Details
| Original language | English |
|---|---|
| Pages (from-to) | 981-998 |
| Number of pages | 18 |
| Journal | Journal of Signal Processing Systems |
| Volume | 92 |
| Issue number | 9 |
| Early online date | 1 Jul 2020 |
| Publication status | Published - Sept 2020 |
Abstract
Multicore processors serve as target platforms in a broad variety of applications ranging from high-performance computing to embedded mobile computing and automotive applications. But, the required parallel programming opens up a huge design space of parallelization strategies each with potential bottlenecks. Therefore, an early estimation of an application’s performance is a desirable development tool. However, out-of-order execution, superscalar instruction pipelines, as well as communication costs and (shared-) cache effects essentially influence the performance of parallel programs. While offering low modeling effort and good simulation speed, current approximate analytic models provide moderate prediction results so far. Virtual prototyping requires a time-consuming simulation, but produces better accuracy. Furthermore, even existing statistical methods often require detailed knowledge of the hardware for characterization. In this work, we present a concept called Multicore Performance Evaluation Tool (MPET) and its evaluation for a statistical approach for performance prediction based on abstract runtime parameters, which describe an application’s scalability behavior and can be extracted from profiles without user input. These scalability parameters not only include information on the interference of software demands and hardware capabilities, but indicate bottlenecks as well. Depending on the database setup, we achieve a competitive accuracy of 20% mean prediction error (11% median), which we also demonstrate in a case study.
Keywords
- Multicore Software Migration, Parallelization, Performance Prediction, Scalability
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- Signal Processing
- Computer Science(all)
- Information Systems
- Mathematics(all)
- Modelling and Simulation
- Computer Science(all)
- Hardware and Architecture
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In: Journal of Signal Processing Systems, Vol. 92, No. 9, 09.2020, p. 981-998.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Multicore performance prediction with MPET
T2 - Using scalability characteristics for statistical cross-architecture prediction
AU - Arndt, Oliver Jakob
AU - Lüders, Matthias
AU - Riggers, Christoph
AU - Blume, Holger
PY - 2020/9
Y1 - 2020/9
N2 - Multicore processors serve as target platforms in a broad variety of applications ranging from high-performance computing to embedded mobile computing and automotive applications. But, the required parallel programming opens up a huge design space of parallelization strategies each with potential bottlenecks. Therefore, an early estimation of an application’s performance is a desirable development tool. However, out-of-order execution, superscalar instruction pipelines, as well as communication costs and (shared-) cache effects essentially influence the performance of parallel programs. While offering low modeling effort and good simulation speed, current approximate analytic models provide moderate prediction results so far. Virtual prototyping requires a time-consuming simulation, but produces better accuracy. Furthermore, even existing statistical methods often require detailed knowledge of the hardware for characterization. In this work, we present a concept called Multicore Performance Evaluation Tool (MPET) and its evaluation for a statistical approach for performance prediction based on abstract runtime parameters, which describe an application’s scalability behavior and can be extracted from profiles without user input. These scalability parameters not only include information on the interference of software demands and hardware capabilities, but indicate bottlenecks as well. Depending on the database setup, we achieve a competitive accuracy of 20% mean prediction error (11% median), which we also demonstrate in a case study.
AB - Multicore processors serve as target platforms in a broad variety of applications ranging from high-performance computing to embedded mobile computing and automotive applications. But, the required parallel programming opens up a huge design space of parallelization strategies each with potential bottlenecks. Therefore, an early estimation of an application’s performance is a desirable development tool. However, out-of-order execution, superscalar instruction pipelines, as well as communication costs and (shared-) cache effects essentially influence the performance of parallel programs. While offering low modeling effort and good simulation speed, current approximate analytic models provide moderate prediction results so far. Virtual prototyping requires a time-consuming simulation, but produces better accuracy. Furthermore, even existing statistical methods often require detailed knowledge of the hardware for characterization. In this work, we present a concept called Multicore Performance Evaluation Tool (MPET) and its evaluation for a statistical approach for performance prediction based on abstract runtime parameters, which describe an application’s scalability behavior and can be extracted from profiles without user input. These scalability parameters not only include information on the interference of software demands and hardware capabilities, but indicate bottlenecks as well. Depending on the database setup, we achieve a competitive accuracy of 20% mean prediction error (11% median), which we also demonstrate in a case study.
KW - Multicore Software Migration
KW - Parallelization
KW - Performance Prediction
KW - Scalability
UR - http://www.scopus.com/inward/record.url?scp=85087405083&partnerID=8YFLogxK
U2 - 10.1007/s11265-020-01563-w
DO - 10.1007/s11265-020-01563-w
M3 - Article
AN - SCOPUS:85087405083
VL - 92
SP - 981
EP - 998
JO - Journal of Signal Processing Systems
JF - Journal of Signal Processing Systems
SN - 1939-8018
IS - 9
ER -