As the US Department of Energy (DOE) computing facilities began deploying petascale systems in 2008, DOE was already setting its sights on exascale. In that year, DARPA published a report on the feasibility of reaching exascale. The report authors identified several key challenges in the pursuit of exascale including power, memory, concurrency, and resiliency. That report informed the DOE's computing strategy for reaching exascale. With the deployment of Oak Ridge National Laboratory's Frontier supercomputer, we have officially entered the exascale era. In this paper, we discuss Frontier's architecture, how it addresses those challenges, and describe some early application results from Oak Ridge Leadership Computing Facility's Center of Excellence and the Exascale Computing Project.

The Oak Ridge Leadership Computing Facility (OLCF) has deployed multiple large-scale parallel file systems (PFS) to support its operations. During this process, OLCF acquired significant expertise in large-scale storage system design, file system software development, technology evaluation, benchmarking, procurement, deployment, and operational practices. Based on the lessons learned from each new PFS deployment, OLCF improved its operating procedures, and strategies. This paper provides an account of our experience and lessons learned in acquiring, deploying, and operating large-scale parallel file systems. We believe that these lessons will be useful to the wider HPC community.

High performance computing resources attract a wide range of computational users and corresponding job widths and lengths. For example, on the petaflop Cray XT5 machine, Kraken, users submit jobs ranging from a few hundred cores (capacity computing) to over hundred thousand cores (capability computing). Traditionally it has been difficult to maintain high utilization while juggling such a diverse job mix. This paper explores four unique approaches to achieve our scheduling goals of maximizing utilization on four distinct resources at the National Institute for Computational Sciences. The resources include the petaflop machine, Kraken, Athena - a 166 TF Cray XT4, a 4 TB shared memory NUMA machine called Nautilus, and a GPU cluster called Keeneland.