Performance and energy analysis of OpenMP runtime systems with dense linear algebra algorithms

Author:

Ferreira Lima João Vicente1ORCID,Raïs Issam2,Lefèvre Laurent2,Gautier Thierry2

Affiliation:

1. Universidade Federal de Santa Maria, Santa Maria, Rio Grande do Sul, Brazil

2. Université de Lyon, INRIA, CNRS, ENS de Lyon, Université Claude-Bernard Lyon 1, LIP, Lyon, France

Abstract

In this article, we analyze performance and energy consumption of five OpenMP runtime systems over a non-uniform memory access (NUMA) platform. We also selected three CPU-level optimizations or techniques to evaluate their impact on the runtime systems: processors features Turbo Boost and C-States, and CPU Dynamic Voltage and Frequency Scaling through Linux CPUFreq governors. We present an experimental study to characterize OpenMP runtime systems on the three main kernels in dense linear algebra algorithms (Cholesky, LU, and QR) in terms of performance and energy consumption. Our experimental results suggest that OpenMP runtime systems can be considered as a new energy leverage, and Turbo Boost, as well as C-States, impacted significantly performance and energy. CPUFreq governors had more impact with Turbo Boost disabled, since both optimizations reduced performance due to CPU thermal limits. An LU factorization with concurrent-write extension from libKOMP achieved up to 63% of performance gain and 29% of energy decrease over original PLASMA algorithm using GNU C compiler (GCC) libGOMP runtime.

Publisher

SAGE Publications

Subject

Hardware and Architecture,Theoretical Computer Science,Software

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Energy Efficiency of Multithreaded WZ Factorization with the Use of OpenMP and OpenACC on CPU and GPU;Lecture Notes in Computer Science;2024

2. Efficient parallel kernel based on Cholesky decomposition to accelerate multichannel nonnegative matrix factorization;The Journal of Supercomputing;2023-06-16

3. Power Constrained Autotuning using Graph Neural Networks;2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS);2023-05

4. Study of the Processor and Memory Power and Energy Consumption of Coupled Sparse/Dense Solvers;2022 IEEE 34th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD);2022-11

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