2023
December
|
Parcae: Proactive, Liveput-Optimized DNN Training on Preemptible Instances.
Jiangfei Duan, Ziang Song, Xupeng Miao, Xiaoli Xi, Dahua Lin, Harry Xu, Minjia Zhang, and Zhihao Jia.
NSDI 2024.
|
2023
November
|
SpotServe: Serving Generative Large Language Models on Preemptible Instances.
Xupeng Miao, Chunan Shi, Jiangfei Duan, Xiaoli Xi, Dahua Lin, Bin Cui, and Zhihao Jia.
ASPLOS 2024.
|
2023
April
|
SDPipe: A Semi-Decentralized Framework for Heterogeneity-aware Pipeline-parallel Training.
Xupeng Miao, Yining Shi, Zhi Yang, Bin Cui, and Zhihao Jia.
VLDB 2023.
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2023
March
|
EinNet: Optimizing Tensor Programs with Derivation-Based Transformations.
Liyan Zheng, Haojie Wang, Jidong Zhai, Muyan Hu, Zixuan Ma, Tuowei Wang, Shuhong Huang, Xupeng Miao, Shizhi Tang, Kezhao Huang, and Zhihao Jia.
OSDI 2023.
|
2023
March
|
SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning.
Zihao Ye, Ruihang Lai, Junru Shao, Tianqi Chen, and Luis Ceze.
ASPLOS 2023.
|
2023
March
|
TensorIR: An Abstraction for Automatic Tensorized Program Optimization.
Siyuan Feng, Bohan Hou, Hongyi Jin, Wuwei Lin, Junru Shao, Ruihang Lai, Zihao Ye, Lianmin Zheng, Cody Hao Yu, Yong Yu, and Tianqi Chen.
ASPLOS 2023.
|
2022
October
|
Collage: Seamless Integration of Deep Learning Backends with Automatic Placement.
Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, and Zhihao Jia.
PACT 2022.
|
2022
September
|
Tensor Program Optimization with Probabilistic Programs.
Junru Shao, Xiyou Zhou, Siyuan Feng, Bohan Hou, Ruihang Lai, Hongyi Jin, Wuwei Lin, Masahiro Masuda, Cody Hao Yu, and Tianqi Chen.
NeurIPS 2022.
|
2022
July
|
Unity: Accelerating DNN Training Through Joint Optimization of Algebraic Transformations and Parallelization.
Zhihao Jia, Colin Unger, Wei Wu, Sina Lin, Mandeep Baines, Vinay Ramakrishnaiah Carlos Efrain, Nirmal Prajapati, Pat McCormick, Jamaludin Mohd-Yusof, Xi Luo, Dheevatsa Mudigere, Jongsoo Park, Misha Smelyanskiy, and Alex Aiken.
OSDI 2022.
|
2022
June
|
Quartz: Superoptimization of Quantum Circuits.
Mingkuan Xu, Zikun Li, Oded Padon, Sina Lin, Jessica Pointing, Auguste Hirth, Henry Ma, Jens Palsberg, Alex Aiken, Umut A. Acar, and Zhihao Jia.
PLDI 2022.
|
2022
April
|
GradSign: Model Performance Inference with Theoretical Insights.
Zhihao Zhang and Zhihao Jia.
ICLR 2022.
|
2022
March
|
The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding.
Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, and Todd C. Mowry.
MLSys 2022.
|
2022
March
|
DietCode: Automatic Optimization for Dynamic Tensor Programs.
Bojian Zheng, Ziheng Jiang, Cody Hao Yu, Haichen Shen, Joshua Fromm, Yizhi Liu, Yida Wang, Luis Ceze, Tianqi Chen, and Gennady Pekhimenko.
MLSys 2022.
|
2021
July
|
PET: Optimizing Tensor Programs with Partially Equivalent Transformation and Automated Correction.
Haojie Wang, Jidong Zhai, Mingyu Gao, Zixuan Ma, Shizhi Tang, Liyan Zheng, Yuanzhi Li, Kaiyuan Rong, Yuanyong Chen, and Zhihao Jia.
OSDI 2021.
|
2021
April
|
IOS: Inter-Operator Scheduler for CNN Acceleration.
Yaoyao Ding, Ligeng Zhu, Zhihao Jia, Gennady Pekhimenko, and Song Han.
MLSys 2021.
|
2021
April
|
Cortex: A Compiler for Recursive Deep Learning Models.
Pratik Fegade, Tianqi Chen, Phil Gibbons, and Todd Mowry.
MLSys 2021.
|
2020
August
|
Redundancy-free computation graphs for graph neural networks.
Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, and Alex Aiken.
KDD 2020.
|
2020
March
|
Improving the accuracy, scalability, and performance of graph neural networks with roc.
Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken.
MLSys 2020.
|
2020
February
|
Automating Generation of Low Precision Deep Learning Operators.
Meghan Cowan, Thierry Moreau, Tianqi Chen, and Luis Ceze.
CGO.
|
2019
November
|
TASO: optimizing deep learning computation with automatic generation of graph substitutions.
Zhihao Jia, Oded Padon, James Thomas, Todd Warszawski, Matei Zaharia, and Alex Aiken.
SOSP 2019.
|
2019
September
|
A Hardware-Software Blueprint for Flexible Deep Learning Specialization.
Thierry Moreau, Tianqi Chen, Luis Vega, Jared Roesch, Eddie Yan, Lianmin Zheng, Josh Fromm, Ziheng Jiang, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy.
IEEE Micro 39(5).
|
2019
April
|
Beyond data and model parallelism for deep neural networks.
Zhihao Jia, Matei Zaharia, and Alex Aiken.
SysML 2019.
|
2019
April
|
Optimizing DNN Computation with Relaxed Graph Substitutions.
Zhihao Jia, James Thomas, Todd Warzawski, Mingyu Gao, Matei Zaharia, and Alex Aiken.
SysML 2019.
|
2018
December
|
Learning to Optimize Tensor Programs.
Tianqi Chen, Lianmin Zheng, Eddie Yan, Ziheng Jiang, Thierry Moreau, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy.
NeurIPS 2018.
|
2018
October
|
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning.
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Meghan Cowan, Haichen Shen, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy.
OSDI 2018.
|
2018
July
|
Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks.
Zhihao Jia, Sina Lin, Charles R. Qi, and Alex Aiken.
ICML 2018 (Proceedings of Machine Learning Research).
|
2017
November
|
A Distributed Multi-GPU System for Fast Graph Processing.
Zhihao Jia, Yongkee Kwon, Galen Shipman, Pat McCormick, Mattan Erez, and Alex Aiken.
VLDB 11(3).
|
2016
August
|
XGBoost: A Scalable Tree Boosting System.
Tianqi Chen and Carlos Guestrin.
KDD 2016.
|
2015
December
|
MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems.
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang.
LearningSys Workshop at Neural Information Processing Systems 2015.
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