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。与库代中的实践状态相比，用于新的高性能计算平台的库将针对极端的并行性进行优化，并从更大的设计空间中进行搜索。虽然已经为现有的计算机提出了许多库生成器，但为新平台自动生成库的综合方法还很少。拟议中的研究旨在帮助填补这一空白。具体来说，这项工作提出了一个计算原语框架，可以系统地定义新的并行体系结构的程序设计空间，以及一个协同搜索策略，将机器学习算法与计算机体系结构建模相结合，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能力来处理复杂的搜索空间，具有卓越的准确性和架构模型提供的低开销。此外，该项目还研究了软件设计在局部性、并行性、通信和设计复杂性之间的权衡。特别是，新框架将应用于重要的内核例程，包括矩阵乘法，快速傅里叶变换（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.