EXPLORING MULTIPLE LEVELS OF PERFORMANCE MODELING FOR HETEROGENEOUS SYSTEMS
Dr. Melissa C. Smith
One of the major challenges faced by the HPC community today is user-friendly and accurate heterogeneous performance modeling. Although performance prediction models exist to fine-tune applications, they are seldom easy-to-use and do not address multiple levels of design space abstraction. Our research aims to bridge the gap between reliable performance model selection and user-friendly analysis. We propose a straightforward and accurate performance prediction suite for multi-GPGPU systems that primarily targets synchronous iterative algorithms using our synchronous iterative GPGPU execution model. The performance modeling suite addresses two levels of system abstraction: low-level where partial details of implementation are present along with system specifications; and high-level where implementation details are minimum and only high-level system specifications are known. The low-level abstraction models use statistical techniques for performance prediction whereas the high-level abstraction models are composed of existing analytical and quantitative models. Our initial validation results yield high prediction accuracy with less than 10% error rate for several tested GPGPU cluster configurations and case studies. The final goal of our research is to offer a reliable and user-friendly performance prediction framework that allows users to select an optimal performance modeling strategy for the given design goals.
Pallipuram, Vivek K., "EXPLORING MULTIPLE LEVELS OF PERFORMANCE MODELING FOR HETEROGENEOUS SYSTEMS " (2013). Graduate Research and Discovery Symposium (GRADS). 53.