• Projects
• Teaching
• Awards
• Publications
• Experience
• Talks

Assistant Professor
Computer Science Department
Carnegie Mellon University
zhihao@cmu.edu

I am an assistant professor in the Computer Science Department at Carnegie Mellon University, where I work on computer systems and machine learning as part of CMU Catalyst Group and Parallel Data Lab. Before joining CMU, I was a research scientist at Facebook. I received my PhD from the Computer Science Department at Stanford University in 2020, where I was co-advised by Alex Aiken and Matei Zaharia. Before coming to Stanford, I received my bachelor degree in Computer Science from the Special Pilot CS Class supervised by Andrew Yao at Tsinghua University.

Research interests: I am interested in building systems for emerging application domains such as machine learning, quantum computing, and large-scale data analytics. In particular, my current research focuses on (1) accelerating deep learning computations on modern hardware platforms, and (2) optimizing quantum computations on today's intermediate-scale quantum devices.

I am actively looking for strong and self-motivated students interested in building systems for machine learning and quantum computing to join my group.
(1) Prospective students: if you are interested in working with me as a Ph.D. student, please apply through the CMU CS PhD program and mention me in your application.
(2) Current CMU students: if you are already a graduate, masters, or undergraduate student at CMU, please send me an email and we can find a time to chat.
(3) Prospective students not at CMU: if you are interested in joining my group for remote collaborations, please send me an email with your CV.

Teaching

• 15-418/618 Parallel Computer Architecture: fall 2022
• 15-849 Machine Learning Systems: spring 2022

Projects

• Quartz is a quantum circuit superoptimizer that automatically generates and verifies circuit transformations for arbitrary quantum gate sets. By using these auto-generated transformations, Quartz can outperform existing quantum circuit optimizers on a diversity of gate sets.

• TASO is a Tensor Algebra SuperOptimizer for deep learning. It optimizes DNN computation graphs using automatically generated graph transformations, achieving up to 3x speedup over existing DNN frameworks. PET further extends TASO by leveraging partially equivalent transformations and automated corrections.

• FlexFlow is a deep learning engine that accelerates distributed DNN training by automatically discovering fast parallelization strategies for a specific parallel machine.

• Lux is a distributed multi-GPU system for high performance graph processing. Lux achieves fast graph processing by exploiting the aggregate memory bandwidth of multiple GPUs. Lux achieves up to 20x speedup over state-of-the-art graph processing systems.

• Legion is a high performance programming system for heterogeneous, parallel machines with complex memory hierarchies.

Awards

• Samsung GRO Research Award, 2023
• Cisco Research Award, 2023
• Amazon Research Award, 2023
• NSF CAREER Award, 2023
• Meta Research Award, 2022
• Qualcomm Innovation Fellowship, 2022
• Amazon Research Award, 2022
• Google Faculty Research Award, 2022
• Stanford Arthur Samuel Best Doctoral Thesis Award, 2020

Publications

2024

• SpotServe: Serving Generative Large Language Models on Preemptible Instances
  Xupeng Miao, Chunan Shi, Jiangfei Duan, Xiaoli Xi, Dahua Lin, Bin Cui, and Zhihao Jia
  In Proceedings of the ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), April 2024.
  

2023

• Heterogeneity-Aware, Goodput-Optimized ML-Cluster Scheduling
  Suhas Jayaram Subramanya, Daiyaan Arfeen, Shouxu Lin, Aurick Qiao, Zhihao Jia, and Greg Ganger
  In Proceedings of the Symposium on Operating Systems Principles (SOSP), October 2023.
  

• TOD: GPU-accelerated Outlier Detection via Tensor Operations
  Yue Zhao, George Chen, and Zhihao Jia
  In Proceedings of the International Conference on Very Large Data Bases (VLDB), August 2023.
  

• SDPipe: A Semi-Decentralized Framework for Heterogeneity-aware Pipeline-parallel Training
  Xupeng Miao, Yining Shi, Zhi Yang, Bin Cui and Zhihao Jia
  In Proceedings of the International Conference on Very Large Data Bases (VLDB), August 2023.
  

• 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
  In Proceedings of the Symposium on Operating Systems Design and Implementation (OSDI), July 2023.
  

• Building Verified Neural Networks for Computer Systems with Ouroboros
  Tianhao Wang, Zhihao Jia, Changliu Liu, and Cheng Tan
  In Proceedings of the Conference on Machine Learning and Systems (MLSys), June 2023.
  

• Bamboo: Making Preemptible Instances Resilient for Affordable Training of Large DNNs
  John Thorpe*, Pengzhan Zhao*, Jonathan Eyolfson, Yifan Qiao, Zhihao Jia, Minjia Zhang, Ravi Netravali, and Guoqing Harry Xu
  In Proceedings of the Symposium on Networked Systems Design and Implementation (NSDI), April 2023.
  

• TopoOpt: Optimizing the Network Topology for Distributed DNN Training
  Weiyang Wang, Moein Khazraee, Zhizhen Zhong, Zhihao Jia, Dheevatsa Mudigere, Ying Zhang, Anthony Kewitsch, and Manya Ghobadi
  In Proceedings of the Symposium on Networked Systems Design and Implementation (NSDI), April 2023.
  

2022

• Benchmarking Node Outlier Detection on Graphs
  Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu
  In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), December 2022.
  

• Collage: Seamless Integration of Deep Learning Backends with Automatic Placement
  Byungsoo Jeon, Sunghyun Park, Peiyuan Liao, Sheng Xu, Tianqi Chen, and Zhihao Jia
  In Proceedings of the International Conference on Parallel Architectures and Compilation Techniques (PACT), October 2022.
  

• Unity: Accelerating DNN Training Through Joint Optimization of Algebraic Transformations and Parallelization
  Colin Unger*, Zhihao Jia*, Wei Wu, Sina Lin, Mandeep Baines, Carlos Efrain Quintero Narvaez, Vinay Ramakrishnaiah, Nirmal Prajapati, Pat McCormick, Jamaludin Mohd-Yusof, Xi Luo, Dheevatsa Mudigere, Jongsoo Park, Misha Smelyanskiy, and Alex Aiken
  In Proceedings of the Symposium on Operating Systems Design and Implementation (OSDI), July 2022.
  

• 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
  In Proceedings of the Conference on Programming Language Design and Implementation (PLDI), June 2022.
  

• Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
  Dheevatsa Mudigere, et al.
  In Proceedings of the International Symposium of Computer Architecture (ISCA), Industry track, June 2022.
  

• GradSign: Model Performance Inference with Theoretical Insights
  Zhihao Zhang and Zhihao Jia
  In Proceedings of the International Conference on Learning Representations (ICLR), April 2022.
  

2021

• PET: Optimizing Tensor Programs with Partially Equivalent Transformations and Automated Corrections
  Haojie Wang, Jidong Zhai, Mingyu Gao, Zixuan Ma, Shizhi Tang, Liyan Zheng, Yuanzhi Li, Kaiyuan Rong, Yuanyong Chen, and Zhihao Jia
  In Proceedings of the Symposium on Operating Systems Design and Implementation (OSDI), July 2021.
  

• Dorylus: Affordable, Scalable, and Accurate GNN Training over Billion-Edge Graphs
  John Thorpe, Yifan Qiao, Jonathan Eyolfson, Shen Teng, Guanzhou Hu, Zhihao Jia, Jinliang Wei, Keval Voral, Ravi Netravali, Miryung Kim, and Guoqing Harry Xu
  In Proceedings of the Symposium on Operating Systems Design and Implementation (OSDI), July 2021.
  

• IOS: Inter-Operator Scheduler for CNN Acceleration
  Yaoyao Ding, Ligeng Zhu, Zhihao Jia, Gennady Pekhimenko, and Song Han
  In Proceedings of the Conference on Machine Learning and Systems (MLSys), April 2021.
  

• Scaling Implicit Parallelism via Dynamic Control Replication
  Michael Bauer, Wonchan Lee, Elliott Slaughter, Zhihao Jia, Mario Di Renzo, Manolis Papadakis, Galen Shipman, Pat McCormick, Michael Garland, and Alex Aiken.
  In Proceedings of the Principles and Practice of Parallel Programming (PPoPP), February 2021.
  

2020

• Automated Discovery of Machine Learning Optimizations
  Zhihao Jia.
  Ph.D. Dissertation, September 2020.
  Stanford Arthur Samuel Best Doctoral Thesis Award
  

• Redundancy-Free Computation Graphs for Graph Neural Networks
  Zhihao Jia, Sina Lin, Rex Ying, Jiaxuan You, Jure Leskovec, and Alex Aiken.
  In Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD), San Diego, CA, August 2020.
  

• Improving the Accuracy, Scalability, and Performance of Graph Neural Networks with Roc
  Zhihao Jia, Sina Lin, Mingyu Gao, Matei Zaharia, and Alex Aiken.
  In Proceedings of the 3rd Conference on Machine Learning and Systems (MLSys), Austin, TX, March 2020.
  

2019

• TASO: Optimizing Deep Learning Computation with Automated Generation of Graph Substitutions
  Zhihao Jia, Oded Padon, James Thomas, Todd Warszawski, Matei Zaharia, and Alex Aiken.
  In Proceedings of the Symposium on Operating Systems Principles (SOSP), Ontario, Canada, October 2019.
  

• Optimizing DNN Computation with Relaxed Graph Substitutions
  Zhihao Jia, James Thomas, Todd Warszawski, Mingyu Gao, Matei Zaharia, and Alex Aiken.
  In Proceedings of the 2nd Conference on Machine Learning and Systems (MLSys), Palo Alto, CA, April 2019.
  

• Beyond Data and Model Parallelism for Deep Neural Networks
  Zhihao Jia, Matei Zaharia, and Alex Aiken.
  In Proceedings of the 2nd Conference on Machine Learning and Systems (MLSys), Palo Alto, CA, April 2019.
  

2018

• A Distributed Multi-GPU System for Fast Graph Processing
  Zhihao Jia, Yongkee Kwon, Galen Shipman, Pat McCormick, Mattan Erez, and Alex Aiken.
  In Proceedings of the International Conference on Very Large Data Bases (VLDB), Rio de Janeiro, Brazil, August 2018.

• Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks
  Zhihao Jia, Sina Lin, Charles R. Qi, and Alex Aiken.
  In Proceedings of the International Conference on Machine Learning (ICML), Stockholm, Sweden, July 2018.
  (Extended version with proofs.)

• Isometry: A Path-Based Distributed Data Transfer System
  Zhihao Jia, Sean Treichler, Galen Shipman, Pat McCormick, and Alex Aiken.
  In Proceedings of the International Conference on Supercomputing (ICS), Beijing, China, June 2018.

2017 and earlier

• Integrating External Resources with a Task-Based Programming Model
  Zhihao Jia, Sean Treichler, Galen Shipman, Mike Bauer, Noah Watkins, Carlos Maltzahn, Pat McCormick, and Alex Aiken.
  In Proceedings of the International Conference on High Performance Computing, Data, and Analytics (HIPC), Jaipur, India, December 2017.

• SLIK: Scalable Low-Latency Indexes for a Key-Value Store
  Ankita Kejriwal, Arjun Gopalan, Ashish Gupta, Zhihao Jia, Stephen Yang, and John Ousterhout.
  In Proceedings of the USENIX Annual Technical Conference (ATC), Denver, CO, June 2016.

• Automatic and Transparent I/O Optimization With Storage Integrated Application Runtime Support
  Noah Watkins, Zhihao Jia, Galen Shipman, Carlos Maltzahn, Alex Aiken and Pat McCormick.
  In Proceedings of the Parallel Data Storage Workshop (PDSW), Austin, TX, November 2015.

• Improving Integer Security for Systems with KINT
  Xi Wang, Haogang Chen, Zhihao Jia, Nickolai Zeldovich, and M. Frans Kaashoek.
  In Proceedings of the Symposium on Operating Systems Design and Implementation (OSDI), Hollywood, CA, October 2012.

• Undefined Behavior: What Happened to My Code?
  Xi Wang, Haogang Chen, Alvin Cheung, Zhihao Jia, Nickolai Zeldovich, and M. Frans Kaashoek.
  In Proceedings of the Asia-Pacific Workshop on Systems (APSys), Seoul, South Korea, July 2012.

Education

• 2013.9 - 2020.6: Ph.D. candidate at Stanford University
  

• 2009.9 - 2013.6: B.Eng from Yao Class, Tsinghua University
  

• 2012.1 - 2012.5: Exchange student at MIT

Experience

• 2016.6 - 2016.9: Research intern at Los Alamos National Lab
  Working with Galen Shipman and Pat McCormick

• 2014.6 - 2014.9: Research intern at Microsoft Research Silicon Valley
  Working with Yuan Yu

• 2012.6 - 2013.6: Research intern at Microsoft Research Asia
  Working with Jiaxing Zhang and Lidong Zhou

• 2011.2 - 2012.1: Research intern at Microsoft Research Asia
  Working with Ming Wu and Lidong Zhou

Talks

• Automated Discovery of Machine Learning Optimizations
  DAWN Seminar, Stanford, January 2020
  

• TASO: Optimizing Deep Learning Computation with Automated Generation of Graph Substitutions
  Invited Talk, C4ML@CGO, San Diego, February 2020
  Google Brain, Mountain View, January 2020
  Facebook, Menlo Park, January 2020
  Nuro, Montain View, January 2020
  NVIDIA, Sunnyvale, January 2020
  Alibaba, Sunnyvale, January 2020
  TVM Conference, Seattle, December 2019
  Microsoft, Sunnyvale, November 2019
  NVIDIA, Sunnyvale, November 2019
  SOSP, Ontario, October 2019
  Stanford Software Research Lunch, Stanford, October 2019
  Stanford DAWN Retreat, Menlo Park, September 2019
  

• Search-Based Approaches to Accelerate Deep Learning
  Invited Talk, MLforSystems@ISCA, Phoenix, June 2019
  Twitter, Sunnyvale, June 2019
  Facebook, Menlo Park, May 2019
  RISELab Seminar, UC Berkeley, May 2019
  SAMPL Seminar, University of Washington, May 2019
  Stanford DAWN Retreat, Santa Cruz, March 2019
  

• Optimizing DNN Computation with Relaxed Graph Substitutions
  SysML, Palo Alto, April 2019
  

• Beyond Data and Model Parallelism for Deep Neural Networks
  SysML, Palo Alto, April 2019
  Intel, Sunnyvale, October 2018
  DAWN Seminar, Stanford, October 2018
  NVIDIA, Sunnyvale, October 2018
  Google Brain, Mountain View, August 2018
  Tsinghua University, Beijing, June 2018
  Toutiao AI Lab, Beijing, June 2018
  Alibaba, Beijing, June 2018
  Software Research Lunch, Stanford, May 2018
  SLAC, Menlo Park, May 2018

• Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks
  ICML Long Oral, Stockholm, July 2018

• A Distributed Multi-GPU System for Fast Graph Processing
  VLDB, Rio de Janeiro, August 2018
  Software Research Lunch, Stanford, June 2017

• Isometry: A Path-Based Distributed Data Transfer System
  ICS, Beijing, June 2018
  Software Research Lunch, Stanford, May 2018
  

• Integrating External Resources with a Task-Based Programming Model
  HIPC, Jaipur, India, December 2017
  

• Forsythia: Computing over Hundreds of Thousands of Potentially Spotty Machines. [slides]
  Software Research Lunch, Stanford, February 2015
  Microsoft Research Silicon Valley, Mountain View, September 2014