SHF:Small:RUI: Optimizing Compiler Instruction Scheduling Using GPU-Accelerated Intelligent Search

编译器将用编程语言编写的程序翻译成机器代码。除了执行翻译之外，编译器还执行优化，通过提高性能和降低能耗来提高生成代码的质量。在这个研究项目中，研究人员将使用智能搜索技术和并行计算的组合来开发增强的编译器优化算法，这些算法将提高在中央处理器(CPU)和/或图形处理器(GPU)上运行的各种程序的性能。GPU的并行计算能力被广泛用于加速某些人工智能(AI)算法的实现。该项目的创新之处在于使用智能搜索技术为CPU和GPU生成更高效的代码，并利用现代并行计算来最大限度地提高这些智能搜索技术的速度。该项目的影响是开发并行智能搜索技术的算法，并使用这些算法来优化在CPU和/或GPU上运行的各种应用程序的性能。更具体地说，本研究项目解决了预分配指令调度，这是编译器优化中一个长期存在的和根本上重要的问题。当前的产生式编译器使用启发式方法解决这个问题。实验评估表明，现有的启发式算法可能会在性能和能耗方面产生质量较差的代码，特别是在为GPU编译时。该项目利用当今强大的并行计算能力，将两种特定的智能搜索技术，即分枝定界(B&amp；B)和蚁群优化(ACO)应用于该问题。在GPU上的并行计算被用来使这些计算密集型搜索技术可行。所提出的智能算法还用于为GPU本身生成更高效的代码，从而使未来的GPU能够为未来的AI程序提供更高的性能。该项目还开发了这些算法的基于GPU的并行版本，以最大限度地减少编译时间，并探索使用智能搜索可以获得的性能收益的限制。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

A compiler translates a program written in a programming language into machine code. In addition to performing the translation, the compiler also performs optimizations that improve the quality of the generated code by increasing its performance and reducing its energy consumption. In this research project, the investigators will use a combination of intelligent search techniques and parallel computing to develop enhanced compiler-optimization algorithms that will improve the performance of a wide range of programs running on the Central Processing Unit (CPU) and/or the Graphics Processing Unit (GPU). The GPU's parallel computing power is widely used to accelerate the implementations of certain Artificial Intelligence (AI) algorithms. The project's novelties are using intelligent search techniques to generate more efficient code for both CPUs and GPUs, and taking advantage of modern parallel computing to maximize the speed of these intelligent search techniques. The project's impacts are developing algorithms for parallelizing intelligent search techniques and using these algorithms to optimize the performance of a wide range of applications running on the CPU and/or the GPU. More specifically, this research project addresses pre-allocation instruction scheduling, which is a long-standing and fundamentally important problem in compiler optimizations. Current production compilers solve this problem using heuristic approaches. Experimental evaluation has shown that existing heuristics may produce poor-quality code in terms of both performance and energy consumption, especially in compiling for the GPU. This project uses today's powerful parallel computing to apply two specific intelligent search techniques, namely Branch-and-Bound (B&B) and Ant Colony Optimization (ACO), to this problem. Parallel computing on the GPU is used to make these compute-intensive search techniques feasible. The proposed intelligent algorithms are also used to generate more efficient code for the GPU itself, thus allowing future GPUs to deliver higher performance for future AI programs. The project also develops parallel GPU-based versions of these algorithms to both minimize compilation time and to explore the limits of the performance gain that can be achieved using intelligent search.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.