Exploiting In-Memory Processing Capabilities for Density Functional Theory Applications

Processing-in-memory (PIM) is an approach to address the data transport challenge in future HPC architectures and various designs have been explored in the past. Despite, it remains unclear how scientific applications could efficiently exploit massively-parallel HPC architectures integrating PIM modules. In this paper we address this question for material science applications for which we ported relevant kernels to the Active Memory Cube architecture developed by IBM Research.