorphous
Symbolic
Parallel
Communication Avoiding
Linear Algebra
Tropical Semi-Ring
Single-Source Shortest Path
Dynamic Programming
Longest Common Subsequence
Smith-Waterman
Needleman-Wunsch
Viterbi Decoder
Parallel Notations
Abstract:Parallelizing large sized problem in parallel systems has always been a challenge for programmer. This difficulty is caused by the complexity of the existing systems as well as the target problems. This is becoming a greater issue as the data sizes are constantly growing and as a result, larger parallel systems are required. Graph algorithms, machine learning problems and bio-informatics methods are among the many ever-growing problems. These group of problems are amorphous, meaning that memory accesses are unpredictable and the application usually has a poor locality. Therefore, synchronizations in these problems are specially costly since all-to-all communications are required and delivering an efficient parallel algorithm becomes more challenging. Another difficulty with these problems is that the amount of parallelism in them is limited which naturally makes them hard to parallelize. This is due to complicated data-dependences among the data elements in the algorithm. Writing parallel algorithms for these problems, on the other hand, are specially difficult since an amorphous problem can be expressed in several dramatically different ways. This is because of complex data dependences which are statically unknown and therefore, many unique parallel approaches exist for a single problem. Consequently, programming each single approach requires starting from scratch which is time consuming. This thesis introduces several ways to avoid costly communications in amorphous problems by compromising from the computation. This means that we can increase the total amount of work done by the processors to avoid synchronizations in an algorithm. This is specially effective in large clusters since there is a massive computing power with very costly communications. These approaches, clearly, have a trade off between computation and communication and in this thesis, we study these trade offs as well. Also, we propose a new language to express the proposed algorithms to overcome the programming difficulty of the problems by providing tunable parameters for performance.
Issue Date:2015-11-10
Type:Thesis
URI:http://hdl.handle.net/2142/88981
Rights Information:Copyright 2015 Saeed Maleki
Date Available in IDEALS:2016-03-02
Date Deposited:2015-12


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