MoHA: a composable system for efficient in-situ analytics on heterogeneous HPC systems

Heterogeneous, dense computing architectures consisting of several accelerators, such as GPUs, attached to general-purpose CPUs are now integral High-Performance Computing (HPC) systems. However, these architectures pose severe memory and I/O constraints to computations involving in-situ analytics. This paper introduces MoHA, a framework for in-situ analytics that is designed to efficiently use the limited resources available on heterogeneous platforms. MoHA achieves this efficiency through the extensive use of bitmaps as a compressed or summary representation of simulation outputs. Our specific contributions in this paper include the design of bitmap generation and storage methods suitable for GPUs, the design and efficient implementation of a set of key operators for MoHA, and demonstrations of how several real queries on real datasets can be implemented using these operators. We demonstrate that MoHA reduces I/O transfer as well as overall processing time when compared to a baseline that does not use compressed representations.