Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Euro-Par 2019: Parallel Processing Workshops |
| Untertitel | Euro-Par 2019 International Workshops, Göttingen, Germany, August 26–30, 2019, Revised Selected Papers |
| Herausgeber/-innen | Ulrich Schwardmann, Christian Boehme, Dora B. Heras, Valeria Cardellini, Emmanuel Jeannot, Antonio Salis, Claudio Schifanella, Ravi Reddy Manumachu, Dieter Schwamborn, Laura Ricci, Oh Sangyoon, Thomas Gruber, Laura Antonelli, Stephen L. Scott |
| Herausgeber (Verlag) | Springer Nature |
| Seiten | 282-294 |
| Seitenumfang | 13 |
| ISBN (Print) | 9783030483395 |
| Publikationsstatus | Veröffentlicht - 2020 |
| Veranstaltung | 25th International European Conference on Parallel and Distributed Computing, EuroPar 2019 - Göttingen, Deutschland Dauer: 26 Aug. 2019 → 30 Aug. 2019 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 11997 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Even though parallel programs, written in high-level languages, are portable across different architectures, their parallelism does not necessarily scale after migration. Predicting a multicore-application’s performance on the target platform in an early development phase can prevent developers from unpromising optimizations and thus significantly reduce development time. However, the vast diversity and heterogeneity of system-design decisions of processor types from HPC and desktop PCs to embedded MPSoCs complicate the modeling due to varying capabilities. Concurrency effects (caching, locks, or bandwidth bottlenecks) influence parallel runtime behavior as well. Complex performance prediction approaches emerged, which can be grouped into: virtual prototyping, analytical models, and statistical methods. In this work, we predict the performance of two algorithms from the field of advanced driver-assistance systems in a case study. With the following three methods, we provide a comparative overview of state-of-the-art predictions: GEM5 (virtual prototype), IBM Exabounds (analytical model), and an in-house developed statistical method. We first describe the theoretical background, describe the experimental- and model-setup, and give a detailed evaluation of the prediction. In addition, we discuss the applicability of all three methods for predicting parallel and heterogeneous systems.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Euro-Par 2019: Parallel Processing Workshops : Euro-Par 2019 International Workshops, Göttingen, Germany, August 26–30, 2019, Revised Selected Papers. Hrsg. / Ulrich Schwardmann; Christian Boehme; Dora B. Heras; Valeria Cardellini; Emmanuel Jeannot; Antonio Salis; Claudio Schifanella; Ravi Reddy Manumachu; Dieter Schwamborn; Laura Ricci; Oh Sangyoon; Thomas Gruber; Laura Antonelli; Stephen L. Scott. Springer Nature, 2020. S. 282-294 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 11997 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Multicore Performance Prediction – Comparing Three Recent Approaches in a Case Study
AU - Lüders, Matthias
AU - Arndt, Oliver Jakob
AU - Blume, Holger
PY - 2020
Y1 - 2020
N2 - Even though parallel programs, written in high-level languages, are portable across different architectures, their parallelism does not necessarily scale after migration. Predicting a multicore-application’s performance on the target platform in an early development phase can prevent developers from unpromising optimizations and thus significantly reduce development time. However, the vast diversity and heterogeneity of system-design decisions of processor types from HPC and desktop PCs to embedded MPSoCs complicate the modeling due to varying capabilities. Concurrency effects (caching, locks, or bandwidth bottlenecks) influence parallel runtime behavior as well. Complex performance prediction approaches emerged, which can be grouped into: virtual prototyping, analytical models, and statistical methods. In this work, we predict the performance of two algorithms from the field of advanced driver-assistance systems in a case study. With the following three methods, we provide a comparative overview of state-of-the-art predictions: GEM5 (virtual prototype), IBM Exabounds (analytical model), and an in-house developed statistical method. We first describe the theoretical background, describe the experimental- and model-setup, and give a detailed evaluation of the prediction. In addition, we discuss the applicability of all three methods for predicting parallel and heterogeneous systems.
AB - Even though parallel programs, written in high-level languages, are portable across different architectures, their parallelism does not necessarily scale after migration. Predicting a multicore-application’s performance on the target platform in an early development phase can prevent developers from unpromising optimizations and thus significantly reduce development time. However, the vast diversity and heterogeneity of system-design decisions of processor types from HPC and desktop PCs to embedded MPSoCs complicate the modeling due to varying capabilities. Concurrency effects (caching, locks, or bandwidth bottlenecks) influence parallel runtime behavior as well. Complex performance prediction approaches emerged, which can be grouped into: virtual prototyping, analytical models, and statistical methods. In this work, we predict the performance of two algorithms from the field of advanced driver-assistance systems in a case study. With the following three methods, we provide a comparative overview of state-of-the-art predictions: GEM5 (virtual prototype), IBM Exabounds (analytical model), and an in-house developed statistical method. We first describe the theoretical background, describe the experimental- and model-setup, and give a detailed evaluation of the prediction. In addition, we discuss the applicability of all three methods for predicting parallel and heterogeneous systems.
KW - Advanced driver-assistance systems
KW - MPSoC
KW - Parallelization
KW - Performance prediction
KW - Scalability
KW - Virtual prototyping
UR - http://www.scopus.com/inward/record.url?scp=85086235192&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-48340-1_22
DO - 10.1007/978-3-030-48340-1_22
M3 - Conference contribution
AN - SCOPUS:85086235192
SN - 9783030483395
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 282
EP - 294
BT - Euro-Par 2019: Parallel Processing Workshops
A2 - Schwardmann, Ulrich
A2 - Boehme, Christian
A2 - B. Heras, Dora
A2 - Cardellini, Valeria
A2 - Jeannot, Emmanuel
A2 - Salis, Antonio
A2 - Schifanella, Claudio
A2 - Manumachu, Ravi Reddy
A2 - Schwamborn, Dieter
A2 - Ricci, Laura
A2 - Sangyoon, Oh
A2 - Gruber, Thomas
A2 - Antonelli, Laura
A2 - Scott, Stephen L.
PB - Springer Nature
T2 - 25th International European Conference on Parallel and Distributed Computing, EuroPar 2019
Y2 - 26 August 2019 through 30 August 2019
ER -