PPT-GPU programming: CUDA

Author : alida-meadow | Published Date : 2015-10-07

Acknowledgement the lecture materials are based on the materials in NVIDIA teaching center CUDA course materials including materials from Wisconsin Negrut North

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GPU programming: CUDA: Transcript

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. 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:. Håkon Kvale . Stensland. iAD-lab, Department for Informatics. Basic 3D Graphics Pipeline. Application. Scene Management. Geometry. Rasterization. Pixel Processing. ROP/FBI/Display. Frame. Buffer. Memory. 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. 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 . Introduction to CUDA Programming CUDA Programming Introduction Andreas Moshovos Winter 2009 Some slides/material from: UIUC course by Wen-Mei Hwu and David Kirk UCSB course by Andrea Di Blas Universitat Jena by Waqar Saleem . 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. Waters. Introduction to GPU Computing. Brief History of GPU Computing. Technical Issues. Social Impact. Marketing and Ethical . Issues. Project Management. Conclusion. Table of Contents. A . GPU is . Programming, Part 3. -- Streaming, Library and Tuning. CSCE 790: Parallel Programming Models for Multicore and . Manycore. Processors. Department of Computer Science and Engineering. Yonghong. Yan. Martin Burtscher. Department of Computer Science. High-end CPU-GPU Comparison. . Xeon 8180M. . Titan V. Cores 28 5120 (+ 640). Active threads 2 per core 32 per core. Frequency 2.5 (3.8) GHz 1.2 (1.45) GHz. 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. Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2014. Lectures. Monday. 6-9pm. Moore 212. Fall. and . Spring. 2012 lectures were recorded. Attendance is required for guest lectures. Image from . Patrick Cozzi. University of Pennsylvania. CIS 565 - Fall 2013. Lectures. Monday and Wednesday. 6-7:30pm. Towne . 307. Fall. and . Spring. 2012 lectures were recorded. Attendance is required for guest lectures.

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