Collaborative Research: SHF: Medium: Vectorized Instruction Space (VIS)

该项目旨在显著提高应用程序的性能，从运行在移动设备上的应用程序到不断增长的数据中心所使用的应用程序。项目的影响可能会在所有尺度上实现更好的系统，从而直接和积极地影响社会。大多数应用程序的效率仍然主要受分支指令处理速度的影响，而分支指令的处理速度又决定了处理器每个时钟周期可以执行多少条机器指令。在这个问题领域中推进技术的发展需要范式的转变和一种全新的系统设计方法。这个项目的新颖之处在于，它建议研究一种机制，这种机制比基于现有技术的增量改进具有更好的潜力。该团队将开发这种方法的基础。开发的工具集和设计基础设施将是开源的，并将对教育和研究产生影响。该项目的新方法可以概括为将向量化原理应用于指令空间，称为向量化指令空间（VIS）。VIS依赖于在机器指令级别实现的可执行动态单赋值形式。指令空间的向量化使用一个比传统预测具有更少开销的过程将控制依赖转换为数据依赖，并且它自然地将受控的多路径执行与推测结合起来。有了这个新范例，就有可能消除很大一部分难以预测的分支指令，并有效地展开带有未知迭代计数的循环。该项目涉及在微体系结构和编译器级别开发范式的基础，目标是超标量和VLIW体系结构。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.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.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.