PDF-Fast Deformable Registration on the GPU A CUDA Implementation of Demons Pnar Muyan Ozcelik

Author : yoshiko-marsland | Published Date : 2014-11-30

Owens University of California Davis One Shields Avenue Davis CA 95616 pmuyan jowens ucdavisedu Junyi Xia Sanjiv S Samant University of Florida Gainesville FL 32611

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Fast Deformable Registration on the GPU A CUDA Implementation of Demons Pnar Muyan Ozcelik: Transcript

Owens University of California Davis One Shields Avenue Davis CA 95616 pmuyan jowens ucdavisedu Junyi Xia Sanjiv S Samant University of Florida Gainesville FL 32611 junyixia samant u64258edu Abstract In the medical imaging 64257eld we need fast defo. . 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. CUDA Platform. CUDA Parallel Computing Platform. . Hardware . . . Capabilities. GPUDirect. SMX. Dynamic Parallelism. HyperQ. Programming . Approaches. Libraries. “Drop-in” Acceleration. 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. © Dan Negrut, . 2012. UW-Madison. Dan Negrut. Simulation-Based Engineering Lab. Wisconsin Applied Computing Center. Department of Mechanical Engineering. Department of . Electrical and Computer Engineering. Martin Burtscher. Department of Computer Science. CUDA Optimization Tutorial. Martin Burtscher. burtscher@txstate.edu. http://www.cs.txstate.edu/~burtscher/. Tutorial slides. http://www.cs.txstate.edu/~burtscher/tutorials/COT5/slides.pptx. Introduction to Programming Massively Parallel Graphics processors. Andreas . Moshovos. moshovos@eecg.toronto.edu. ECE, Univ. of Toronto. Summer 2010. Some slides/material from:. UIUC course by . Wen. Monday, Feb . 21. Prof. Kristen . Grauman. UT-Austin. Recap so far:. Grouping and Fitting. Goal: move from array of pixel . values (or filter outputs) . to a collection of regions, objects, and shapes.. 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 . . CMS experiment. Felice Pantaleo. EP-CMG-CO. 1. Outline. Physics and Technologic . Motivations. Tracking. HGCAL clustering. CUDA Translation. Conclusion. 2. Physics and Technologic Motivations. 3. Physics Motivation. Defines much more than an API. A language . Hardware Specifications. PA0. Let’s look into your first assignment and figure some things out.. HELLOCUDA.CU. HELLOCUDA.CU. Pointers to GPU land. dev_a. Scientific Computing and Visualization. Boston . University. GPU Programming. GPU – graphics processing unit. Originally designed as a graphics processor. Nvidia's. GeForce 256 (1999) – first GPU. Agenda. Text book / resources. Eclipse . Nsight. , NVIDIA Visual Profiler. Available libraries. Questions. Certificate dispersal. (Optional) Multiple GPUs: Where’s Pixel-Waldo?. Text Book / Resources. 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. Cliff Woolley NVIDIADeveloper Technology GroupGPUCPUGPGPU Revolutionizes ComputingLatency Processor Throughput processorLow Latency or High ThroughputCPUOptimized for low-latency access to cached dat

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