melange:schedule:spring2021
Schedule : Spring 2021
This is the tentative schedule of Mélange group for the Spring 2021 semester.
Meeting time & Place : Mondays 12:00 PM - 1:00 PM (MST) via Webex
| WEEK | DATE | TOPIC | PRESENTER |
|---|---|---|---|
| 1 | 01/29/2021 | Intro meeting | |
| 2 | 02/08/2021 | Planning | |
| 3 | 02/15/2021 | From High-Level Inference Algorithms to Efficient Code (link) | William Scarbro |
| 4 | 02/22/2021 | Software for polyhedral compilation (link1,link2,link3) | Louis-Noel Pouchet |
| 5 | 03/01/2021 | Analytical Characterization and Design Space Exploration for Optimization of CNNs (link) | Chiranjeb Mondal |
| 6 | 03/08/2021 | Open discussion | |
| 7 | 03/15/2021 | Program transformations for energy efficiency | Louis Narmour |
| 8 | 03/22/2021 | Graph-based, Self-Supervised Program Repair from Diagnostic Feedback (link) | Steve Kommrusch |
| 9 | 03/29/2021 | Canonical Facet Allocation: A Burst-Friendly Data Layout | Corentin Ferry |
| 10 | 04/05/2021 | Cancelled | |
| 11 | 04/12/2021 | Spring break | |
| 12 | 04/19/2021 | Type-directed scheduling of streaming accelerators link) | Prerana Ghalsasi |
| 13 | 04/26/2021 | Tensorflow | Sanjay Rajopadhye |
| 14 | 05/03/2021 | Intel CnC (link) and the PIPES compiler (link) | Alexandre Dubois |
| 15 | 05/10/2021 |
Previous Semesters, including legacy reading lists
Standard paper study questions
- Write a short (max 5 sentences) summary of the paper.
- What is the problem addressed in the paper?
- Why is the problem important?
- How do the authors address the problem?
- How do they evaluate their approach?
- What is the punch-line (key cool idea, or “what I got out of this paper”)? This is often different for different people and different from what the authors may have intended.
- Make a list of deeper questions that you would like discussed in the meeting.
Current Reading Pool
- Nathanaël Courant, Xavier Leroy. Verified Code Generation for the Polyhedral Model. In Proc. ACM Program. Lang., POPL, 2021. https://doi.org/10.1145/3434321
- Rui Li, Yufan Xu, Aravind Sukumaran-Rajam, Atanas Rountev, P. Sadayappan. Analytical Characterization and Design Space Exploration for Optimization of CNNs. In The ACM Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS, 2021. https://arxiv.org/pdf/2101.09808.pdf
- Rajan Walia, Praveen Narayanan, Jacques Carette, Sam Tobin-Hochstadt, Chung-chieh Shan. From High-Level Inference Algorithms to Efficient Code. In Proc. ACM Program. Lang., ICFP, 2019. https://doi.org/10.1145/3341702
- Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, Noah D. Goodman. Pyro: Deep Universal Probabilistic Programming. In J. Mach. Learn. Res., JMLR, 2019. https://paperswithcode.com/paper/pyro-deep-universal-probabilistic-programming
- Zoubin Ghahramani. Probabilistic machine learning and artificial intelligence. In Nature, 521, 2015. https://doi.org/10.1038/nature14541
melange/schedule/spring2021.txt · Last modified: 2021/08/24 11:14 by corentin
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