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Hasyim Gautama

dblp:78/4865 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 2006
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 3 · 3 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Performance modeling and evaluation · 87% Parallel and multicore computing · 13%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation › performance prediction
execution time prediction		 0.112006
Low-Cost Static Performance Prediction of Parallel Stochastic Task Compositions · IEEE Trans. Parallel Distributed Syst. 2006
Parallel and multicore computing › parallel computing
parallel application performance		 0.012006
Low-Cost Static Performance Prediction of Parallel Stochastic Task Compositions · IEEE Trans. Parallel Distributed Syst. 2006

Methods — techniques the papers use, named apart from their topics

moment approximation · 0.1analytical modeling · 0.1
YearPublicationVenuePosition
2006 Low-Cost Static Performance Prediction of Parallel Stochastic Task Compositions
abstract
Current analytic solutions to the execution time distribution of a parallel composition of tasks having stochastic execution times are computationally complex, except for a limited number of distributions. In this paper, we present an analytical solution based on approximating execution time distributions in terms of the first four statistical moments. This low-cost approach allows the parallel execution time distribution to be approximated at ultra-low solution complexity for a wide range of execution time distributions. The accuracy of our method is experimentally evaluated for synthetic distributions as well as for task execution time distributions found in real parallel programs and kernels (NAS-EP, SSSP, APSP, Splash2-Barnes, PSRS, and WATOR). Our experiments show that the prediction error of the mean value of the parallel execution time for N-ary parallel composition is in the order of percents, provided the task execution time distributions are sufficiently independent and unimodal.
Hasyim Gautama, Arjan J. C. van Gemund
 IEEE Trans. Parallel Distributed Syst.1
2003 Symbolic Performance Prediction of Speculative Parallel Programs
Hasyim Gautama, Arjan J. C. van Gemund
 Euro-Par1
2001 Performance Prediction of Data-Dependent Task Parallel Programs
Hasyim Gautama, Arjan J. C. van Gemund
 Euro-Par1