Sesame: A User-Transparent Optimizing Framework for Many-Core Processors

With the integration of more computational cores and deeper memory hierarchies on modern processors, the performance gap between naively parallel zed code and optimized code becomes much larger than ever before. Very often, bridging the gap involves architecture-specific optimizations. These optimizations are difficult to implement by application programmers, who typically focus on the basic functionality of their code. Therefore, in this thesis, I focus on answering the following research question: "How can we address architecture-specific optimizations in a programmer-friendly way?'' As an answer, I propose an optimizing framework for parallel applications running on many-core processors (\textit{Sesame}). Taking a simple parallel zed code provided by the application programmers as input, Sesame chooses and applies the most suitable architecture-specific optimizations, aiming to improve the overall application performance in a user-transparent way. In this short paper, I present the motivation for designing and implementing Sesame, its structure and its modules. Furthermore, I describe the current status of Sesame, discussing our promising results in source-to-source vectorization, automated usage of local memory, and auto-tuning for implementation-specific parameters. Finally, I discuss my work-in-progress and sketch my ideas for finalizing Sesame's development and testing.