56 lines
1.8 KiB
Markdown
56 lines
1.8 KiB
Markdown
+++
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title = "Nsight Systems and Nsight Compute Teaching Resources"
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subtitle = ""
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date = 2020-04-16T00:00:00
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lastmod = 2020-04-16T00:00:00
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draft = false
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# Authors. Comma separated list, e.g. `["Bob Smith", "David Jones"]`.
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authors = ["Carl Pearson"]
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tags = ["GPU", "Nsight Compute", "Nsight Systems"]
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summary = ""
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# Projects (optional).
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# Associate this post with one or more of your projects.
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# Simply enter your project's folder or file name without extension.
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# E.g. `projects = ["deep-learning"]` references
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# `content/project/deep-learning/index.md`.
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# Otherwise, set `projects = []`.
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projects = ["nsight"]
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# Featured image
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# To use, add an image named `featured.jpg/png` to your project's folder.
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[image]
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# Caption (optional)
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caption = ""
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# Focal point (optional)
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# Options: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight
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focal_point = "Center"
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# Show image only in page previews?
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preview_only = true
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categories = []
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# Set captions for image gallery.
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+++
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I was invited to give a guest lecture for the Spring 2020 ECE 408 GPU programming course at the University of Illinois.
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This lecture covers some performance measurement techniques available in CUDA.
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The 75 minute lecture, available on Youtube in four parts:
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* [Slides (pdf)](/pdf/20200416_nsight.pdf)
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* [Part 1: Intro](https://youtu.be/uN2qju175aE)
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* [Part 2: CUDA Events](https://youtu.be/yI137sSOlkU)
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* [Part 3: Nsight Compute](https://youtu.be/UNX0KNMQlW8)
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* [Part 4: Nsight Systems](https://youtu.be/YHrmnaPgFfY)
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There is also a repository [cwpearson/nvidia-performance-tools](https://github.com/cwpearson/nvidia-performance-tools) which contains all the code examples used in the lecture.
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It also contains Docker images for amd64 and ppc64le with Nsight Compute and Nsight Systems.
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