Performance Prediction for Large-Scale Parallel Applications Using Representative Replay

• Published in: IEEE transactions on computers 2016-07, Vol.65 (7), p.2184-2198
• Main Authors: Zhai, Jidong, Chen, Wenguang, Zheng, Weimin, Li, Keqin
• Format: Article
• Language: English
• Subjects: Cloud computing; Computation; Computational modeling; Computer simulation; Debugging; Deterministic Replay; High performance computing; Marketing; MPI; Parallel Applications; Performance Prediction; Phantoms; Platforms; Production scheduling; Servers; Similarity; Time measurement; Trace-driven Simulation
• ISSN: 0018-9340; 1557-9956
• DOI: 10.1109/TC.2015.2479630

Automatically predicting performance of parallel applications has been a long-standing goal in the area of high performance computing. However, accurate performance prediction is challenging, since the execution time of parallel applications is determined by several factors, such as sequential computation time, communication time and their complex interactions. Despite previous efforts, accurately estimating the sequential computation time in each process for large-scale parallel applications remains an open problem. In this paper, we propose a novel approach to acquiring accurate sequential computation time using a parallel debugging technique called deterministic replay. The main advantage of our approach is that we only need a single node of a target platform but the whole target platform does not need to be available. Therefore, with this approach we can simply measure the real sequential computation time on a target node for each process on by one. Moreover, we observe that there is great computation similarity in parallel applications, not only within each process but also among different processes. Based on this observation, we further propose representative replay that can significantly reduce replay overhead, because we only need to replay partial iterations for representative processes instead of all of them. Finally, we implement a complete performance prediction system, called Phantom, which combines the above computation-time acquisition approach and a trace-driven simulator. We validate our approach on both traditional HPC platforms and the latest Amazon EC2 cloud platform. On both types of platforms, prediction error of our approach is less than 7 percent on average up to 2,500 processes.