add Nsight resources and lecture

This commit is contained in:
Carl Pearson
2020-04-16 13:07:52 -05:00
parent d924f2963b
commit ce75bcafe4
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@@ -82,3 +82,6 @@ During the Mavis fellowship, I administered the ECE 408 GPU programming project
[l4]: ../../pptx/2017FA_ECE408_dl04_CNN02.pptx
[kickoff-slides]: ../../pdf/2017FA_ECE408_project-kickoff.pdf
[kickoff-repo]: https://github.com/illinois-impact/ece408_project-kickoff-slides
I also created a set of resources on using Nvidia's Nsight Compute and Nsight Systems performance profiling tools, including a 75 minute recorded lecture.
See the [Github repository](https://github.com/cwpearson/nvidia-performance-tools) to get started.

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title = "Nsight Systems and Nsight Compute Teaching Resources"
subtitle = ""
date = 2020-04-16T00:00:00
lastmod = 2020-04-16T00:00:00
draft = false
# Authors. Comma separated list, e.g. `["Bob Smith", "David Jones"]`.
authors = ["Carl Pearson"]
tags = ["GPU", "Nsight Compute", "Nsight Systems"]
summary = ""
# Projects (optional).
# Associate this post with one or more of your projects.
# Simply enter your project's folder or file name without extension.
# E.g. `projects = ["deep-learning"]` references
# `content/project/deep-learning/index.md`.
# Otherwise, set `projects = []`.
projects = ["nsight"]
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[image]
# Caption (optional)
caption = ""
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# Options: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight
focal_point = "Center"
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categories = []
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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.
This lecture covers some performance measurement techniques available in CUDA.
The 75 minute lecture, available on Youtube in four parts:
* [Slides (pdf)](/pdf/20200416_nsight.pdf)
* [Part 1: Intro](https://youtu.be/uN2qju175aE)
* [Part 2: CUDA Events](https://youtu.be/yI137sSOlkU)
* [Part 3: Nsight Compute](https://youtu.be/UNX0KNMQlW8)
* [Part 4: Nsight Systems](https://youtu.be/YHrmnaPgFfY)
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.
It also contains Docker images for amd64 and ppc64le with Nsight Compute and Nsight Systems.

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---
title: CUDA Profiling Resources
summary: Examples, Docker images, walkthroughs, and lectures for Nvidia's profiling tools
tags:
- impact
date: "2020-04-16T00:00:00Z"
# Optional external URL for project (replaces project detail page).
external_link: https://github.com/cwpearson/nvidia-performance-tools
image:
caption: Photo by Toa Heftiba on Unsplash
focal_point: Smart
---

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---
title: Using Nsight Compute and Nsight Systems
event: ECE 408 Guest Lecture
event_url:
location: University of Illinois
address:
street:
city: Urbana, IL
region:
postcode:
country: USA
summary: Introduction and walkthrough of Nvidia Nsight Compute and Nsight Systems profiling tools.
abstract: ""
# Talk start and end times.
# End time can optionally be hidden by prefixing the line with `#`.
date: "2020-04-16"
#date_end: "2020-04-16"
all_day: true
# Schedule page publish date (NOT talk date).
publishDate: "2020-04-16T00:00:00Z"
authors: ["Carl Pearson"]
tags: []
# Is this a featured talk? (true/false)
featured: false
image:
caption: ''
focal_point: Right
links:
url_code: ""
url_pdf: ""
url_slides: "pdf/20200416_nsight.pdf"
url_video: "https://www.youtube.com/watch?v=YHrmnaPgFfY"
# Markdown Slides (optional).
# Associate this talk with Markdown slides.
# Simply enter your slide deck's filename without extension.
# E.g. `slides = "example-slides"` references `content/slides/example-slides.md`.
# Otherwise, set `slides = ""`.
slides: ""
# Projects (optional).
# Associate this post with one or more of your projects.
# Simply enter your project's folder or file name without extension.
# E.g. `projects = ["internal-project"]` references `content/project/deep-learning/index.md`.
# Otherwise, set `projects = []`.
projects:
- nsight
# Enable math on this page?
math: true
---
Invited guest lecture for Professor Steve Lumetta.

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