MLSys

Keyphrases

Publications

2022

Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos
On the Utility of Gradient Compression in Distributed Training Systems. MLSys (2022)
Cheng Wan, Youjie Li, Ang Li, Nam Sung Kim, Yingyan Lin
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node Sampling. MLSys (2022)
Oznur Alkan, Dennis Wei, Massimiliano Mattetti, Rahul Nair, Elizabeth Daly, Diptikalyan Saha
FROTE: Feedback Rule-Driven Oversampling for Editing Models. MLSys (2022)
John Chen, Cameron R. Wolfe, Tasos Kyrillidis
REX: Revisiting Budgeted Training with an Improved Schedule. MLSys (2022)
Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry
The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding. MLSys (2022)
Corey J. Nolet, Divye Gala, Edward Raff, Joe Eaton, Brad Rees, Tim Oates
GPU Semiring Primitives for Sparse Neighborhood Methods. MLSys (2022)
Aditya Desai, Li Chou, Anshumali Shrivastava
Random Offset Block Embedding (ROBE) for compressed embedding tables in deep learning recommendation systems. MLSys (2022)
Yanqi Zhou, Xuanyi Dong, Tianjian Meng, Mingxing Tan, Berkin Akin, Daiyi Peng, Amir Yazdanbakhsh, Da Huang, Ravi Narayanaswami, James Laudon
Towards the Co-design of Neural Networks and Accelerators. MLSys (2022)
Runsheng Guo, Victor Guo, Antonio Kim, Josh Hildred, Khuzaima Daudjee
Hydrozoa: Dynamic Hybrid-Parallel DNN Training on Serverless Containers. MLSys (2022)
Hang Qiu, Ioanna Vavelidou, Jian Li, Evgenya Pergament, Pete Warden, Sandeep Chinchali, Zain Asgar, Sachin Katti
ML-EXray: Visibility into ML Deployment on the Edge. MLSys (2022)
Jiwon Seo
Gyro Dropout: Maximizing Ensemble Effect in Neural Network Training. MLSys (2022)
Jiarong Xing, Leyuan Wang, Shang Zhang, Jack Chen, Ang Chen, Yibo Zhu
Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance. MLSys (2022)
Yi Ding, Avinash Rao, Hyebin Song, Rebecca Willett, Henry Hoffmann
NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction. MLSys (2022)
Haotian Tang, Zhijian Liu, Xiuyu Li, Yujun Lin, Song Han
TorchSparse: Efficient Point Cloud Inference Engine. MLSys (2022)
Zirui Xu, Fuxun Yu, Jinjun Xiong, Xiang Chen
QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration. MLSys (2022)
Jinhyun So, Corey J. Nolet, Chien-Sheng Yang, Songze Li, Qian Yu, Ramy E. Ali, Basak Guler, Salman Avestimehr
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning. MLSys (2022)
Kuntai Du, Qizheng Zhang, Anton Arapin, Haodong Wang, Zhengxu Xia, Junchen Jiang
AccMPEG: Optimizing Video Encoding for Accurate Video Analytics. MLSys (2022)
Donglin Zhuang, Xingyao Zhang, Shuaiwen Song, Sara Hooker
Randomness in Neural Network Training: Characterizing the Impact of Tooling. MLSys (2022)
Seo Jin Park, Joshua Fried, Sunghyun Kim, Mohammad Alizadeh, Adam Belay
Efficient Strong Scaling Through Burst Parallel Training. MLSys (2022)
Jaeyeon Won, Jeyeon Si, Sam Son, Tae Jun Ham, Jae W. Lee
ULPPACK: Fast Sub-8-bit Matrix Multiply on Commodity SIMD Hardware. MLSys (2022)
Samuel Alexander Stein, Betis Baheri, Daniel Chen, Ying Mao, Qiang Guan, Ang Li, Shuai Xu, Caiwen Ding
QuClassi: A Hybrid Deep Neural Network Architecture based on Quantum State Fidelity. MLSys (2022)
Zhiming Hu, Angela Ning Ye, Iqbal Mohomed
mmSampler: Efficient Frame Sampler for Multimodal Video Retrieval. MLSys (2022)
Jie Zhao, Xiong Gao, Ruijie Xia, Zhaochuang Zhang, Deshi Chen, Lei Chen, Renwei Zhang, Zhen Geng, Bin Cheng, Xuefeng Jin
Apollo: Automatic Partition-based Operator Fusion through Layer by Layer Optimization. MLSys (2022)
Pradeep Dogga, Karthik Narasimhan, Anirudh Sivaraman, Shiv Kumar Saini, George Varghese, Ravi Netravali
Revelio: ML-Generated Debugging Queries for Finding Root Causes in Distributed Systems. MLSys (2022)
Dzmitry Huba, John Nguyen, Kshitiz Malik, Ruiyu Zhu, Mike Rabbat, Ashkan Yousefpour, Carole-Jean Wu, Hongyuan Zhan, Pavel Ustinov, Harish Srinivas, Kaikai Wang, Anthony Shoumikhin, Jesik Min, Mani Malek
PAPAYA: Practical, Private, and Scalable Federated Learning. MLSys (2022)
Hippolyt Ritter, Theofanis Karaletsos
TyXe: Pyro-based Bayesian neural nets for Pytorch. MLSys (2022)
Kartikeya Bhardwaj, Milos Milosavljevic, Liam O'Neil, Dibakar Gope, Ramon Matas Navarro, Alex Chalfin, Naveen Suda, Lingchuan Meng, Danny Loh
Collapsible Linear Blocks for Super-Efficient Super Resolution. MLSys (2022)
Shurui Li, Puneet Gupta
Bit-serial Weight Pools: Compression and Arbitrary Precision Execution of Neural Networks on Resource Constrained Processors. MLSys (2022)
Wasu Piriyakulkij, Cristina Menghini, Ross Briden, Nihal V. Nayak, Jeffrey Zhu, Elaheh Raisi, Stephen H. Bach
TAGLETS: A System for Automatic Semi-Supervised Learning with Auxiliary Data. MLSys (2022)
Bojian Zheng, Ziheng Jiang, Cody Hao Yu, Haichen Shen, Joshua Fromm, Yizhi Liu, Yida Wang, Luis Ceze, Tianqi Chen, Gennady Pekhimenko
DietCode: Automatic Optimization for Dynamic Tensor Programs. MLSys (2022)
Liang Luo, Peter West, Pratyush Patel, Arvind Krishnamurthy, Luis Ceze
SRIFTY: Swift and Thrifty Distributed Neural Network Training on the Cloud. MLSys (2022)
Proceedings of Machine Learning and Systems 2022, MLSys 2022, Santa Clara, CA, USA, August 29 - September 1, 2022 MLSys (2022)
Niketan Pansare, Jay Katukuri, Aditya Arora, Frank Cipollone, Riyaaz Shaik, Noyan Tokgozoglu, Chandru Venkataraman
Learning Compressed Embeddings for On-Device Inference. MLSys (2022)
Andrew Or, Haoyu Zhang, Michael None Freedman
VirtualFlow: Decoupling Deep Learning Models from the Underlying Hardware. MLSys (2022)
Wei Hao, Aahil Awatramani, Jiayang Hu, Chengzhi Mao, Pin-Chun Chen, Eyal Cidon, Asaf Cidon, Junfeng Yang
A Tale of Two Models: Constructing Evasive Attacks on Edge Models. MLSys (2022)
Meet P. Vadera, Jinyang Li, Adam D. Cobb, Brian Jalaian, Tarek F. Abdelzaher, Benjamin M. Marlin
URSABench: A System for Comprehensive Benchmarking of Bayesian Deep Neural Network Models and Inference methods. MLSys (2022)
James K. Reed, Zachary DeVito, Horace He, Ansley Ussery, Jason Ansel
torch.fx: Practical Program Capture and Transformation for Deep Learning in Python. MLSys (2022)
Junguk Cho, Diman Zad Tootaghaj, Lianjie Cao, Puneet Sharma
SLA-Driven ML Inference Framework for Clouds with Hetergeneous Accelerators. MLSys (2022)
Hengrui Zhang, Zhongming Yu, Guohao Dai, Guyue Huang, Yufei Ding, Yuan Xie, Yu Wang
Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective. MLSys (2022)
Hesham Mostafa
Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs. MLSys (2022)
Hanpeng Hu, Chenyu Jiang, Yuchen Zhong, Yanghua Peng, Chuan Wu, Yibo Zhu, Haibin Lin, Chuanxiong Guo
dPRO: A Generic Performance Diagnosis and Optimization Toolkit for Expediting Distributed DNN Training. MLSys (2022)
Vijay Janapa Reddi, David Kanter, Peter Mattson, Jared Duke, Thai Nguyen, Ramesh Chukka, Kenneth Shiring, Koan-Sin Tan, Mark Charlebois, William Chou, Mostafa El-Khamy, Jungwook Hong, Tom St. John, Cindy Trinh, Michael Buch, Mark Mazumder, Relja Markovic, Thomas Atta-fosu, Fatih Çakir, Masoud Charkhabi, Xiaodong Chen, Cheng-Ming Chiang, Dave Dexter, Terry Heo, Guenther Schmuelling, Maryam Shabani, Dylan Zika
MLPerf Mobile Inference Benchmark: An Industry-Standard Open-Source Machine Learning Benchmark for On-Device AI. MLSys (2022)
Zichang Liu, Zhaozhuo Xu, Alan Baonan Ji, Junyan Zhang, Jonathan Li, Beidi Chen, Anshumali Shrivastava
HALOS: Hashing Large Output Space for Cheap Inference. MLSys (2022)
Ankur Mallick, Kevin Hsieh, Behnaz Arzani, Gauri Joshi
Matchmaker: Data Drift Mitigation in Machine Learning for Large-Scale Systems. MLSys (2022)
Zhiqiang Xie, Minjie Wang, Zihao Ye, Zheng Zhang, Rui Fan
Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph. MLSys (2022)
Yimin Huang, Yujun Li, Hanrong Ye, Zhenguo Li, Zhihua Zhang
Improving Model Training with Multi-fidelity Hyperparameter Evaluation. MLSys (2022)
Michael Kuchnik, Ana Klimovic, Jiri Simsa, Virginia Smith, George Amvrosiadis
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines. MLSys (2022)
Tim Kaler, Nickolas Stathas, Anne Ouyang, Alexandros-Stavros Iliopoulos, Tao B. Schardl, Charles E. Leiserson, Jie Chen
Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining. MLSys (2022)
Ningning Xie, Tamara Norman, Dominik Grewe, Dimitrios Vytiniotis
Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning. MLSys (2022)
Paul Barham, Aakanksha Chowdhery, Jeff Dean, Sanjay Ghemawat, Steven Hand, Dan Hurt, Michael Isard, Hyeontaek Lim, Ruoming Pang, Sudip Roy, Brennan Saeta, Parker Schuh, Ryan Sepassi, Laurent El Shafey, Chandramohan A. Thekkath, Yonghui Wu
Pathways: Asynchronous Distributed Dataflow for ML. MLSys (2022)