PPT-CS 179: GPU Computing

Lecture 2 more basics Recap Can use GPU to solve highly parallelizable problems Straightforward extension to C Separate CUDA code into cu and cuh files and compile with nvcc to create object files o files. Goals for Rest of Course. Learn how to program massively parallel processors and achieve. high performance. functionality and maintainability. scalability across future generations. Acquire technical knowledge required to achieve the above goals. ITK v4 . . summer . meeting. June 28, 2011. Won-. Ki. . Jeong. Harvard University. Overview. Introduction. Current status. Examples. Future work. 2. GPU Acceleration. GPU as a fast co-processor. Massively parallel. mei. W. . Hwu. , 2007-2012 . University . of Illinois, Urbana-Champaign. 1. CS/EE 217. GPU Architecture and Parallel . Programming. Project . Kickoff. Two flavors. Application. Implement/optimize an realistic application on GPGPUs. 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. Lecture 5: GPU Compute . Architecture. 1. Last time.... GPU Memory System. Different kinds of memory pools, caches, . etc. Different optimization techniques. 2. Warp Schedulers. Warp schedulers find a warp that is ready to execute its next instruction and available execution cores and then start execution. Host-Device Data Transfer. 1. Moving data is slow. So far we’ve only considered performance when the data is already on the GPU. This neglects the slowest part of GPU programming: getting data on and off of GPU. 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. Topics. Non-numerical algorithms. Parallel breadth-first search (BFS). Texture memory. GPUs – good for many numerical calculations…. What about “non-numerical” problems?. Graph Algorithms. Graph Algorithms. 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. Topics. Non-numerical algorithms. Parallel breadth-first search (BFS). Texture memory. GPUs – good for many numerical calculations…. What about “non-numerical” problems?. Graph Algorithms. Graph Algorithms. Week 3. Goals:. More involved GPU-. accelerable. algorithms. Relevant hardware quirks. CUDA libraries. Outline. GPU-accelerated:. Reduction. Prefix sum. Stream compaction. Sorting (quicksort). Reduction. CS 179: GPU Programming Lecture 7 Week 3 Goals: Advanced GPU- accelerable algorithms CUDA libraries and tools This Lecture GPU- accelerable algorithms: Reduction Prefix sum Stream compaction Sorting (quicksort) 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: . Jerry Adams. 1. , Bradley Hittle. 2. , Eliot Prokop. 3. , . Ronny Antequera. 3. , Dr.Prasad Calyam. 3. University of Hawaii-West Oahu. 1. , . The . Ohio State University. 2. , University of Missouri-Columbia.