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---------------------------------------------------
## some possible additional papers
[LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers](https://arxiv.org/abs/2403.11522)
Merouani, M., Boudaoud, K.A., Aouadj, I.N., Tchoulak, N., Bernou, I.K., Benyamina, H., Tayeb, F.B.S., Benatchba, K., Leather, H. and Baghdadi, R. arXiv preprint arXiv:2403.11522
after polyhedrial
[Improving Data Locality with Loop Transformations](https://dl.acm.org/doi/pdf/10.1145/233561.233564)
McKinley, Carr, & Tseng toplas 1996
[High-performance code generation for stencil computations on GPU architectures.](https://www.cs.colostate.edu/~pouchet/doc/ics-article.12.pdf)
*Holewinski, Jan, Louis-Noël Pouchet, and P. Sadayappan. supercomputing 2012
Context Threading: A flexible and efficient dispatch technique for virtual machine interpreters
chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.cs.toronto.edu/syslab/pubs/demkea_context.pdf Marc Berndl, Benjamin Vitale, Mathew Zaleski and Angela Demke Brown
[Program optimization space pruning for a multithreaded gpu](https://dl.acm.org/doi/pdf/10.1145/1356058.1356084)
Shane Ryoo, Christopher I. Rodrigues, Sam S. Stone, Sara S. Baghsorkhi, Sain-Zee Ueng, John A. Stratton, and Wen-mei W. Hwu cgo 2008
[Detecting equality of variables in programs](https://dl.acm.org/doi/10.1145/73560.73561)
Alpern, Bowen, Mark N. Wegman, and F. Kenneth Zadeck. ??
https://dl.acm.org/doi/pdf/10.1145/1133255.1134000
DieHard: probabilistic memory safety for unsafe languages
maybes
[The Garbage Collection Advantage: Improving Program Locality](https://dl.acm.org/doi/10.1145/1028976.1028983)
X. Huang, S. M. Blackburn, K. S. McKinley, J. E. B. Moss, Z. Wang, and P. Cheng, oopsla 2004
Pradelle, B., Baskaran, M., Henretty, T., Meister, B., Konstantinidis, A. and Lethin, R., 2016, September. Polyhedral compilation for energy efficiency. In 2016 IEEE High Performance Extreme Computing Conference (HPEC) (pp. 1-7). IEEE. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7761595
5. **Baskaran, Muthu Manikandan, et al.** "Automatic data movement and computation mapping for multi-level parallel architectures with explicitly managed memories." *Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming.* 2010.
3. **Ryoo, Seongbeom, et al.** "Optimization principles and application performance evaluation of a multithreaded GPU using CUDA." *Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming.* 2008.
22. Boehm, Hans-Juergen, and Mark Weiser. "Garbage collection in an uncooperative environment." Software: Practice and Experience 18.9 (1988): 807-820.
possible lectures
[cranelift checking correctness in register allocator])(https://cfallin.org/blog/2021/03/15/cranelift-isel-3/)
this is a blog post - after ssa
[Value Numbering](https://www.cs.tufts.edu/~nr/cs257/archive/keith-cooper/value-numbering.pdf)
P. Briggs, K. D. Cooper, L. Taylor Simpson, Software-Practice & Experience, 1997
global value numbering in ssa, congruence
Wilson PR. Uniprocessor garbage collection techniques. InInternational Workshop on Memory Management 1992 Sep 17 (pp. 1-42). Berlin, Heidelberg: Springer Berlin Heidelberg.
https://ieeexplore.ieee.org/document/10444819
A. Murtovi, G. Georgakoudis, K. Parasyris, C. Liao, I. Laguna and B. Steffen, "Enhancing Performance Through Control-Flow Unmerging and Loop Unrolling on GPUs," 2024 IEEE/ACM International Symposium on Code Generation and Optimization (CGO), Edinburgh, United Kingdom, 2024, pp. 106-118, doi: 10.1109/CGO57630.2024.10444819. keywords: {Codes;Costs;Graphics processing units;Prototypes;Benchmark testing;Predictive models;Optimization;compiler;code duplication;LLVM;GPU},
https://escholarship.org/uc/item/3rt0n0q2
Gal, A., Probst, C. W, & Franz, M. (2003). A denial of service attack on the Java bytecode verifier. UC Irvine: Donald Bren School of Information and Computer Sciences. Retrieved from https://escholarship.org/uc/item/3rt0n0q2
https://dl.acm.org/doi/pdf/10.1145/3620665.3640366
PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation ASPLOS '24: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2
Meta Large Language Model Compiler: Foundation Models of Compiler Optimization Chris Cummins†, Volker Seeker†, Dejan Grubisic, Baptiste Rozière, Jonas Gehring, Gabriel Synnaeve, Hugh Leather /https://scontent-bos5-1.xx.fbcdn.net/v/t39.2365-6/448997590_1496256481254967_2304975057370160015_n.pdf?_nc_cat=106&ccb=1-7&_nc_sid=3c67a6&_nc_ohc=4Yn8V9DFdbsQ7kNvgEwOdGk&_nc_ht=scontent-bos5-1.xx&oh=00_AYD-0YTCXuS11WU8rqC3N2aA-AfiflOptch_BD__V1V3xA&oe=6684630D
------------------- papers
paper 1 - [Producing Wrong Data Without Doing Anything Obviously Wrong!]
paper 2 - [iterative data-flow analysis, revisited](https://repository.rice.edu/server/api/core/bitstreams/790ce776-44cf-4474-8f60-4c1f5959ee74/content)
Cooper, Keith D.; Harvey, Timothy J.; Kennedy, Ken (2004-03-26) [November 2002]. pldi 2002
paper 3 https://dl.acm.org/doi/10.1145/1064978.1065042 Threads cannot be implemented as a library
paper 4 [Superoptimizer: A Look at the Smallest Program](https://dl.acm.org/doi/abs/10.1145/36177.36194)
Alexia Massalin. ASPLOS 1987.
paper 5 pappe 5 Formal Verification of a Realistic Compiler
Xavier Leroy. CACM in 2009.
paper 6 Efficient Path Profiling
Thomas Ball and James R. Larus. MICRO 1996.
paper 7 An Efficient Implementation of SELF, a Dynamically-Typed Object-Oriented Language Based on Prototypes
C. Chambers, D. Ungar, and E. Lee. OOPSLA 1989.
paper 8 "Partial Redundancy Elimination" by Jens Knoop, Oliver Rüthing, and Bernhard Steffen
Formal Verification of a Realistic Compiler
Xavier Leroy. CACM in 2009.
paper [Dynamo: A Transparent Dynamic Optimization System] (https://dl.acm.org/doi/pdf/10.1145/349299.349303)
Vasanth Bala
https://dada.cs.washington.edu/research/tr/2017/12/UW-CSE-17-12-01.pdf
12-01 TVM:End-to-End Optimization Stack for Deep Learnin
Revealing Compiler Heuristics through Automated Discovery and Optimization, V. Seeker, C. Cummins, M. Cole, B. Franke, K. Hazelwood, H. Leather
End-to-end deep learning of optimization heuristics - Chris Cummins, Pavlos Petoumenos, Zheng Wang, and Hugh Leather PACT 2017.
https://ieeexplore.ieee.org/document/8091247
1988
2000
[Dynamo: A Transparent Dynamic Optimization System](/https://dl.acm.org/doi/pdf/10.1145/349299.349303)
Bala, V., Duesterwald, E. and Banerjia, S., PLDI 2000
2002
[iterative data-flow analysis, revisited](https://repository.rice.edu/server/api/core/bitstreams/790ce776-44cf-4474-8f60-4c1f5959ee74/content)
Cooper, Keith D.; Harvey, Timothy J.; Kennedy, Ken (2004-03-26),November 2002
2005
[Threads cannot be implemented as a library] (https://dl.acm.org/doi/10.1145/1064978.1065042)
Boehm, H.J.. PLDI 2005
2015
[Provably correct peephole optimizations with alive](https://dl.acm.org/doi/pdf/10.1145/2737924.2737965?casa_token=o9UQe90sRVwAAAAA:thVHM1EjwKgubb_CO07_pqFVz2SZFbkGiaPxUsMdMv5DZqFVqNJoTIXTZ1MwbCYZSm0i-49M_eqY)
Lopes, N.P., Menendez, D., Nagarakatte, S. and Regehr, J. pldi 2015
2009
[Formal Verification of a Realistic Compiler](https://dl.acm.org/doi/pdf/10.1145/1538788.1538814)
Xavier Leroy. CACM 2009.
2018
[TVM: end-to-end optimization stack for deep learning](https://dada.cs.washington.edu/research/tr/2017/12/UW-CSE-17-12-01.pdf)
Chen, Tianqi, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Q. Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy.arXiv preprint arXiv:1802.04799 11, no. 2018 (2018): 20.
2024
[Enhancing Performance Through Control-Flow Unmerging and Loop Unrolling on GPU](https://ieeexplore.ieee.org/document/10444819)
A. Murtovi, G. Georgakoudis, K. Parasyris, C. Liao, I. Laguna and B. Steffen, cgo 2024
https://escholarship.org/uc/item/3rt0n0q2
Gal, A., Probst, C. W, & Franz, M. (2003). A denial of service attack on the Java bytecode verifier. UC Irvine: Donald Bren School of Information and Computer Sciences. Retrieved from https://escholarship.org/uc/item/3rt0n0q2
https://dl.acm.org/doi/pdf/10.1145/3620665.3640366
PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation ASPLOS '24: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2
/https://scontent-bos5-1.xx.fbcdn.net/v/t39.2365-6/448997590_1496256481254967_2304975057370160015_n.pdf?_nc_cat=106&ccb=1-7&_nc_sid=3c67a6&_nc_ohc=4Yn8V9DFdbsQ7kNvgEwOdGk&_nc_ht=scontent-bos5-1.xx&oh=00_AYD-0YTCXuS11WU8rqC3N2aA-AfiflOptch_BD__V1V3xA&oe=6684630D
Meta Large Language Model Compiler: Foundation Models of Compiler Optimization Chris Cummins†, Volker Seeker†, Dejan Grubisic, Baptiste Rozière, Jonas Gehring, Gabriel Synnaeve, Hugh Leather†
Chlorophyll: Synthesis-Aided Compiler for Low-Power Spatial Architectures
Phitchaya Mangpo Phothilimthana, Tikhon Jelvis, Rohin Shah, Nishant Totla, Sarah Chasins, and Rastislav Bodik. PLDI 2014.
Trace-Based Just-in-Time Type Specialization for Dynamic Languages
Andreas Gal, Brendan Eich, Mike Shaver, David Anderson, David Mandelin, Mohammad R. Haghighat, Blake Kaplan, Graydon Hoare, Boris Zbarsky, Jason Orendorff, Jesse Ruderman, Edwin W. Smith, Rick Reitmaier, Michael Bebenita, Mason Chang, and Michael Franz. PLDI 2009.
Mesh: Compacting Memory Management for C/C++ Applications
Bobby Powers, David Tench, Emery D. Berger, and Andrew McGregor. PLDI 2019.
A Unified Theory of Garbage Collection
David F. Bacon, Perry Cheng, and V. T. Rajan. OOPSLA 2004.
Type-Based Alias Analysis
Amer Diwan, Kathryn S. McKinley, and J. Eliot B. Moss.
Bodik, Rastislav, Rajiv Gupta, and Vivek Sarkar. "ABC: Path-sensitive dynamic test generation." ACM SIGPLAN Notices 35.5 (2000): 61-73.
Cooper, Keith D., and Linda Torczon. "Tiling for improved register usage." ACM SIGPLAN Notices 28.6 (1993): 279-290.
Cytron, Ron, et al. "Efficiently computing static single assignment form and the control dependence graph." ACM Transactions on Programming Languages and Systems (TOPLAS) 13.4 (1991): 451-490.
Ganapathi, Madhusudhan, et al. "Experience with the MIPS compiler." ACM SIGPLAN Notices 21.7 (1986): 175-187.
Go
Hall, Mary W., and Ken Kennedy. "Efficient call graph analysis." ACM Letters on Programming Languages and Systems (LOPLAS) 1.3 (1992): 227-242.
Kennedy, Ken, and Kathryn S. McKinley. "Loop distribution with arbitrary control flow." ACM SIGPLAN Notices 29.6 (1994): 140-151.
Knoop, Jens, Oliver Rüthing, and Bernhard Steffen. "Lazy code motion." ACM SIGPLAN Notices 27.7 (1992): 224-234.
Lamport, Leslie. "The parallel execution of DO loops." Communications of the ACM 17.2 (1974): 83-93.
Sarkar, Vivek. "Partitioning parallel programs for macro-dataflow." ACM SIGPLAN Notices 23.7 (1988): 98-106.
Shivers, Olin. "Control-flow analysis in Scheme." ACM SIGPLAN Notices 23.7 (1988): 164-174.
Steensgaard, Bjarne. "Points-to analysis in almost linear time." ACM SIGPLAN Notices 31.5 (1996): 32-41.
Wolf, Michael E., and Monica S. Lam. "A data locality optimizing algorithm." ACM SIGPLAN Notices 26.6 (1991): 30-44.
Yaccarino, Joseph, and Keshav Pingali. "Data-flow analysis for distributed-memory multiprocessors." ACM SIGPLAN Notices 27.9 (1992): 353-363.
Zadeck, F. Kenneth, and Olivier Rüthing. "Incremental data flow analysis." ACM SIGPLAN Notices 23.7 (1988): 132-146.
2. Bodik, Rastislav, Rajiv Gupta, and Vivek Sarkar. "ABC: Path-sensitive dynamic test generation." ACM SIGPLAN Notices 35.5 (2000): 61-73.
4. Cooper, Keith D., and Linda Torczon. "Tiling for improved register usage." ACM SIGPLAN Notices 28.6 (1993): 279-290.
5. Cytron, Ron, et al. "Efficiently computing static single assignment form and the control dependence graph." ACM Transactions on Programming Languages and Systems (TOPLAS) 13.4 (1991): 451-490.
6. Ferrante, Jeanne, Karl J. Ottenstein, and Joe D. Warren. "The program dependence graph and its use in optimization." ACM Transactions on Programming Languages and Systems (TOPLAS) 9.3 (1987): 319-349.
https://dl.acm.org/doi/10.1145/24039.24041
7. Ganapathi, Madhusudhan, et al. "Experience with the MIPS compiler." ACM SIGPLAN Notices 21.7 (1986): 175-187.
9. Hall, Mary W., and Ken Kennedy. "Efficient call graph analysis." ACM Letters on Programming Languages and Systems (LOPLAS) 1.3 (1992): 227-242.
10. Kennedy, Ken, and Kathryn S. McKinley. "Loop distribution with arbitrary control flow." ACM SIGPLAN Notices 29.6 (1994): 140-151.
11. Knoop, Jens, Oliver Rüthing, and Bernhard Steffen. "Lazy code motion." ACM SIGPLAN Notices 27.7 (1992): 224-234.
12. Lamport, Leslie. "The parallel execution of DO loops." Communications of the ACM 17.2 (1974): 83-93.
13. McKinley, Kathryn S., Steve Carr, and Chau-Wen Tseng. "Improving data locality with loop transformations." ACM Transactions on Programming Languages and Systems (TOPLAS) 18.4 (1996): 424-453.
15. Sarkar, Vivek. "Partitioning parallel programs for macro-dataflow." ACM SIGPLAN Notices 23.7 (1988): 98-106.
16. Shivers, Olin. "Control-flow analysis in Scheme." ACM SIGPLAN Notices 23.7 (1988): 164-174.
17. Steensgaard, Bjarne. "Points-to analysis in almost linear time." ACM SIGPLAN Notices 31.5 (1996): 32-41.
19. Tichy, Walter F. "Smart recompilation." ACM Transactions on Programming Languages and Systems (TOPLAS) 8.3 (1986): 273-291.
20. Wolf, Michael E., and Monica S. Lam. "A data locality optimizing algorithm." ACM SIGPLAN Notices 26.6 (1991): 30-44.
21. Appel, Andrew W. "Simple generational garbage collection and fast allocation." Software: Practice and Experience 19.2 (1989): 171-183.
23.
Dijkstra, Edsger W., et al. "On-the-fly garbage collection: An exercise in cooperation." Communications of the ACM 21.11 (1978): 965-975.
24.
Bacon, David F., Perry Cheng, and V. T. Rajan. "A real-time garbage collector with low overhead and consistent utilization." ACM SIGPLAN Notices. Vol. 38. No. 5. 2003.
Wegman & Zadeck, Constant Propagation with Conditional Branches, ACM Transactions on Programming Languages and Systems, 13(2):181-210, April 1991.
2. **Garland, Michael, et al.** "Parallel computing experiences with CUDA." *IEEE Micro* 28.4 (2008): 13-27.
do to ********
ai in compilers
--------------------
papers
Generating GPU Compiler Heuristics using Reinforcement Learning https://arxiv.org/abs/2111.12055
Ian Colbert, Jake Daly, Norm Rubin
Revealing Compiler Heuristics Through Automated Discovery and Optimization
Volker Seeker; Chris Cummins; Murray Cole; Björn Franke; Kim Hazelwood; Hugh Leather
https://ieeexplore.ieee.org/document/10444847
End-to-End Deep Learning of Optimization Heuristics
Chris Cummins; Pavlos Petoumenos; Zheng Wang; Hugh Leather
https://ieeexplore.ieee.org/document/8091247
possible lecture source
Machine Learning in Compilers: Past, Present and Future
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9232934
Hugh Leather; Chris Cummins
papers
Relay: A High-Level Compiler for Deep Learning https://arxiv.org/abs/1904.08368
Jared Roesch, Steven Lyubomirsky, Marisa Kirisame, Logan Weber, Josh Pollock, Luis Vega, Ziheng Jiang, Tianqi Chen, Thierry Moreau, Zachary Tatlock
--------------------------
possible lecture source
https://huyenchip.com/2021/09/07/a-friendly-introduction-to-machine-learning-compilers-and-optimizers.html
https://medium.com/geekculture/ai-compilers-ae28afbc4907
------------------------
papers not included
A comprehensive study of deep learning compiler bugs https://dl.acm.org/doi/abs/10.1145/3468264.3468591?casa_token=Aj2H-DPPcmQAAAAA:7gy0bCEqaIBDoYDSS6MTtMtGYNTUYySFlmyNjrzsc0d1S8DAEC9yuCUwv0Sx4SLk2ujUhKWijbvo
Qingchao Shen, Haoyang Ma, Junjie Chen, Yongqiang Tian, Shing-Chi Cheung, and Xiang ChenAuthors Info & Claims