Performance Analysis of Tile Low-Rank Cholesky Factorization Using PaRSEC Instrumentation Tools - Cao, Qinglei; Pei, Yu; Herault, Thomas; Akbudak, Kadir; Mikhalev, Aleksandr; Bosilca, George; Ltaief, Hatem; Keyes, David; Dongarra, Jack (2019 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools), 2019)
This paper highlights the necessary development of new instrumentation tools within the PaRSE task-based runtime system to leverage the performance of low-rank matrix computations. In particular, the tile low-rank (TLR) Cholesky factorization represents one of the most critical matrix operations toward solving challenging large-scale scientific applications. The challenge resides in the heterogeneous arithmetic intensity of the various computational kernels, which stresses PaRSE's dynamic engine when orchestrating the task executions at runtime. Such irregular workload imposes the deployment of new scheduling heuristics to privilege the critical path, while exposing task parallelism to maximize hardware occupancy. To measure the effectiveness of PaRSE's engine and its various scheduling strategies for tackling such workloads, it becomes paramount to implement adequate performance analysis and profiling tools tailored to fine-grained and heterogeneous task execution. This permits us not only to provide insights from PaRSE, but also to identify potential applications' performance bottlenecks. These instrumentation tools may actually foster synergism between applications and PaRSE developers for productivity as well as high-performance computing purposes. We demonstrate the benefits of these amenable tools, while assessing the performance of TLR Cholesky factorization from data distribution, communication-reducing and synchronization-reducing perspectives. This tool-assisted performance analysis results in three major contributions: a new hybrid data distribution, a new hierarchical TLR Cholesky algorithm, and a new performance model for tuning the tile size. The new TLR Cholesky factorization achieves an 8X performance speedup over existing implementations on massively parallel supercomputers, toward solving large-scale 3D climate and weather prediction applications.