High Performance Computing

We present an approach to enhancing the optimization process in a polyhedral compiler by introducing compile-time versioning, i.e., the production…

We present a fast algorithm for transforming loop nests represented in the polyhedral model, which have been placed onto a…

The Theory of Bottleneck Structures is a recently developed framework for studying the performance of data networks. It describes how…

Large data sets can contain patterns at multiple scales (spatial, temporal, etc.). In practice, it is useful for data exploration…

In the post-Moore’s Law era, the quest for exascale computing has resulted in diverse hardware architecture trends, including novel custom…

CANDECOMP/PARAFAC (CP) tensor decomposition is a popular unsupervised machine learning method with numerous applications. This process involves modeling a high–dimensional,…