PPT-GPU Programming using BU Shared Computing Cluster

Author : rayfantasy | Published Date : 2020-11-06

Scientific Computing and Visualization Boston University GPU Programming GPU graphics processing unit Originally designed as a graphics processor Nvidias GeForce

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GPU Programming using BU Shared Computing Cluster: Transcript

Scientific Computing and Visualization Boston University GPU Programming GPU graphics processing unit Originally designed as a graphics processor Nvidias GeForce 256 1999 first GPU. . Acknowledgement: the lecture materials are based on the materials in NVIDIA teaching center CUDA course materials, including materials from Wisconsin (. Negrut. ), North Carolina Charlotte (. Wikinson. ITS Research Computing. Lani. Clough, Mark Reed. markreed@unc.edu. . Objectives. Introductory. level MATLAB course for people who want to learn . parallel and GPU computing . in MATLAB.. Help participants . Rajat Phull, . Srihari. Cadambi, Nishkam Ravi and Srimat Chakradhar. NEC Laboratories America. Princeton, New Jersey, USA.. www.nec-labs.com. OpenFOAM Overview. OpenFOAM stands for:. ‘. O. pen . F. Condor Week 2012. Bob Nordlund. Grid Computing @The Hartford…. Using Condor in our production environment since 2004. Computing Environment. Two pools (Hartford, CT and Boulder, CO). Linux central managers and schedulers. Gareth James Hani Adhami. Senior Consultant Senior Consultant. WSV423. Agenda. Overview of Cluster Shared Volumes (CSV). What’s changed in Windows Server 2012. New CSV Architecture. P. erformance Enhancements. Add GPUs: Accelerate Science Applications. © NVIDIA 2013. Small Changes, Big Speed-up. Application Code. . GPU. C. PU. Use GPU to Parallelize. Compute-Intensive Functions. Rest of Sequential. CPU Code. Computational Lab. Scott Sammons. Kevin Tang. Chandni Desai. Sequencing Lab. Mike Frace. Missy Olsen-Rasmussen. Marina Khristova. Lori Rowe. Pacific Biosciences SMRT sequencer. . Ion Torrent Personal Gene Machine. Leiming Yu, Fanny Nina-Paravecino, David Kaeli, Qianqian Fang. 1. Outline. Monte Carlo . eXtreme. GPU Computing. MCX. in OpenCL. Conclusion. 2. Monte Carlo . eXtreme. Estimates the 3D light (. fluence. ITS Research Computing. Mark Reed . Objectives. Learn why computing with accelerators is important. Understand accelerator hardware. Learn what types of problems are suitable for accelerators. Survey the programming models available. A Tale of Two Cities: GPU Computing and Machine Learning Dr. Xiaowen Chu Department of Computer Science, Hong Kong Baptist University Outline 2 Some Stories of “Two Cities” Evolution of CPUs/ Lecture 7. Last Week. Memory optimizations using different GPU caches. Atomic operations. Synchronization with __. syncthreads. (). Week 3. Advanced GPU-accelerable algorithms. “Reductions” to parallelize problems that don’t seem intuitively parallelizable. Waters. Introduction to GPU Computing. Brief History of GPU Computing. Technical Issues. Social Impact. Marketing and Ethical . Issues. Project Management. Conclusion. Table of Contents. A . GPU is . Martin Burtscher. Department of Computer Science. High-end CPU-GPU Comparison. . Xeon 8180M. . Titan V. Cores 28 5120 (+ 640). Active threads 2 per core 32 per core. Frequency 2.5 (3.8) GHz 1.2 (1.45) GHz. Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2014. Lectures. Monday. 6-9pm. Moore 212. Fall. and . Spring. 2012 lectures were recorded. Attendance is required for guest lectures. Image from .

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