Merge pull request zwang4#20 from Wheest/patch-2

Add papers + IREE
BealZephyr · Jun 21, 2023 · 7b2d7b6 · 7b2d7b6
2 parents 23d22bb + ea59a8b
commit 7b2d7b6
Showing 1 changed file with 8 additions and 6 deletions.
diff --git a/README.md b/README.md
@@ -88,12 +88,12 @@ Polyhedron Model](https://www.infosun.fim.uni-passau.de/publications/docs/GGS+17
 #### Auto-tuning and Design Space Exploration
 - <img src="https://img.shields.io/badge/24-pages-green.svg" alt="24-pages" align="top"> [BaCO: A Fast and Portable Bayesian Compiler Optimization Framework](https://arxiv.org/pdf/2212.11142.pdf) - Erik Hellsten, Artur Souza, Johannes Lenfers, Rubens Lacouture, Olivia Hsu, Adel Ejjeh, Fredrik Kjolstad, Michel Steuwer, Kunle Olukotun, Luigi Nardi. ASPLOS 2024. 
 - <img src="https://img.shields.io/badge/12-pages-green.svg" alt="12-pages" align="top"> [(De/Re)-Compositions Expressed Systematically via MDH-Based Schedules](https://doi.org/10.1145/3578360.3580269) - Ari Rasch , Richard Schulze , Denys Shabalin , Anne Elster , Sergei Gorlatch , Mary Hall. CC 2023. 
-- <img src="https://img.shields.io/badge/23-pages-green.svg" alt="23-pages" align="top"> [Autotuning Convolutions is Easier Than You Think
-](https://dl.acm.org/doi/10.1145/3570641) - Nicolas Tollenaere , Guillaume Iooss , Stéphane Pouget , Hugo Brunie , Christophe Guillon , Albert Cohen , P. Sadayappan , Fabrice Rastello. ACM TACO 2022. 
+- <img src="https://img.shields.io/badge/23-pages-green.svg" alt="23-pages" align="top"> [Autotuning Convolutions is Easier Than You Think](https://dl.acm.org/doi/10.1145/3570641) - Nicolas Tollenaere , Guillaume Iooss , Stéphane Pouget , Hugo Brunie , Christophe Guillon , Albert Cohen , P. Sadayappan , Fabrice Rastello. ACM TACO 2022.
+- <img src="https://img.shields.io/badge/12-pages-green.svg" alt="12-pages" align="top"> [Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation](https://dl.acm.org/doi/10.1145/3559009.3569682) - Perry Gibson, Jose Cano. PACT 2022.
 - <img src="https://img.shields.io/badge/6-pages-green.svg" alt="6-pages" align="top"> [Glimpse: Mathematical Embedding of Hardware Specification for Neural Compilation](https://dl.acm.org/doi/abs/10.1145/3489517.3530590) - Byung Hoon Ahn, Sean Kinzer, Hadi Esmaeilzadeh. DAC 2022.
 - <img src="https://img.shields.io/badge/14-pages-green.svg" alt="14-pages" align="top"> [One-shot tuner for deep learning compilers](https://dl.acm.org/doi/abs/10.1145/3497776.3517774) - Jaehun Ryu, Eunhyeok Park, Hyojin Sung. CC 2022.
-- <img src="https://img.shields.io/badge/16-pages-green.svg" alt="16-pages" align="top"> [A Flexible Approach to Autotuning Multi-Pass Machine Learning Compilers](https://mangpo.net/papers/xla-autotuning-pact2021.pdf) - Phitchaya Mangpo Phothilimthana, Amit Sabne, Nikhil Sarda, Karthik Srinivasa Murthy, Yanqi Zhou, Christof Angermueller, Mike Burrows, Sudip Roy, Ketan Mandke, Rezsa Farahani, Yu Emma Wang, Berkin Ilbeyi,
-Blake Hechtman, Bjarke Roune, Shen Wang, Yuanzhong Xu, and Samuel J. Kaufman. PACT 2021.
+- <img src="https://img.shields.io/badge/16-pages-green.svg" alt="16-pages" align="top"> [A Flexible Approach to Autotuning Multi-Pass Machine Learning Compilers](https://mangpo.net/papers/xla-autotuning-pact2021.pdf) - Phitchaya Mangpo Phothilimthana, Amit Sabne, Nikhil Sarda, Karthik Srinivasa Murthy, Yanqi Zhou, Christof Angermueller, Mike Burrows, Sudip Roy, Ketan Mandke, Rezsa Farahani, Yu Emma Wang, Berkin Ilbeyi, Blake Hechtman, Bjarke Roune, Shen Wang, Yuanzhong Xu, and Samuel J. Kaufman. PACT 2021.
+- <img src="https://img.shields.io/badge/16-pages-green.svg" alt="16-pages" align="top"> [TASO: Optimizing Deep Learning Computation with Automatic Generation of Graph Substitutions]([https://mangpo.net/papers/xla-autotuning-pact2021.pdf](https://doi.org/10.1145/3341301.3359630)) - Zhihao Jia, Oded Padon, James Thomas, Todd Warszawski, Matei Zaharia, and Alex Aiken. ACM SOSP 2019.
 - <img src="https://img.shields.io/badge/12-pages-green.svg" alt="12-pages" align="top"> [Value Learning for Throughput Optimization of Deep Neural Workloads](https://proceedings.mlsys.org/paper/2021/file/73278a4a86960eeb576a8fd4c9ec6997-Paper.pdf) - Benoit Steiner, Chris Cummins, Horace He, Hugh Leather. MLSys 2021.
 - <img src="https://img.shields.io/badge/14-pages-green.svg" alt="14-pages" align="top"> [DynaTune: Dynamic Tensor Program Optimization in Deep Neural NetworkCompilation](https://openreview.net/pdf?id=GTGb3M_KcUl) - Minjia Zhang, Menghao Li, Chi Wang, Mingqin Li. ICLR 2021.
 - <img src="https://img.shields.io/badge/17-pages-green.svg" alt="17-pages" align="top"> [Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning](https://openreview.net/pdf?id=-6vS_4Kfz0) - Shauharda Khadka, Estelle Aflalo, Mattias Mardar, Avrech Ben-David, Santiago Miret, Shie Mannor, Tamir Hazan, Hanlin Tang, Somdeb Majumdar. ICLR 2021.
@@ -143,7 +143,7 @@ Ninghui Sun. ACM Transactions on Architecture and Code Optimization (TACO), 2015
 #### Domain-specific Optimisation
 - <img src="https://img.shields.io/badge/18-pages-green.svg" alt="18-pages" align="top"> [Tensor Program Optimization with Probabilistic Programs](https://arxiv.org/pdf/2205.13603.pdf) - Junru Shao, Xiyou Zhou, Siyuan Feng, Bohan Hou, Ruihang Lai, Hongyi Jin, Wuwei Lin, Masahiro Masuda, Cody Hao Yu, Tianqi Chen. NeurIPS 2022
 - <img src="https://img.shields.io/badge/11-pages-green.svg" alt="11-pages" align="top"> [moTuner: a compiler-based auto-tuning approach for mixed-precision operators](https://dl.acm.org/doi/abs/10.1145/3528416.3530231) - Zewei Mo, Zejia Lin, Xianwei Zhang, Yutong Lu. CF 2022
-- <img src="https://img.shields.io/badge/12-pages-green.svg" alt="12-pages" align="top"> [Collage: Automated Integration of Deep Learning Backends](https://arxiv.org/pdf/2111.00655.pdf) - Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, Zhihao Jia. arXiV 2022
+- <img src="https://img.shields.io/badge/12-pages-green.svg" alt="12-pages" align="top"> [Collage: Automated Integration of Deep Learning Backends](https://dl.acm.org/doi/10.1145/3559009.3569651) - Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, Zhihao Jia. PACT 2022
 - <img src="https://img.shields.io/badge/15-pages-green.svg" alt="15-pages" align="top"> [Learning Nonlinear Loop Invariants with Gated Continuous Logic Networks](https://www.cs.columbia.edu/~rgu/publications/pldi20-yao.pdf) - J. Yao, G. Ryan, J. Wong, S. Jana, and R. Gu. PLDI 2020. 
 - <img src="https://img.shields.io/badge/16-pages-green.svg" alt="16-pages" align="top"> [Learning-based Memory Allocation for C++ Server Workloads](https://www.cs.utexas.edu/users/mckinley/papers/llama-asplos-2020.pdf) - Maas, Martin, David G. Andersen, Michael Isard, Mohammad Mahdi Javanmard, Kathryn S. McKinley, and Colin Raffel. ASPLOS 2020. [presetnation](https://www.youtube.com/watch?v=gs8m5W-xdDM&feature=emb_title)
 - <img src="https://img.shields.io/badge/15-pages-green.svg" alt="15-pages" align="top"> [Bridging the gap between deep learning and sparse matrix format selection](https://people.engr.ncsu.edu/xshen5/Publications/ppopp18.pdf) - Yue Zhao, Jiajia Li, Chunhua Liao and Xipeng Shen. PPoPP 2018. 
@@ -152,7 +152,8 @@ Ninghui Sun. ACM Transactions on Architecture and Code Optimization (TACO), 2015
 
 #### Languages and Compilation
 - <img src="https://img.shields.io/badge/12-pages-green.svg" alt="12-pages" align="top"> [Halide: a language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines](https://core.ac.uk/download/pdf/20024748.pdf) - Jonathan Ragan-Kelley, Connelly Barnes, Andrew Adams, Sylvain Paris, Frédo Durand, and Saman Amarasinghe, PLDI 2013. 
-- <img src="https://img.shields.io/badge/12-pages-green.svg" alt="12-pages" align="top"> [PetaBricks: a language and compiler for algorithmic choice](http://people.csail.mit.edu/cychan/papers/2009pldi-petabricks.pdf) - Jason Ansel, Cy Chan, Yee Lok Wong, Marek Olszewski, Qin Zhao, Alan Edelman, and Saman Amarasinghe. PLDI 2009. 
+- <img src="https://img.shields.io/badge/12-pages-green.svg" alt="12-pages" align="top"> [PetaBricks: a language and compiler for algorithmic choice](http://people.csail.mit.edu/cychan/papers/2009pldi-petabricks.pdf) - Jason Ansel, Cy Chan, Yee Lok Wong, Marek Olszewski, Qin Zhao, Alan Edelman, and Saman Amarasinghe. PLDI 2009.
+- <img src="https://img.shields.io/badge/29-pages-green.svg" alt="29-pages" align="top"> [Achieving High-performance the Functional Way: a Functional Pearl on Expressing High-performance Optimizations as Rewrite Strategies](https://dl.acm.org/doi/10.1145/3408974) - Bastian Hagedorn, Johannes Lenfers, Thomas K{\oe}hler, Xueying Qin, Sergei Gorlatch, and Michel Steuwer. Proceedings of the ACM on Programming Languages 2020.
 
 #### Code Size Reduction
 - <img src="https://img.shields.io/badge/15-pages-green.svg" alt="15-pages" align="top"> [Learning Compiler Pass Orders using Coreset and Normalized Value Prediction](https://arxiv.org/pdf/2301.05104.pdf) - Youwei Liang, Kevin Stone, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh Leather, Yuandong Tian. ICML 2023.
@@ -252,6 +253,7 @@ for Extreme Summarization of Source Code](http://proceedings.mlr.press/v48/allam
 - [COBAYN](https://github.com/amirjamez/COBAYN) - Compiler Autotuning using BNs ([paper](http://amirashouri.ca/resources/COBAYN-ashouri_taco16.pdf)).
 - [OpenTuner](https://github.com/jansel/opentuner) - Framework for building domain-specific multi-objective program autotuners ([paper](http://groups.csail.mit.edu/commit/papers/2014/ansel-pact14-opentuner.pdf); [slides](http://groups.csail.mit.edu/commit/papers/2014/ansel-pact14-opentuner-slides.pdf))
 - [ONNX-MLIR](http://onnx.ai/onnx-mlir/) - Representation and Reference Lowering of ONNX Models in MLIR Compiler Infrastructure ([paper](https://arxiv.org/pdf/2008.08272.pdf)).
+- [IREE](https://github.com/openxla/iree) - A retargetable MLIR-based machine learning compiler and runtime toolkit. 
 
 ## Benchmarks and Datasets
 - [TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers](https://github.com/tlc-pack/tenset) - A dataset of tensor program performance records for six commonly used hardware platforms ([paper](https://openreview.net/pdf?id=aIfp8kLuvc9)).