M
M. Amber Hassaan
Researcher at University of Texas at Austin
Publications - 5
Citations - 635
M. Amber Hassaan is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Data structure & Parallel algorithm. The author has an hindex of 4, co-authored 5 publications receiving 602 citations.
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Journal ArticleDOI
The tao of parallelism in algorithms
Keshav Pingali,Donald Nguyen,Milind Kulkarni,Martin Burtscher,M. Amber Hassaan,Rashid Kaleem,Tsung-Hsien Lee,Andrew Lenharth,Roman Manevich,Mario Méndez-Lojo,Dimitrios Prountzos,Xin Sui +11 more
TL;DR: It is suggested that the operator formulation and tao-analysis of algorithms can be the foundation of a systematic approach to parallel programming.
Proceedings ArticleDOI
Navigating the maze of graph analytics frameworks using massive graph datasets
Nadathur Satish,Narayanan Sundaram,Md. Mostofa Ali Patwary,Jiwon Seo,Jongsoo Park,M. Amber Hassaan,Shubho Sengupta,Zhaoming Yin,Pradeep Dubey +8 more
TL;DR: A quantitative roadmap for improving the performance of all these frameworks and bridging the "ninja gap" is offered, and changes to alleviate bottlenecks arising from the algorithms themselves vs. programming model abstractions vs. the framework implementations are recommended.
Proceedings ArticleDOI
Structure-driven optimizations for amorphous data-parallel programs
Mario Méndez-Lojo,Donald Nguyen,Dimitrios Prountzos,Xin Sui,M. Amber Hassaan,Milind Kulkarni,Martin Burtscher,Keshav Pingali +7 more
TL;DR: This paper shows that many irregular algorithms have structure that can be exploited and presents three key optimizations that take advantage of algorithmic structure to reduce speculative overheads and describes the implementation of these optimizations in the Galois system and presents experimental results to demonstrate their benefits.
Proceedings ArticleDOI
Ordered and unordered algorithms for parallel breadth first search
TL;DR: The unordered algorithm is based on viewing breadth-first search as a fixpoint computation, and in general, it may perform more work than the ordered algorithms while requiring less global synchronization.
Proceedings ArticleDOI
Parallelization of asynchronous variational integrators forshared memory architectures
TL;DR: If the dependence graph for AVI can be updated incrementally as the computation is performed, it is possible to parallelize AVI in a systematic way and is able to obtain speedups of up to 20 on 24 cores for relatively small AVI problems.