forked from normrubin/normrubin.github.io
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathweekly.qmd
95 lines (63 loc) · 4.31 KB
/
weekly.qmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
---
format:
html: default
tbl-colwidths:
- 10
- 20
- 20
- 20
- 15
- 15
title: EECS7398 Weekly Schedule fa 2024
---
Since the is the first time this course is offered.
This is a tentative schedule.
The papers listed here are suggestions, if there is a different paper you would like to present, send me a link so that I can approve it.
Either 1 or 2 people can sign up for a paper. Once everyone has done so, I'll schedule which paper gets which date.
| session | Date | topic | due | discussions|
|--:|---------|--------| ----| -----------|
1| Sept 6 | [Compiler Overview](lectures/010_compiler_overview.qmd) | | [discussion](https://github.com/normrubin/normrubin.github.io/discussions/70)
2|Sept 10 | [Performance Measurement](lectures/01a_performance_measurement.qmd) | | [discussion](https://github.com/normrubin/normrubin.github.io/discussions/71)
3|Sept 13 |[Representing programs](lectures/02a_representation.qmd)| hw0 | [discussion](https://github.com/normrubin/normrubin.github.io/discussions/72)
| | | [Bril](lectures/02b_bril.qmd)| |[discussion](https://github.com/normrubin/normrubin.github.io/discussions/73)
4| Sept 20 | [Local analysis and optimization](lectures/03_local.qmd) | | [discussion](https://github.com/normrubin/normrubin.github.io/discussions/74)
|||[Local Value Numbering](lectures/03b_local_value_numbering.qmd)
5| Sept 27 | Data flow | |
6| Oct 1 |Global analysis |
7| Oct 4 | Loop invariant code motion | |
8|Oct 8 |Static single assignment |
9|Oct 11 | [GPU Compilers](lectures/14_gpu_compilers.qmd) | |
10| Oct 15 |Global value number |
11| Oct 22 | readings | |
12| Oct 25 |LLVM | |
13| Oct 29 | readings |
14 | Nov 1 |[Dynamic compilers 1 ](lectures/13_dynamic_compilers.qmd) |
15| Nov 5 | [Dynamic compilers 2 ](lectures/13_dynamic_compilers.qmd) | |
16| Nov 8 | Classical loop optimizations | | |
19| Nov 12 | readings |
20| Nov 15 | Polyhedral analysis | |
21| Nov 19 | readings | | |
22| Nov 22 | readings | |
| |Nov 29 | **Thanksgiving**|
23|Nov 26 | readings | |
24| Dec 3 | Interprocedural Analysis |
25| Dec 6 | ai in compilers |
Suggested Papers
leader: [Should AI Optimize Your Code? A Comparative Study of Current Large Language Models Versus Classical Optimizing Compilers](https://arxiv.org/html/2406.12146v1)
leader: [SLaDe: A Portable Small Language Model Decompiler for Optimized Assembly](https://arxiv.org/abs/2305.12520)
leader: Sana Taghipour Anvari [Large Language Models for Compiler Optimization](https://ar5iv.labs.arxiv.org/html/2309.07062)
Leader:[ACPO: AI-Enabled Compiler-Driven Program Optimization](https://ar5iv.labs.arxiv.org/html/2312.09982)
leader: [Retargeting and Respecializing GPU Workloads for Performance Portability](https://ieeexplore.ieee.org/document/10444828)
leader: [Adaptive Online Context-Sensitive Inlining](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1191550)
leader: [MLIR: A Compiler Infrastructure for the End of Moore’s Law](https://arxiv.org/abs/2002.11054)
leader: [Threaded Code Variations and Optimizations](https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=e1e1a8622dc562e7f42d98b59f67de6949e2539e)
leader: [ProGraML: Graph-based Deep Learning for Program Optimization and Analysis](https://arxiv.org/abs/2003.10536)
leader: [TVM: An Automated End-to-End Optimizing Compiler for Deep Learning](https://www.usenix.org/conference/osdi18/presentation/chen)
leader:[Learning Compiler Pass Orders using Coreset and Normalized Value Prediction](https://ar5iv.labs.arxiv.org/html/2301.05104v1)
leader: [An MLIR-based Compiler Flow for System-Level Design and Hardware Acceleration](https://dl.acm.org/doi/abs/10.1145/3508352.3549424)
leader: [Learning to Optimize Tensor Programs](https://arxiv.org/abs/1805.08166)
leader: [End-to-end Deep Learning of Optimization Heuristics](https://chriscummins.cc/pub/2017-pact.pdf)
leader: [Compiler Fuzzing through Deep Learning](https://chriscummins.cc/pub/2018-issta.pdf)
leader:[Autotuning OpenCL workgroup size for stencil patterns](https://arxiv.org/pdf/1511.02490)
leader: [Generating GPU Compiler Heuristics using Reinforcement Learning](https://arxiv.org/abs/2111.12055)
leader: [Energy-Aware Tile Size Selection for Affine Programs on GPUs](https://malithjayaweera.com/wp-content/uploads/2024/01/CGO24_eatss_PREPRINT.pdf)