Efficient Runtime Support for a Partitioned Global Logical Address Space

Abstract

Many HPC applications have successfully applied Partitioned Global Address Space (PGAS) parallel programming models to efficiently manage shared data that is distributed across multiple nodes in a distributed memory system. However, while the flat addressing model provided by PGAS systems is effective for regular array data, it renders such systems difficult to use with loosely-structured or sparse data. This work proposes a logically addressed PGLAS model that naturally supports a variety of data models through the automatic mapping of an application-defined key space onto the underlying distributed memory system. We present an efficient implementation of the PGLAS model built atop a parallel distributed hash table (PDHT) and demonstrate that this model is amenable to offloading using the Portals 4 network programming interface. We demonstrate the effectiveness of PDHT using representative applications from the computational chemistry and genomics domains. Results indicate that PGLAS models such as PDHT provide a promising new method for parallelizing applications with non-regular data.