PPT-GPU Hardware and CUDA Programming

Author : webraph | Published Date : 2020-11-06

Martin Burtscher Department of Computer Science Highend CPUGPU Comparison Xeon 8180M Titan V Cores 28 5120 640 Active threads 2 per core 32 per core Frequency

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GPU Hardware and CUDA Programming: Transcript

Martin Burtscher Department of Computer Science Highend CPUGPU Comparison Xeon 8180M Titan V Cores 28 5120 640 Active threads 2 per core 32 per core Frequency 25 38 GHz 12 145 GHz. 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 . 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. CUDA Lecture 4. CUDA Programming Basics. Things we need to consider:. Control. Synchronization. Communication. Parallel programming languages offer different ways of dealing with above. CUDA Programming Basics – Slide . 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. 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. Quentin Ochem. October 4. th. , 2018. What is GPGPU?. GPU were traditionally dedicated to graphical rendering …. … but their capability is really vectorized computation. Enters General Programming GPU (GPGPU). 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 . . 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. 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. 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: . The Desired Brand Effect Stand Out in a Saturated Market with a Timeless Brand

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