PPT-GPU/CUDA Instrumentation Notes

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

Current Goals Generate stacktraces of GPU executions and associate GPU call chains with CPU call graphs Particular interest on how to determine call chains when

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GPU/CUDA Instrumentation Notes: Transcript

Current Goals Generate stacktraces of GPU executions and associate GPU call chains with CPU call graphs Particular interest on how to determine call chains when inlined GPU functions are used. 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. 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. 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. 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. 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. 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). 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. 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 . Agenda. Text book / resources. Eclipse . Nsight. , NVIDIA Visual Profiler. Available libraries. Questions. Certificate dispersal. (Optional) Multiple GPUs: Where’s Pixel-Waldo?. Text Book / Resources. May 8-Stephen Jones GTC 2018CUDANEW FEATURESAND BEYOND22CUDA ECOSYSTEM 2018CUDADOWNLOADS IN 20173500000CUDA REGISTERED DEVELOPERS800000GTC ATTENDEES80003CUDA DEVELOPMENT ECOSYSTEMFrom Ease of Use to S

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