 CUDA Spotlight: GPU-Accelerated Multi-Phase Flow Simulations Read more CUDA in Action Spotlights This week's Spotlight is on Dr. Mehdi Raessi, Assistant Professor in the Department of Mechanical Engineering at University of Massachusetts-Dartmouth. NVIDIA: Mehdi, tell us about your research. NVIDIA: Explain “multi-phase flow” in layman’s terms. NVIDIA: What role does GPU computing play in your work? To speed up this task, my graduate student, Stephen Codyer, ported the pressure calculations to the GPU. His tests show that the GPU-accelerated solver can run a 3D simulation with over 28 million grid points 15 times faster (compared to performing the same calculation on the CPU). My colleague, Prof. Gaurav Khanna, from our Physics Department, helped us a lot in this project and shared his extensive experience in GPU computing. We minimized the communications between the GPU and the host CPU and achieved much better speedups than what open-source GPU linear algebra libraries offer. NVIDIA: What are the benefits of using CUDA? While the limited on board memory on a GPU may deter some compared to the potentially unlimited memory sizes of a cluster, a single Tesla C2070 was able to suffice for most of our computational needs, because we port only a portion (usually less than 6 GB) of our calculations to the GPU. We are now exploring using multiple GPUs for our calculations. I would like to add that with the 15X speedup that I mentioned earlier, we can of course run simulations faster or increase the size of our simulations; but, just as importantly, a 15X speedup using CUDA means that a computational scientist could perhaps replace a common small-scale CPU cluster with a single Tesla workstation. This would be a significant saving in costs (both in terms of procurement and power-consumption). This could also potentially tie in very well with various “green” initiatives because GPU computing is extremely effective in terms of performance-delivered per watt-consumed. NVIDIA: Tell us about the Scientific Computing Group at UMass-Dartmouth. The members of this group have collaborations on a variety of applications that include complex flows in energy devices (especially renewable energy), material processing and mechanics, astrophysics, etc. I am pleased to mention that our Scientific Computing Group recently acquired and installed a GPU cluster from IBM, which has 60 Tesla M2050 GPUs. We are currently planning to add data visualization capability to the group. NVIDIA: What future applications can you envision in your research area? We have begun projects that are targeting these issues. With GPU-accelerated computational tools, we are now able to study much larger problems at a level of detail that was not feasible before. These simulations can lead to new energy devices that are more efficient and have less environmental impact. I believe the capability to run faster and faster simulations with GPUs will one day enable us to predict, respond to and mitigate catastrophic events. Bio: He is the recipient of the Industrial Research and Development Fellowship from the Government of Canada, Postdoctoral Fellowship from NASA-Stanford University’s Center for Turbulence Research, and Early Career Teaching Award from the University of Toronto. Relevant Links: Contact Info: |
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