By

ii The current trend in High-Performance Computing (HPC) is to extract concurrency from clusters that include heterogeneous resources such as General Purpose Graphical Processing Units (GPGPUs) and Field Programmable Gate Array (FPGAs). Although these heterogeneous systems can provide substantial performance for massively parallel applications, much of the available computing resources are often under-utilized due to inefficient application mapping, load balancing, and tuning. While several performance prediction models exist to efficiently tune applications, they often require significant computing architecture knowledge for reliable prediction. In addition, they do not address multiple levels of design space abstraction and it is often difficult to choose a reliable prediction model for a given design. In this research, we develop a multi-level suite of performance prediction models for heterogeneous systems that primarily targets Synchronous Iterative Algorithms (SIAs). The modeling suite aims to produce accurate and straightforward application runtim