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Echelon Code on Minksy Servers Sets Record for Oil & Gas Simulation

IBM and Stone Ridge Technology have announced a new performance milestone in reservoir simulation that will improve efficiency and lower the cost of production.

Working with Nvidia, the companies reported that they had beat previous results using one-tenth the power and 1/100th of the space by employing GPUs alongside a GPU optimized code from Stone Ridge Technology called ECHELON.

The simulation represents a significant milestone for oil and gas simulation as the project demonstrated the ability of Nvidia GPUs to simulate one billion cell models in a fraction of the time while delivering 10x the performance and efficiency of legacy CPU codes. The simulation used 60 processors and 120 accelerators easily beating the previous supercomputer record which was set using 1000s of processors.

This milestone calculation illuminates the advantages of the Minsky OpenPower GPU architecture.’ said Sumit Gupta, IBM vice president, High-Performance Computing, and Analytics. “The bottom line is that by running ECHELON on Minsky, users can achieve faster run-times using a fraction of the hardware. One recent effort used more than 700,000 processors in a server installation that occupies nearly half a football field. Stone Ridge did this calculation on two racks of IBM machines that could fit in the space of half a ping-pong table.”

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 extract the most oil with the least financial and environmental risk. A billion-cell simulation is extremely challenging due to the level of detail it seeks to provide. Stone Ridge Technology, the maker of the ECHELON petroleum reservoir simulation software, completed the billion-cell reservoir simulation in 92 minutes using 30 IBM OpenPower servers equipped with 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 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,” added Natoli.

This latest advance challenges misconceptions that GPUs could not be efficient on complex application codes like reservoir simulators and are better suited to simple, more naturally parallel applications such as seismic imaging.

The energy industry was among the first to adopt GPUs for numerical modeling, using them to accelerate seismic processing,” said Marc Hamilton, vice president of Solutions Architecture and Engineering at Nvidia. “They are now making a powerful impact on reservoir simulation, and we expect this to drive further efforts to utilize GPUs for other computationally intense workflows in the oil and gas sector.”

This story appears here as part of a cross-publishing agreement with Scientific Computing World.

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