PPT-INF5063 – GPU & CUDA

Author : pasty-toler | Published Date : 2017-09-28

Håkon Kvale Stensland Simula Research Laboratory PC Graphics Timeline Challenges Render infinitely complex scenes And extremely high resolution In 160 th of one

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INF5063 – GPU & CUDA: Transcript

Håkon Kvale Stensland Simula Research Laboratory PC Graphics Timeline Challenges Render infinitely complex scenes And extremely high resolution In 160 th of one second 60 frames per second. Basically a child CUDA Kernel can be called from within a parent CUDA kernel and then optionally synchronize on the completion of that child CUDA Kernel The parent CUDA kernel can consume the output produced from the child CUDA Kernel all withou t 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. © 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. 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. 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 . K. ainz. Overview. About myself. Motivation. GPU hardware and system architecture. GPU programming languages. GPU programming paradigms. Pitfalls and best practice. Reduction and tiling examples. State-of-the-art . 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 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 . 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 . NVIDIA by Simon Green. 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. Research Computing Services. Boston . University. GPU Programming. Access to the SCC. Login: . tuta#. Password: . VizTut#. GPU Programming. Access to the SCC GPU nodes. # copy tutorial materials: . Ajaya. . Neupane. , . Zhiyun. Qian . and . Nael. Abu-. Ghazaleh. University of California, Riverside. Rendered Insecure: . GPU Side Channel Attacks . are Practical. 1. G. raphics . P. rocessing .

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