Professor at UPitt runs 40,000 CPU MATLAB® job in Google Cloud to reduce simulation time from six months to two days

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Mahrad Sharifvaghefi, an Assistant Professor of Economics at the University of Pittsburgh, studies applied macroeconomics and specializes in using machine learning to conduct large-scale statistics.

To optimize his experiment he needed to run at least 1,000 simulations with 300 different designs. Sharifvaghefi et al. estimated that it would take more than six months to yield high-confidence results if they continued at this pace, and they couldn’t be sure they’d always have access to the time and computing capacity they needed on premises.

Enterprise Architect Brian Pasquini works to solve exactly these kinds of problems for Pittsburgh’s research scientists. He and his colleague Sandra E. Brandon, Strategic Research Liaison in the Office of the CIO, looked for a solution where he could scale up on demand and on his schedule. This led the Pitt team to Google Cloud and Techila Technologies, an approved partner on Google Cloud Marketplace.

It worked. With Google Cloud and Techila, Sharifvaghefi ran the simulations on 40,000 spot vCPUs. “This would have been impossible on shared campus resources,” he says. Pasquini agrees, adding that deploying Google Cloud and Techila was like “providing an easy button.”

Brandon says, “we want to encourage research that transcends disciplines and impacts the whole community. This is about speed to science–demonstrating the capacity of cloud computing for scaling and just-in-time resources. If we rely only on traditional infrastructure, by the time we design, purchase, and build it, it could very well be obsolete.”

Using preemptible instances was also more cost-effective: the team estimates that the project cost less than a third of what it would have cost on premises.

“This workflow could transform statistical methodology for economics and beyond.”, Mahrad Sharifvaghefi, Assistant Professor of Economics, University of Pittsburgh

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