CSR--AES: Machine Learning Based Library Generation Techniques for Multi-core Processors and GPU's

CSR-AES：基于机器学习的多核处理器和GPU库生成技术本研究计划的目标是为新兴的高性能计算机平台-多核处理器和GPU开发自适应库生成和优化技术。与库生成的实践状态相比，新的高性能计算平台的库将针对极端并行水平进行优化，并从更大的设计空间中搜索。虽然许多库生成器已被提议用于现有计算机，但很少有工作致力于一种全面的方法来自动生成新平台的库。具体而言，这项工作提出了一个计算原语的框架，可以系统地定义新的并行架构的程序设计空间，以及一个协同搜索策略，将机器学习算法与计算机架构建模相结合，以有效地搜索多核处理器和GPU上的最佳形式的库例程。新的代码生成框架结合了机器学习的灵活性和处理复杂搜索空间的能力，以及架构模型提供的上级准确性和低开销。此外，本计画也研究软体设计在局部性、平行性、通讯性与设计复杂度之间的折衷。特别是，新的框架将应用于重要的内核例程，包括矩阵乘法，快速傅立叶变换（FFT）和排序，以便新的高性能计算平台的用户可以轻松地利用平台提供的高性能。

CSR-AES: Machine Learning Based Library Generation Techniques for Multi-coreProcessors and GPUsThe goal of this research program is to develop adaptive library generation and optimization technology for emerging high-performance computer platforms - Multi-core processors and GPUs. Compared to the state-of-the-practice in library generations, the libraries for the new high-performance computation platforms will be optimized for extreme level of parallelism and be searched from a much larger design space. While many library generators have been proposed for existing computers, there has been little work towards a comprehensive approach to automatically generate libraries for the new platforms. The proposed research is meant to help fill this gap.Specifically, this work proposes a framework of computation primitives that can systematically define the program design space for the new parallel architectures, and a synergistic search strategy that couples machine learning algorithms with computer architecture modeling to efficiently search the best form of library routines on multi-core processors and GPUs. The new code generation framework combines the flexibility and the capabilities of machine learning to handle complex search space, with the superior accuracy and the low overhead that architecture models provide. Furthermore, this project studies the software design tradeoffs between locality, parallelism, communication and design complexity. In particular, the new framework will be applied to important kernel routines, including matrix multiplication, Fast Fourier Transform (FFT), and sorting, so that users of the new high-performance computing platforms can readily exploit the high performance provided by the platforms.