| NVIDIA CUDA: Week in Review |
| Wednesday, 21 September 2011 | ||
NVIDIA CUDA: Week in Review - Issue 62CUDA SPOTLIGHTGPU-Accelerated Multi-Phase Flow Simulations 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. Mehdi: The focus of my research is primarily on multi-phase flows and free-surface flows with phase change. We develop computational algorithms and flow solvers, and use them to study industrial and research applications that involve multi-phase flows.
NVIDIA: What role does GPU computing play in your work? Mehdi: Our numerical algorithm for solving the fluid flow equations involves a step in which we solve a large system of linear equations to compute the pressure field. That single step can take from 50 to 99.9 percent of the total simulation time! As we increase the number of grid points in our simulations, the pressure solution step takes a larger percentage of the total simulation time. 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. Read the complete interview here: https://developer.nvidia.com/cuda-spotlights CUDA DEVELOPER NEWS Test Drive the Tesla MD SimCluster: Try the new Tesla Molecular Dynamics SimCluster, which is preconfigured to accelerate AMBER or NAMD. All you need to do to start a simulation is load your models. To test drive, visit https://bit.ly/simcluster_cuda or email mdsimcluster (at) nvidia.com. New CUDA Centers: Congrats to all the new CUDA Research Centers and CUDA Teaching Centers, including Carnegie Mellon/Silicon Valley and the University of Edinburgh. For complete list of newcomers, see: https://blogs.nvidia.com/2011/09/new-cuda-research-and-teaching-centers-announced/ MATLAB Acceleration on GPUs: Using MATLAB for GPU computing is ideal for engineers and scientists who want to take advantage of GPUs without low-level C or Fortran programming. Register for a free webinar on October 27 to learn how CUDA-enabled GPUs can help accelerate MATLAB computations: https://bit.ly/ondzzC. For more info about GPU computing in MATLAB, see: https://bit.ly/pOiZg6. Mini-Supercomputer from HP: The new GPU Starter Kit from HP provides a ready-to-use GPU computing cluster, straight out of the box. It combines eight HP ProLiant SL390 G7 servers (based on 24 Tesla M2070 GPUs) with 16 CPUs. Visit www.nvidia.com/docs/IO/43399/NV-Tesla-Starter-Kit.pdf or email Hpc-sales (at) hp.com. Read CUDA: Week in Review on the web at: https://developer.nvidia.com/cuda-news
Related Articles:
|
||
Examples include materials processing (thermal spray coating and casting), energy systems (both renewable and conventional), and environmentally friendly or "green" refrigeration systems.
