Abstract
Nowadays, heterogeneous computing systems have proven to be a good solution for processing computation intensive high-performance applications. The main challenges for such large-scale systems are energy consumption, computing node CPU-GPU utilization dynamic variability, and so on. In response to these challenges, this study first provides heterogeneous computing systems architecture and parallel application job model. Then, we build system computing node CPU-GPU utilization model and analyze job execution energy consumption. We also deduce the optimal CPU-GPU utilization and job deadline scheduling constraint. Third, we propose a systems CPU-GPU utilization aware and energy-efficient heuristic greedy strategy (UEJS) to solve this job scheduling problem. To improve the algorithm global optimization ability, we design a hybrid particle swarm optimization algorithm (H-PSO), which incorporates the heuristic greedy strategy into the bio-inspired search optimization technique. The rigorous experimental results clearly demonstrate that our proposed H-PSO outperforms heuristic greedy strategy, Max-EAMin, and Genetic Algorithm in terms of the average energy consumption of jobs and system job rejection rate. In particular, H-PSO is better than UEJS by 36.5%, Max-EAMin by 36.3%, and GA by 46.7% in term of the job average energy consumption for heterogeneous system with high workload.
Highlights
In the past few years, many computation intensive highperformance applications, such as image processing, natural language processing, have been deployed on large-scale heterogeneous computing systems based on CPU-GPU [1]
We focus on the optimizing energy consumption by considering the system computing node CPU-GPU utilization, which implemented by a heuristic greedy job scheduling algorithm and a hybrid particle swarm optimization technique
We proposed a heuristic greedy strategy to solve this heterogeneous systems CPU-GPU utilization aware and energy-efficient job scheduling problem, which is formalized in Algorithm 1 and we name it as UEJS
Summary
In the past few years, many computation intensive highperformance applications, such as image processing, natural language processing, have been deployed on large-scale heterogeneous computing systems based on CPU-GPU [1]. Fu: CPU–GPU Utilization Aware Energy- Efficient Scheduling Algorithm on Heterogeneous Computing Systems performance requirements and minimizing system energy consumption, which is widely known as a NP-complete in the general case [8]. Techniques such as dynamic power management, dynamic voltage-frequency scaling (DVS), slack reclamation, resource hibernation, and so on, have been successfully applied in reducing the energy consumption of single computing resource [9]–[11]. We provide a systems computing node CPU-GPU utilization aware and energy-efficient heuristic greedy job scheduling algorithm (UEJS).
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