PPT-OpenFOAM on a GPU-based Heterogeneous Cluster

Author : tatyana-admore | Published Date : 2016-07-02

Rajat Phull Srihari Cadambi Nishkam Ravi and Srimat Chakradhar NEC Laboratories America Princeton New Jersey USA wwwneclabscom OpenFOAM Overview OpenFOAM stands

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OpenFOAM on a GPU-based Heterogeneous Cluster: Transcript

Rajat Phull Srihari Cadambi Nishkam Ravi and Srimat Chakradhar NEC Laboratories America Princeton New Jersey USA wwwneclabscom OpenFOAM Overview OpenFOAM stands for O pen F. Indrani Paul. 1,2. , Vignesh Ravi. 1. , Srilatha Manne. 1. , Manish Arora. 1,3. , Sudhakar Yalamanchili. 2. Nov 2013 . 1. Advanced Micro Devices, Inc.. 2. Georgia Institute of Technology. 3. University of California, San Diego. Youngho Kim. CIS665: GPU Programming. Building a Million Particle System: Lutz Latta. UberFlow - A GPU-based Particle Engine: Peter Kipfer et al.. Real-Time Particle Systems on the GPU in Dynamic Environments: Shannon Drone. ENVIRONMENT. . The High Performance Computing environment consists of high-end systems used for executing complex number crunching applications for research it has two such machines and they are,. . . Simulation. . with. . OpenFOAM. Hasret Türkeri. 9 . December. 2016. OpenFOAM. Computer. software. Solves. . equation. . for. . flows. Momentum. Pressure. Energy. …. Developed. . for. Linux . M. Zollhöfer, E. Sert, G. Greiner and J. Süßmuth. Computer Graphics Group, University Erlangen-Nuremberg, Germany. Motivation/Requirements. Intuitive modeling. Handle-based. Direct manipulation. 2. Supercomputing. The Next wave of HPC. Presented by Shel Waggener. HP Materials from Marc Hamilton. June. , . 2011. © Copyright 2011 Hewlett-Packard Development Company, L.P. . GPUs – changing the Economics of Supercomputing. Phitchaya. . Mangpo. . Phothilimthana. Jason . Ansel. Jonathan . Ragan-Kelley. Saman. . Amarasinghe. Computer Science and Artificial Intelligence Laboratory. Massachusetts Institute of Technology. Review student: Fan . Bai. Instructor: Dr. . Sushil. Prasad. 2012.03.21. Andrew . Nere. , . AtifHashmi. , and . MikkoLipasti. University of Wisconsin –Madison. IPDPS 2011. Purpose . Background. Why can be . Objective: develop technologies to improve computer performance. . . 1. Processor. Generation. Max. Clock. Speed (GHz). Max. Numberof Cores. Max. RAM. Bandwidth (GB/s). Max. Peak Floating Point (Gflop/s). Michael Robson. Parallel Programming Lab. Charm Workshop 2016. Charm++ GPU Frameworks. 2. Accelerator Overview. NVIDIA GPUs. Programmed with CUDA. 1,000s of threads. 100s GB/s bandwidth. ~16 GB of memory. Iterative Local Searches. Martin . Burtscher. 1. and Hassan Rabeti. 2. 1. Department of Computer Science, Texas State University-San Marcos. 2. Department of Mathematics, Texas State University-San Marcos. Iterative Local Searches. Martin . Burtscher. 1. and Hassan Rabeti. 2. 1. Department of Computer Science, Texas State University-San Marcos. 2. Department of Mathematics, Texas State University-San Marcos. Dept. of Computer Science and Engineering. University of South Carolina. Dr. Jason D. . Bakos. Assistant Professor. Heterogeneous and Reconfigurable Computing Lab (HeRC). This material is based upon work supported by the National Science Foundation under Grant Nos. CCF-0844951 and CCF-0915608.. Research Computing Services. Boston . University. GPU Programming. Access to the SCC. Login: . tuta#. Password: . VizTut#. GPU Programming. Access to the SCC GPU nodes. # copy tutorial materials: .

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