Metagenomic workflows involve studying uncultured microorganisms directly from the environment. These environmental samples when processed by modern sequencing machines yield large and complex datasets that exceed the capabilities of metagenomic software. The increasing sizes and complexities of datasets make a strong case for exascale-capable metagenome assemblers. However, the underlying algorithmic motifs are not well suited for GPUs. This poses a challenge since the majority of next-generation supercomputers will rely primarily on GPUs for computation. In this paper we present the first of its kind GPU-Accelerated implementation of the local assembly approach that is an integral part of a widely used large-scale metagenome assembler, MetaHipMer. Local assembly uses algorithms that induce random memory accesses and non-deterministic workloads, which make GPU offloading a challenging task. Our GPU implementation outperforms the CPU version by about 7x and boosts the performance of MetaHipMer by 42% when running on 64 Summit nodes.

Tightly-coupled HPC systems have rigid memory allocation and can result in expensive memory resource underutilization. As novel memory and network technologies mature, disaggregated memory systems are becoming a promising solution for future HPC systems. It allows workloads to use the available memory of the entire system. In this paper, we propose a design framework to explore the disaggregated memory system design space. The framework incorporates memory capacity, network bandwidth, and local and remote memory access ratio, and provides an intuitive approach to guide machine configurations based on technology trends and workload characteristics. We apply our framework to analyze eleven workloads from five computational scenarios, including AI training, data analysis, genomics, protein, and traditional HPC. We demonstrate the ability of our methodology to understand the potential and pitfalls on a disaggregated memory system and motivate machine configurations. Our methodology shows that the 10 out of our 11 applications/workflows can leverage disaggregated memory without affecting performance.