PPT-CUDA

Author : calandra-battersby | Published Date : 2016-05-30

Misc Mergesort Pinned Memory Device Query Multi GPU Parallel Mergesort ON runtime with memory copy overhead Not really worth it compared to O NlgN sequential

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CUDA: Transcript

Misc Mergesort Pinned Memory Device Query Multi GPU Parallel Mergesort ON runtime with memory copy overhead Not really worth it compared to O NlgN sequential version but an interesting exercise. . 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. 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. Applications:. NAMD. Parallel Framework for Unstructured Meshing (. ParFUM. ). Features:. Profile snapshots:. Captures the runtime of the application by segregating it into user specified intervals. CUDA Profiling. 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. CUDA Lecture 7. CUDA Threads and Atomics. The Problem: how do you do global communication?. Finish a kernel and start a new one. All writes from all threads complete before a kernel finishes. Would need to decompose kernels into before and after parts. and Debugging. Shehzan. ArrayFire. Summary. ArrayFire. GPU Programming and CUDA. Debugging and Profiling using CUDA Tools. Memory Coalescing. Shared Memory and Bank Conflicts. Transpose. Reduction. ArrayFire. 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. 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. Overview. GPU Ocelot overview. Building, configuring, and executing Ocelot programs. Ocelot Device Interface and CUDA Runtime API. Ocelot PTX Internal Representation. PTX Pass Manager. 2. Ocelot: Multiplatform Dynamic Compilation. Håkon Kvale . Stensland. Simula Research Laboratory. PC Graphics Timeline. Challenges. :. Render infinitely complex scenes. And extremely high resolution. In 1/60. th. of one second (60 frames per second). on . Ubuntu. Cuda. download site. . https://developer.nvidia.com/cuda-downloads. $ . sudo. . dpkg. -. i. cuda-repo-ubuntu1404_7.5-18_amd64.deb . $ . sudo. apt-get update . $ . sudo. apt-get install . heterogeneous programming. Brian Gregor. bgregor@bu.edu. Research Computing Services. Boston University. CUDA C/C BASICS. NVIDIA . Corporation. © NVIDIA 2013. What is CUDA?. CUDA Architecture. Expose GPU parallelism for general-purpose computing. Agenda. Text book / resources. Eclipse . Nsight. , NVIDIA Visual Profiler. Available libraries. Questions. Certificate dispersal. (Optional) Multiple GPUs: Where’s Pixel-Waldo?. Text Book / Resources. 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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