PPT-How do you know your GPU or manycore program is correct?

Prof Miriam Leeser Department of Electrical and Computer Engineering Northeastern University Boston MA melcoeneuedu Typical Radar Processing httpwwwcodesourcerycomvsiplplusplusssarhttpwwwcodesourcerycomvsiplplusplusssahttpwwwcodesourcerycomvsiplplusplusssarwhitepaperpdfrwhitepaperpdfwhitepaperpdf. . 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. Chris Rossbach, Microsoft Research. Jon Currey, Microsoft Research. Emmett . Witchel. , University of Texas at Austin. HotOS. 2011. Lots of GPUs. Must they be so hard to use?. We need dataflow…. GPU Haiku . Alan . Gray. EPCC . The University of Edinburgh. Outline. Why do we want/need accelerators such as GPUs?. Architectural reasons for accelerator performance advantages . Latest accelerator Products. NVIDIA and AMD GPUs. ITK v4 . . w. inter . meeting. Feb 2. nd. 2011. Won-. Ki. . Jeong. , . Harvard University. (. wkjeong@seas.harvard.edu. ). Overview. Introduction. Current status in GPU ITK v4. GPU managers. GPU image. Network Interfaces . for In-Memory Rack-Scale Computing. Alexandros Daglis. ,. . Stanko. . Novakovic. , . Edouard. . Bugnion. , . Babak. . Falsafi. , Boris Grot. In-Memory Computing for High Performance. 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. using BU Shared Computing Cluster. Scientific Computing and Visualization. Boston . University. GPU Programming. GPU – graphics processing unit. Originally designed as a graphics processor. Nvidia's. Sathish. . Vadhiyar. Parallel Programming. GPU. Graphical Processing Unit. A single GPU consists of large number of cores – hundreds of cores.. Whereas a single CPU can consist of 2, 4, 8 or 12 cores. 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. 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). Andy Luedke. Halo Development Team. Microsoft Game Studios. Why do Histogram Analysis?. Dynamically adjust post-processing settings based on rendered scene content. Drive tone adjustments by discovering intensity levels and adjusting . Hui. Li. Geoffrey Fox. Research Goal. provide . a uniform . MapReduce programming . model that works . on HPC . Clusters or . Virtual Clusters cores . on traditional Intel architecture chip, cores on . Current Goal(s):. Generate . stacktraces. of GPU executions and associate GPU call chains with CPU call graphs. Particular interest on how to determine call chains when in-lined GPU functions are used.