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ORCID5th MLSys 2022: Santa Clara, CA, USA
- Diana Marculescu, Yuejie Chi, Carole-Jean Wu:
Proceedings of the Fifth Conference on Machine Learning and Systems, MLSys 2022, Santa Clara, CA, USA, August 29 - September 1, 2022. mlsys.org 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. - Junguk Cho, Diman Zad Tootaghaj, Lianjie Cao, Puneet Sharma:
SLA-Driven ML Inference Framework for Clouds with Hetergeneous Accelerators. - Michael Kuchnik, Ana Klimovic
, Jiri Simsa, Virginia Smith, George Amvrosiadis:
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines. - Jaeyeon Won, Jeyeon Si, Sam Son, Tae Jun Ham, Jae W. Lee:
ULPPACK: Fast Sub-8-bit Matrix Multiply on Commodity SIMD Hardware. - John Chen, Cameron R. Wolfe, Tasos Kyrillidis:
REX: Revisiting Budgeted Training with an Improved Schedule. - Ankur Mallick, Kevin Hsieh, Behnaz Arzani, Gauri Joshi:
Matchmaker: Data Drift Mitigation in Machine Learning for Large-Scale Systems. - Corey J. Nolet, Divye Gala, Edward Raff, Joe Eaton, Brad Rees, Tim Oates:
GPU Semiring Primitives for Sparse Neighborhood Methods. - Zichang Liu, Zhaozhuo Xu, Alan Baonan Ji, Junyan Zhang, Jonathan Li, Beidi Chen, Anshumali Shrivastava:
HALOS: Hashing Large Output Space for Cheap Inference. - Andrew Or, Haoyu Zhang, Michael None Freedman:
VirtualFlow: Decoupling Deep Learning Models from the Underlying Hardware. - 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. - Zhiming Hu, Angela Ning Ye, Iqbal Mohomed:
mmSampler: Efficient Frame Sampler for Multimodal Video Retrieval. - 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. - Yi Ding, Avinash Rao, Hyebin Song, Rebecca Willett, Henry Hoffmann:
NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction. - Jiarong Xing, Leyuan Wang, Shang Zhang, Jack Chen, Ang Chen, Yibo Zhu:
Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance. - 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. - Shurui Li, Puneet Gupta:
Bit-serial Weight Pools: Compression and Arbitrary Precision Execution of Neural Networks on Resource Constrained Processors. - 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. - Hesham Mostafa:
Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs. - Oznur Alkan, Dennis Wei, Massimiliano Mattetti, Rahul Nair, Elizabeth Daly, Diptikalyan Saha:
FROTE: Feedback Rule-Driven Oversampling for Editing Models. - Haotian Tang, Zhijian Liu, Xiuyu Li, Yujun Lin, Song Han:
TorchSparse: Efficient Point Cloud Inference Engine. - Donglin Zhuang, Xingyao Zhang, Shuaiwen Song, Sara Hooker:
Randomness in Neural Network Training: Characterizing the Impact of Tooling. - 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. - 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. - Xinfeng Xie, Prakash Prabhu, Ulysse Beaugnon, Phitchaya Mangpo Phothilimthana, Sudip Roy, Azalia Mirhoseini, Eugene Brevdo, James Laudon, Yanqi Zhou:
A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules. - Niketan Pansare, Jay Katukuri, Aditya Arora, Frank Cipollone, Riyaaz Shaik, Noyan Tokgozoglu, Chandru Venkataraman:
Learning Compressed Embeddings for On-Device Inference. - Hippolyt Ritter, Theofanis Karaletsos:
TyXe: Pyro-based Bayesian neural nets for Pytorch. - 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. - 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. - Kuntai Du, Qizheng Zhang, Anton Arapin, Haodong Wang, Zhengxu Xia, Junchen Jiang:
AccMPEG: Optimizing Video Encoding for Accurate Video Analytics. - 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. - Yimin Huang, Yujun Li, Hanrong Ye, Zhenguo Li, Zhihua Zhang:
Improving Model Training with Multi-fidelity Hyperparameter Evaluation. - Zirui Xu, Fuxun Yu, Jinjun Xiong, Xiang Chen:
QuadraLib: A Performant Quadratic Neural Network Library for Architecture Optimization and Design Exploration. - Zhiqiang Xie, Minjie Wang, Zihao Ye, Zheng Zhang, Rui Fan:
Graphiler: Optimizing Graph Neural Networks with Message Passing Data Flow Graph. - 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. - Ningning Xie, Tamara Norman, Dominik Grewe, Dimitrios Vytiniotis:
Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning. - Jiwon Seo:
Gyro Dropout: Maximizing Ensemble Effect in Neural Network Training. - 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. - 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. - 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. - James K. Reed, Zachary DeVito, Horace He, Ansley Ussery, Jason Ansel:
torch.fx: Practical Program Capture and Transformation for Deep Learning in Python. - Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos:
On the Utility of Gradient Compression in Distributed Training Systems. - 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. - 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. - Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry:
The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding. - Seo Jin Park, Joshua Fried, Sunghyun Kim, Mohammad Alizadeh, Adam Belay:
Efficient Strong Scaling Through Burst Parallel Training. - Aditya Desai, Li Chou, Anshumali Shrivastava:
Random Offset Block Embedding (ROBE) for compressed embedding tables in deep learning recommendation systems. - Runsheng Guo, Victor Guo, Antonio Kim, Josh Hildred, Khuzaima Daudjee:
Hydrozoa: Dynamic Hybrid-Parallel DNN Training on Serverless Containers. - Carole-Jean Wu, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, Fiona Aga Behram, Jinshi Huang, Charles Bai, Michael Gschwind, Anurag Gupta, Myle Ott, Anastasia Melnikov, Salvatore Candido, David Brooks, Geeta Chauhan, Benjamin Lee, Hsien-Hsin S. Lee, Bugra Akyildiz, Maximilian Balandat, Joe Spisak, Ravi Jain, Mike Rabbat, Kim M. Hazelwood:
Sustainable AI: Environmental Implications, Challenges and Opportunities. - 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. - Liang Luo, Peter West, Pratyush Patel, Arvind Krishnamurthy, Luis Ceze:
SRIFTY: Swift and Thrifty Distributed Neural Network Training on the Cloud. - 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.
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