by Angela Guess
According to a new press release, “IBM and Stone Ridge Technology today announced a performance milestone in reservoir simulation designed to help improve efficiency and lower the cost of production. Working with NVIDIA, the companies shattered previous published results using one-tenth the power and 1/100th of the space. The news demonstrates the ability of NVIDIA GPUs to simulate one billion cell models in a fraction of the published time, while delivering 10x the performance and efficiency than legacy CPU codes. The breakthrough achievement used 60 Power processors and 120 GPU accelerators shattering the previous supercomputer record which used over a 700,000 processors. The results aim to transform the price and performance for business critical High Performance Computing (HPC) applications for simulation and exploration.”
The release goes on, “Energy companies use reservoir modeling to predict the flow of oil, water and natural gas in the subsurface of the earth before they drill to figure out how to more efficiently extract the most oil. A billion-cell simulation is extremely challenging due to the level of detail it seeks to provide. Stone Ridge Technology, maker of the ECHELON petroleum reservoir simulation software, completed the billion-cell reservoir simulation in 92 minutes using 30 IBM Power Systems S822LC for HPC servers equipped with 60 POWER processors and 120 NVIDIA® Tesla™ P100 GPU accelerators. ‘This calculation is a very salient demonstration of the computational capability and density of solution that GPUs offer. That speed lets reservoir engineers run more models and ‘what-if’ scenarios than previously so they can have insights to produce oil more efficiently, open up fewer new fields and make responsible use of limited resources’ said Vincent Natoli, President of Stone Ridge Technology. ‘By increasing compute performance and efficiency by more than an order of magnitude, we’re democratizing HPC for the reservoir simulation community’.”
Read more at PR Newswire.
Photo credit: IBM