AUTOMATIC CODE GENERATION OF HIGH PERFORMANCE FOURIER TRANSFORM LIBRARIES

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 本研究的目标是在超级计算机平台上实现离散傅立叶变换（DFT）库的自动代码生成和优化。用于各种平台的DFT（及其变体）的大多数当前高性能并行实现通常是手动编码和手动调整的，以用于单个变换/大小，并且被约束到特定的数据格式。我们的目标是自动化生成高性能DFT库的过程，用于超级计算平台的各种问题规范和场景。我们的方法使用算法操作在一个高层次的抽象，基于一组数学规则，这使得自动化的库生成。傅立叶变换是一种计算内核，广泛用于科学计算，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.