NSF Award
2211353
This project aims to significantly increase the performance of application programs ranging from those running on mobile devices to those utilized by ever-growing data centers. The project's impacts are potentially enabling better performing systems at all scales, hence directly and positively impacting society. The efficiency of most applications is still primarily affected by how fast branch instructions can be processed, which in turn decides how many machine instructions can be executed by a processor per clock cycle. Advancing the state of the art in this problem domain requires a paradigm shift and a completely novel approach to system design. The project's novelty lies in that it proposes to investigate a mechanism that has significantly better potential than what incremental improvements based on available techniques can provide. The team will develop the foundations of such an approach. Developed toolsets and the design infrastructure will be open source and will have an impact on both education and research.<br/><br/>The project's novel approach can be summarized as applying the principle of vectorization to the instruction space, referred to as Vectorized Instruction Space (VIS). VIS relies on an executable dynamic single-assignment form implemented at the machine-instruction level. Vectorization of the instruction space converts control dependences to data dependences using a process that has significantly less overhead than traditional predication, and it naturally combines controlled multi-path execution with speculation. With this new paradigm, it will be possible to eliminate a significant fraction of difficult-to-predict branch instructions, and efficiently unroll loops with unknown iteration counts. The project involves the development of the foundations of the paradigm both at the microarchitecture and the compiler level, targeting both superscalar and VLIW architectures.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
