Scalable Performance Prediction of Irregular Workloads in Multi-Phase Particle-in-Cell Applications

The demand for reliable performance prediction of large-scale systems is ever-increasing. With the constant need of application users for faster execution and the expectation on system administrators to efficiently allocate system resources, reliable performance prediction frameworks are crucial for identifying scalability bottlenecks which result in suboptimal performance and poor resource utilization. Such challenges in scalable performance prediction are further exacerbated by irregular applications which present dynamic workload fluctuations across processors. In this paper, we propose a novel trace-driven performance prediction framework to reliably predict the performance of a class of irregular applications that employs the Particle-in-Cell (PIC) method. The framework provides multiple advantages in terms of scalability prediction, algorithm evaluation, and performance tuning. To demonstrate scalability prediction, we predicted the performance of CMT-nek, a large-scale scientific application which employs the PIC method, on Quartz (a DOE HPC system) with an average Mean Absolute Percentage Error (MAPE) of 8.42%. For algorithm evaluation, we evaluated the efficiency of two candidate particle mapping algorithms used in CMT-nek. For performance tuning, we performed a parameter study to assess the impact of a key problem parameter in CMT-nek on application performance.