2017 Oil & Gas HPC Conference

General Purpose Graphics Processing Units (GPGPUs) have made significant computational inroads in a number of industries. In the oil and gas industry, for example, sophisticated algorithms have allowed those with requirements for the processing of seismic data via Reverse Time Migration (RTM) to minimize significant I/O bottlenecks through extremely innovative use of GPUs (e.g., Liu et al., Computers & Geosciences 59 (2013) 17–23). As GPU hardware improves, so does the toolchain that programmatically exposes these accelerators for numerically intensive computations in the oil and gas industry. Buoyed by successes involving RTM, and other applications within the industry, it is evident that organizations seek opportunities to further exploit GPUs. In addition to exposing architectural specifics like accelerators-per-GPU-socket and CUDA-core-count, there is a clear interest in regarding GPU memory as a bona fide resource. And owing to the simultaneous TOP500 plus Green500 appeal of high-density GPU configurations, there is clear interest in exposing aggregations of these powerful accelerators as resources that can be addressed by workload-management software - with an awareness that includes topological considerations like GPU memory, sockets, chassis and interconnect fabrics. Resource maps (RSMAPS) were added to Univa Grid Engine to better account for the depth and breadth of capabilities available from GPUs, and to account for their aggregated capabilities. Whereas RSMAPS are able to address numerous resource requirements involving GPUs, it is clear that this remains an area where additional capabilities need to be accounted for by the workload manager. By systematically reviewing increasingly sophisticated use cases for the uptake of GPUs as diverse and capable resources for computational HPC in the oil and gas industry, opportunities and challenges for abstracting these accelerators as workload-managed resources will be shared.