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Application Speedup Characterization: Modeling Parallelization Overhead and Variations of Problem Size and Number of Cores.

Published: 02 April 2018 Publication History

Abstract

To make efficient use of multi-core processors, it is important to understand the performance behavior of parallel applications. Modeling this can enable the use of online approaches to optimize throughput or energy, or even guarantee a minimum QoS. Accurate models would avoid probe different runtime configurations, which causes overhead. Throughout the years, many speedup models were proposed. Most of them based on Amdahl's or Gustafson's laws. However, many of those make considerations such as a fixed parallel fraction, or a parallel fraction that varies linearly with problem size, and inexistent parallelization overhead. Although such models aid in the theoretical understanding, these considerations do not hold in real environments, which makes the modeling unsuitable for accurate characterization of parallel applications. The model proposed estimates the speedup taking into account the variation of its parallel fraction according to problem size, number of cores used and overhead. Using four applications from the PARSEC benchmark suite, the proposed model was able to estimate speedups more accurately than other models in recent literature.

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Cited By

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  • (2022)Analytical Energy Model Parametrized by Workload, Clock Frequency and Number of Active Cores for Share-Memory High-Performance Computing ApplicationsEnergies10.3390/en1503121315:3(1213)Online publication date: 7-Feb-2022
  • (2020)When Parallel Speedups Hit the Memory WallIEEE Access10.1109/ACCESS.2020.29904188(79225-79238)Online publication date: 2020
  • (2019)Would it be Profitable Enough to Re-adapt Algorithmic Thinking for Parallelism Paradigm2019 2nd International Conference on new Trends in Computing Sciences (ICTCS)10.1109/ICTCS.2019.8923085(1-6)Online publication date: Oct-2019

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cover image ACM Conferences
ICPE '18: Companion of the 2018 ACM/SPEC International Conference on Performance Engineering
April 2018
212 pages
ISBN:9781450356299
DOI:10.1145/3185768
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 02 April 2018

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Author Tags

  1. application characterization
  2. parallel computing
  3. performance modeling

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View all
  • (2022)Analytical Energy Model Parametrized by Workload, Clock Frequency and Number of Active Cores for Share-Memory High-Performance Computing ApplicationsEnergies10.3390/en1503121315:3(1213)Online publication date: 7-Feb-2022
  • (2020)When Parallel Speedups Hit the Memory WallIEEE Access10.1109/ACCESS.2020.29904188(79225-79238)Online publication date: 2020
  • (2019)Would it be Profitable Enough to Re-adapt Algorithmic Thinking for Parallelism Paradigm2019 2nd International Conference on new Trends in Computing Sciences (ICTCS)10.1109/ICTCS.2019.8923085(1-6)Online publication date: Oct-2019

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