PPT-GPU Computing with CUDA

Author : tawny-fly | Published Date : 2016-07-28

Dan Negrut 2012 UWMadison Dan Negrut SimulationBased Engineering Lab Wisconsin Applied Computing Center Department of Mechanical Engineering Department of Electrical

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GPU Computing with CUDA: Transcript

Dan Negrut 2012 UWMadison Dan Negrut SimulationBased Engineering Lab Wisconsin Applied Computing Center Department of Mechanical Engineering Department of Electrical and Computer Engineering. 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. Lecture . 7: Lab 3 Recitation. Today. Miscellaneous CUDA syntax. Recap on CUDA and buffers. Shared memory for an N-body simulation. Flocking simulations. Integrators. CUDA Kernels. Launching the kernel:. 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. Martin Burtscher. Department of Computer Science. High-End CPUs and GPUs. Xeon X7550 Tesla C2050. Cores 8 (superscalar) 448 (simple). Active threads 2 per core 48 per core. Frequency 2 GHz 1.15 GHz. 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). 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. 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). 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. 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. 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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