AUTOMATIC CODE GENERATION OF HIGH PERFORMANCE FOURIER TRANSFORM LIBRARIES

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 这项研究的目标是为超级计算机平台自动生成和优化离散傅里叶变换(DFT)库。目前用于各种平台的大多数高性能DFT(及其变体)并行实现通常是手工编码和手动调优的，以针对单个转换/大小工作，并且被限制为特定的数据格式。我们的目标是自动化为超级计算平台的各种问题规范和场景生成高性能DFT库的过程。我们的方法基于一组数学规则，在高抽象级别使用算法操作，这允许库生成的自动化。傅里叶变换是一种在科学计算中广泛使用的计算内核，包括生物医学图像处理。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The objective of this research is the automatic code generation and optimization of discrete Fourier transform (DFT) libraries for supercomputer platforms. Most current high performance parallel implementations of the DFT (and its variants) for various platforms are typically hand-coded and manually tuned to work for a single transform/size, and are constrained to specific data formats. Our goal is to automate the process of generating high-performance DFT libraries for a wide variety of problem specifications and scenarios for supercomputing platforms. Our approach uses algorithmic manipulation at a high level of abstraction, based on a set of mathematical rules, which allows automation of library generation. The Fourier transform is a compute kernel that is used extensively in scientific computing, including biomedical image processing.