Towards predicting GPGPU performance for concurrent workloads in Multi-GPGPU environment
- PDF / 1,617,197 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 3 Downloads / 193 Views
(0123456789().,-volV)(0123456789(). ,- volV)
Towards predicting GPGPU performance for concurrent workloads in Multi-GPGPU environment Sunggon Kim1
•
Dongwhan Kim2 • Yongseok Son3
•
Hyeonsang Eom1
Received: 29 November 2019 / Revised: 28 January 2020 / Accepted: 2 April 2020 Ó Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract General-purpose graphics processing units (GPGPUs) have been widely adapted to the industry due to the high parallelism of graphics processing units (GPUs) compared with central processing units (CPUs). Especially, a GPGPU device has been adopted for various scientific workloads which have high parallelism. To handle the ever increasing demand, multiple applications are often run simultaneously in multiple GPGPU devices. However, when multiple applications are running concurrently, the overall performance of GPGPU devices varies significantly due to the different characteristics of GPGPU applications. To improve the efficiency, it is critical to anticipate the performance of applications and find optimal scheduling policy. In this paper, we analyze various types of scientific applications and identify factors that impact the performance during the concurrent execution of the applications in GPGPU devices. Our analysis results show that each application has distinct characteristic. By considering distinct characteristics of applications, a certain combination of applications has better performance compared with the others when executed concurrently in multiple GPGPU devices. Based on the finding of our analysis, we propose a simulator which predicts the performance of GPGPU devices when multiple applications are running concurrently. Our simulator collects performance metrics during the execution of applications and predicts the performance of certain combinations using the performance metrics. The experimental result shows that the best combination of applications can increase the performance by 39.44% and 65.98% compared with the average of combinations and the worst case, respectively when using a single GPGPU device. When utilizing multiple GPGPU devices, our result shows that the performance improve can be 24.78% and 39.32% compared with the average and the worst combinations, respectively. Keywords GPGPU Performance prediction Heterogeneous Computing Performance modeling
1 Introduction
& Hyeonsang Eom [email protected] Sunggon Kim [email protected] Dongwhan Kim [email protected] Yongseok Son [email protected] 1
Seoul National University, Seoul, South Korea
2
Samsung Electronics, System LSI Business, Yongin-Si, South Korea
3
Chung-Ang University, Seoul, South Korea
General purpose graphics processing units (GPGPU) are being widely used in industry due to the high computation capabilities compared with traditional Central Processing Unit (CPU). Especially, GPGPUs are widely used in many HPC and cloud systems since GPUs are equipped with many programmable computational cores that supports thousands of parallel threads [11, 13, 21]. As G
Data Loading...
