TY - GEN

T1 - Statistical Performance Prediction for Multicore Applications Based on Scalability Characteristics

AU - Arndt, Oliver Jakob

AU - Luders, Matthias

AU - Blume, Holger

PY - 2019/9/5

Y1 - 2019/9/5

N2 - Multicore processors serve as target platforms in a broad variety of applications ranging from high-performance computing to embedded mobile computing and automotive. 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 a good modeling and simulation speed, 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 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 proof 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. 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 a good modeling and simulation speed, 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 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 proof in a case study.

KW - Advanced driver-assistance systems

KW - MPSoC

KW - Parallelization

KW - Performance prediction

KW - Scalability

U2 - 10.1109/ASAP.2019.00015

DO - 10.1109/ASAP.2019.00015

M3 - Conference contribution

AN - SCOPUS:85072598680

SN - 978-1-7281-1602-0

T3 - Proceedings of the International Conference on Application-Specific Systems, Architectures and Processors

SP - 255

EP - 262

BT - 2019 IEEE 30th International Conference on Application-Specific Systems, Architectures and Processors (ASAP)

PB - IEEE Computer Society

T2 - 30th IEEE International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2019

Y2 - 15 July 2019 through 17 July 2019

ER -