Shivaram Venkataraman

133 papers A* 14A 14B 1Misc 6Journal 70Unranked 27

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	From Good to Great: Parameter Tuning in Memory Tiering Systems. IEEE Trans. Computers Konstantinos Kanellis, Sujay Yadalam, Hayden Coffey, Shivaram Venkataraman, Michael Swift
2026	J	`jnl`	Wattchmen: Watching the Wattchers - High Fidelity, Flexible GPU Energy Modeling. CoRR Brandon Tran, Matthias Maiterth, Woong Shin, Matthew D. Sinclair, Shivaram Venkataraman
2025	J	`jnl`	AI Benchmark Democratization and Carpentry. CoRR Gregor von Laszewski, Wesley Brewer, Jeyan Thiyagalingam, Juri Papay, Armstrong Foundjem, Piotr Luszczek, Murali Emani, Shirley V. Moore, Vijay Janapa Reddi, Matthew D. Sinclair, Sebastian Lobentanzer, Sujata S. Goswami, Benjamin Hawks, Marco Colombo, Nhan Tran, Christine R. Kirkpatrick, Abdulkareem Alsudais, Gregg Barrett, Tianhao Li, Kirsten N. Morehouse, Shivaram Venkataraman, Rutwik Jain, Kartik Mathur, Victor Lu, Tejinder Singh, Khojasteh Z. Mirza, Kongtao Chen, Sasidhar Kunapuli, Gavin Farrell, Renato Umeton, Geoffrey C. Fox
2025	J	`jnl`	ARMS: Adaptive and Robust Memory Tiering System. CoRR Sujay Yadalam, Konstantinos Kanellis, Michael Swift, Shivaram Venkataraman
2025	J	`jnl`	An MLCommons Scientific Benchmarks Ontology. CoRR Benjamin Hawks, Gregor von Laszewski, Matthew D. Sinclair, Marco Colombo, Shivaram Venkataraman, Rutwik Jain, Yiwei Jiang, Nhan Tran, Geoffrey C. Fox
2025	J	`jnl`	Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networks. CoRR Roger Waleffe, Devesh Sarda, Jason Mohoney, Emmanouil-Vasileios Vlatakis-Gkaragkounis, Theodoros Rekatsinas, Shivaram Venkataraman
2025	—	`conf`	Decoding Speculative Decoding. NAACL (Long Papers) Minghao Yan, Saurabh Agarwal, Shivaram Venkataraman
2025	—	`conf`	Diamond: Harnessing GPU Resources for Scientific Deep Learning. eScience Haotian Xie, Rohan Marwaha, Minu Mathew, Song Bian, Gengcong Yang, Minghao Yan, Yadu N. Babuji, Owen Price, Yinzhi Wang, Volodymyr V. Kindratenko, Shivaram Venkataraman, Kyle Chard, Ian T. Foster, Zhao Zhang
2025	A	`conf`	Eva: Cost-Efficient Cloud-Based Cluster Scheduling. EuroSys Tzu-Tao Chang, Shivaram Venkataraman
2025	J	`jnl`	Eva: Cost-Efficient Cloud-Based Cluster Scheduling. CoRR Tzu-Tao Chang, Shivaram Venkataraman
2025	—	`conf`	Exploring Distributed Vector Databases Performance on HPC Platforms: A Study with Qdrant. SC Workshops Seth Ockerman, Amal Gueroudji, Song Young Oh, Robert Underwood, Nicholas Chia, Kyle Chard, Robert B. Ross, Shivaram Venkataraman
2025	J	`jnl`	Exploring Distributed Vector Databases Performance on HPC Platforms: A Study with Qdrant. CoRR Seth Ockerman, Amal Gueroudji, Song Young Oh, Robert Underwood, Nicholas Chia, Kyle Chard, Robert B. Ross, Shivaram Venkataraman
2025	J	`jnl`	From FASTER to F2: Evolving Concurrent Key-Value Store Designs for Large Skewed Workloads. Proc. VLDB Endow. Konstantinos Kanellis, Badrish Chandramouli, Ted Hart, Shivaram Venkataraman
2025	J	`jnl`	From Good to Great: Improving Memory Tiering Performance Through Parameter Tuning. CoRR Konstantinos Kanellis, Sujay Yadalam, Fanchao Chen, Michael Swift, Shivaram Venkataraman
2025	A*	`conf`	LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models. ICML Tzu-Tao Chang, Shivaram Venkataraman
2025	J	`jnl`	LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models. CoRR Tzu-Tao Chang, Shivaram Venkataraman
2025	A	`conf`	PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training. SC Seth Ockerman, Amal Gueroudji, Tanwi Mallick, Yixuan He, Line Pouchard, Robert B. Ross, Shivaram Venkataraman
2025	J	`jnl`	PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training. CoRR Seth Ockerman, Amal Gueroudji, Tanwi Mallick, Yixuan He, Line Pouchard, Robert B. Ross, Shivaram Venkataraman
2025	J	`jnl`	PLoRA: Efficient LoRA Hyperparameter Tuning for Large Models. CoRR Minghao Yan, Zhuang Wang, Zhen Jia, Shivaram Venkataraman, Yida Wang
2025	A*	`conf`	Quake: Adaptive Indexing for Vector Search. OSDI Jason Mohoney, Devesh Sarda, Mengze Tang, Shihabur Rahman Chowdhury, Anil Pacaci, Ihab F. Ilyas, Theodoros Rekatsinas, Shivaram Venkataraman
2025	J	`jnl`	Quake: Adaptive Indexing for Vector Search. CoRR Jason Mohoney, Devesh Sarda, Mengze Tang, Shihabur Rahman Chowdhury, Anil Pacaci, Ihab F. Ilyas, Theodoros Rekatsinas, Shivaram Venkataraman
2025	A*	`conf`	Scaling Inference-Efficient Language Models. ICML Song Bian, Minghao Yan, Shivaram Venkataraman
2025	J	`jnl`	Scaling Inference-Efficient Language Models. CoRR Song Bian, Minghao Yan, Shivaram Venkataraman
2025	J	`jnl`	Scaling Laws Meet Model Architecture: Toward Inference-Efficient LLMs. CoRR Song Bian, Tao Yu, Shivaram Venkataraman, Youngsuk Park
2025	—	`conf`	Striking the Right Chord: Parameter Tuning in Memory Tiering Systems. DIMES@SOSP Konstantinos Kanellis, Sujay Yadalam, Shivaram Venkataraman, Michael Swift
2025	A	`conf`	TUNA: Tuning Unstable and Noisy Cloud Applications. EuroSys Johannes Freischuetz, Konstantinos Kanellis, Brian Kroth, Shivaram Venkataraman
2025	J	`jnl`	TUNA: Tuning Unstable and Noisy Cloud Applications. CoRR Johannes Freischuetz, Konstantinos Kanellis, Brian Kroth, Shivaram Venkataraman
2025	J	`jnl`	Tesserae: Scalable Placement Policies for Deep Learning Workloads. CoRR Song Bian, Saurabh Agarwal, Md. Tareq Mahmood, Shivaram Venkataraman
2025	J	`jnl`	The Cambridge Report on Database Research. CoRR Anastasia Ailamaki, Samuel Madden, Daniel Abadi, Gustavo Alonso, Sihem Amer-Yahia, Magdalena Balazinska, Philip A. Bernstein, Peter Boncz, Michael J. Cafarella, Surajit Chaudhuri, Susan B. Davidson, David J. DeWitt, Yanlei Diao, Xin Luna Dong, Michael J. Franklin, Juliana Freire, Johannes Gehrke, Alon Y. Halevy, Joseph M. Hellerstein, Mark D. Hill, Stratos Idreos, Yannis E. Ioannidis, Christoph Koch, Donald Kossmann, Tim Kraska, Arun Kumar, Guoliang Li, Volker Markl, Renée J. Miller, C. Mohan, Thomas Neumann, Beng Chin Ooi, Fatma Ozcan, Aditya G. Parameswaran, Ippokratis Pandis, Jignesh M. Patel, Andrew Pavlo, Danica Porobic, Viktor Sanca, Michael Stonebraker, Julia Stoyanovich, Dan Suciu, Wang-Chiew Tan, Shivaram Venkataraman, Matei Zaharia, Stanley B. Zdonik
2025	J	`jnl`	What Limits Agentic Systems Efficiency? CoRR Song Bian, Minghao Yan, Anand Jayarajan, Gennady Pekhimenko, Shivaram Venkataraman
2024	A	`conf`	Blox: A Modular Toolkit for Deep Learning Schedulers. EuroSys Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman
2024	A*	`conf`	CHAI: Clustered Head Attention for Efficient LLM Inference. ICML Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu
2024	J	`jnl`	CHAI: Clustered Head Attention for Efficient LLM Inference. CoRR Saurabh Agarwal, Bilge Acun, Basil Hosmer, Mostafa Elhoushi, Yejin Lee, Shivaram Venkataraman, Dimitris Papailiopoulos, Carole-Jean Wu
2024	J	`jnl`	Decoding Speculative Decoding. CoRR Minghao Yan, Saurabh Agarwal, Shivaram Venkataraman
2024	—	`conf`	Does Compressing Activations Help Model Parallel Training? MLSys Song Bian, Dacheng Li, Hongyi Wang, Eric P. Xing, Shivaram Venkataraman
2024	J	`jnl`	GraphSnapShot: Graph Machine Learning Acceleration with Fast Storage and Retrieval. CoRR Dong Liu, Roger Waleffe, Meng Jiang, Shivaram Venkataraman
2024	J	`jnl`	Incremental IVF Index Maintenance for Streaming Vector Search. CoRR Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury, Umar Farooq Minhas, Jeffrey Pound, Cédric Renggli, Nima Reyhani, Ihab F. Ilyas, Theodoros Rekatsinas, Shivaram Venkataraman
2024	—	`conf`	Nautilus: A Benchmarking Platform for DBMS Knob Tuning. DEEM@SIGMOD Konstantinos Kanellis, Johannes Freischuetz, Shivaram Venkataraman
2024	A	`conf`	PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters. SC Rutwik Jain, Brandon Tran, Keting Chen, Matthew D. Sinclair, Shivaram Venkataraman
2024	J	`jnl`	PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters. CoRR Rutwik Jain, Brandon Tran, Keting Chen, Matthew D. Sinclair, Shivaram Venkataraman
2024	J	`jnl`	SYMPHONY: Improving Memory Management for LLM Inference Workloads. CoRR Saurabh Agarwal, Anyong Mao, Aditya Akella, Shivaram Venkataraman
2023	A*	`conf`	Bagpipe: Accelerating Deep Recommendation Model Training. SOSP Saurabh Agarwal, Chengpo Yan, Ziyi Zhang, Shivaram Venkataraman
2023	J	`jnl`	Blox: A Modular Toolkit for Deep Learning Schedulers. CoRR Saurabh Agarwal, Amar Phanishayee, Shivaram Venkataraman
2023	J	`jnl`	Does compressing activations help model parallel training? CoRR Song Bian, Dacheng Li, Hongyi Wang, Eric P. Xing, Shivaram Venkataraman
2023	J	`jnl`	F2: Designing a Key-Value Store for Large Skewed Workloads. CoRR Konstantinos Kanellis, Badrish Chandramouli, Shivaram Venkataraman
2023	A	`conf`	MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks. EuroSys Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman
2023	A	`conf`	Mirage: Towards Low-interruption Services on Batch GPU Clusters with Reinforcement Learning. SC Qiyang Ding, Pengfei Zheng, Shreyas Kudari, Shivaram Venkataraman, Zhao Zhang
2023	J	`jnl`	Mirage: Towards Low-interruption Services on Batch GPU Clusters with Reinforcement Learning. CoRR Qiyang Ding, Pengfei Zheng, Shreyas Kudari, Shivaram Venkataraman, Zhao Zhang
2023	J	`jnl`	PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices. CoRR Minghao Yan, Hongyi Wang, Shivaram Venkataraman
2023	Misc	`conf`	Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning. NSDI Pengfei Zheng, Rui Pan, Tarannum Khan, Shivaram Venkataraman, Aditya Akella
2022	J	`jnl`	BagPipe: Accelerating Deep Recommendation Model Training. CoRR Saurabh Agarwal, Ziyi Zhang, Shivaram Venkataraman
2022	J	`jnl`	LlamaTune: Sample-Efficient DBMS Configuration Tuning. CoRR Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, Shivaram Venkataraman
2022	J	`jnl`	LlamaTune: Sample-Efficient DBMS Configuration Tuning. Proc. VLDB Endow. Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas Müller, Carlo Curino, Shivaram Venkataraman
2022	J	`jnl`	Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine. CoRR Roger Waleffe, Jason Mohoney, Theodoros Rekatsinas, Shivaram Venkataraman
2022	A	`conf`	Not All GPUs Are Created Equal: Characterizing Variability in Large-Scale, Accelerator-Rich Systems. SC Prasoon Sinha, Akhil Guliani, Rutwik Jain, Brandon Tran, Matthew D. Sinclair, Shivaram Venkataraman
2022	J	`jnl`	Not All GPUs Are Created Equal: Characterizing Variability in Large-Scale, Accelerator-Rich Systems. CoRR Prasoon Sinha, Akhil Guliani, Rutwik Jain, Brandon Tran, Matthew D. Sinclair, Shivaram Venkataraman
2022	—	`conf`	On the Utility of Gradient Compression in Distributed Training Systems. MLSys Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos
2022	J	`jnl`	Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning. CoRR Pengfei Zheng, Rui Pan, Tarannum Khan, Shivaram Venkataraman, Aditya Akella
2021	J	`jnl`	Accelerating Deep Learning Inference via Learned Caches. CoRR Arjun Balasubramanian, Adarsh Kumar, Yuhan Liu, Han Cao, Shivaram Venkataraman, Aditya Akella
2021	—	`conf`	Adaptive Gradient Communication via Critical Learning Regime Identification. MLSys Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos
2021	—	`conf`	Atoll: A Scalable Low-Latency Serverless Platform. SoCC Arjun Singhvi, Arjun Balasubramanian, Kevin Houck, Mohammed Danish Shaikh, Shivaram Venkataraman, Aditya Akella
2021	J	`jnl`	AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning. CoRR Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman
2021	J	`jnl`	Demonstration of Marius: Graph Embeddings with a Single Machine. Proc. VLDB Endow. Anders Carlsson, Anze Xie, Jason Mohoney, Roger Waleffe, Shanan Peters, Theodoros Rekatsinas, Shivaram Venkataraman
2021	J	`jnl`	Doing More by Doing Less: How Structured Partial Backpropagation Improves Deep Learning Clusters. CoRR Adarsh Kumar, Kausik Subramanian, Shivaram Venkataraman, Aditya Akella
2021	—	`conf`	Doing more by doing less: how structured partial backpropagation improves deep learning clusters. DistributedML@CoNEXT Adarsh Kumar, Kausik Subramanian, Shivaram Venkataraman, Aditya Akella
2021	J	`jnl`	KAISA: An Adaptive Second-order Optimizer Framework for Deep Neural Networks. CoRR J. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian T. Foster, Zhao Zhang
2021	A	`conf`	KAISA: an adaptive second-order optimizer framework for deep neural networks. SC J. Gregory Pauloski, Qi Huang, Lei Huang, Shivaram Venkataraman, Kyle Chard, Ian T. Foster, Zhao Zhang
2021	J	`jnl`	Learning Massive Graph Embeddings on a Single Machine. CoRR Jason Mohoney, Roger Waleffe, Yiheng Xu, Theodoros Rekatsinas, Shivaram Venkataraman
2021	A*	`conf`	Marius: Learning Massive Graph Embeddings on a Single Machine. OSDI Jason Mohoney, Roger Waleffe, Henry Xu, Theodoros Rekatsinas, Shivaram Venkataraman
2021	Misc	`conf`	Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo. NSDI Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai, Rahul Potharaju
2021	J	`jnl`	On the Utility of Gradient Compression in Distributed Training Systems. CoRR Saurabh Agarwal, Hongyi Wang, Shivaram Venkataraman, Dimitris S. Papailiopoulos
2021	J	`jnl`	The Roaming Edge and its Applications. GetMobile Mob. Comput. Commun. Suman Banerjee, Remzi H. Arpaci-Dusseau, Shenghong Dai, Kassem Fawaz, Mohit Gupta, Kangwook Lee, Shivaram Venkataraman
2020	J	`jnl`	Accelerating Deep Learning Inference via Freezing. CoRR Adarsh Kumar, Arjun Balasubramanian, Shivaram Venkataraman, Aditya Akella
2020	J	`jnl`	Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification. CoRR Saurabh Agarwal, Hongyi Wang, Kangwook Lee, Shivaram Venkataraman, Dimitris S. Papailiopoulos
2020	—	`conf`	Blink: Fast and Generic Collectives for Distributed ML. MLSys Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil R. Devanur, Ion Stoica
2020	J	`jnl`	Learning-Based Coded Computation. IEEE J. Sel. Areas Inf. Theory Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
2020	J	`jnl`	Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo. CoRR Le Xu, Shivaram Venkataraman, Indranil Gupta, Luo Mai, Rahul Potharaju
2020	—	`conf`	Serverless linear algebra. SoCC Vaishaal Shankar, Karl Krauth, Kailas Vodrahalli, Qifan Pu, Benjamin Recht, Ion Stoica, Jonathan Ragan-Kelley, Eric Jonas, Shivaram Venkataraman
2020	Misc	`conf`	Themis: Fair and Efficient GPU Cluster Scheduling. NSDI Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla
2020	—	`conf`	Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs. HotStorage Konstantinos Kanellis, Ramnatthan Alagappan, Shivaram Venkataraman
2019	—	`conf`	Accelerating Deep Learning Inference via Freezing. HotCloud Adarsh Kumar, Arjun Balasubramanian, Shivaram Venkataraman, Aditya Akella
2019	—	`conf`	Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads. USENIX ATC Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang
2019	J	`jnl`	Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads. CoRR Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang
2019	J	`jnl`	Archipelago: A Scalable Low-Latency Serverless Platform. CoRR Arjun Singhvi, Kevin Houck, Arjun Balasubramanian, Mohammed Danish Shaikh, Shivaram Venkataraman, Aditya Akella
2019	J	`jnl`	Blink: Fast and Generic Collectives for Distributed ML. CoRR Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil R. Devanur, Ion Stoica
2019	—	`conf`	Cracking open the DNN black-box: Video Analytics with DNNs across the Camera-Cloud Boundary. HotEdgeVideo@MobiCom John Emmons, Sadjad Fouladi, Ganesh Ananthanarayanan, Shivaram Venkataraman, Silvio Savarese, Keith Winstein
2019	J	`jnl`	Parity Models: A General Framework for Coding-Based Resilience in ML Inference. CoRR Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
2019	A*	`conf`	Parity models: erasure-coded resilience for prediction serving systems. SOSP Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
2019	A	`conf`	Serverless Event-Stream Processing over Virtual Actors. CIDR Philip A. Bernstein, Todd Porter, Rahul Potharaju, Alejandro Z. Tomsic, Shivaram Venkataraman, Wentao Wu
2019	Misc	`conf`	Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure. NSDI Qifan Pu, Shivaram Venkataraman, Ion Stoica
2019	J	`jnl`	SysML: The New Frontier of Machine Learning Systems. CoRR Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar
2019	—	`conf`	The Case for Unifying Data Loading in Machine Learning Clusters. HotCloud Aarati Kakaraparthy, Abhay Venkatesh, Amar Phanishayee, Shivaram Venkataraman
2019	J	`jnl`	Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads. CoRR Kshiteej Mahajan, Arjun Singhvi, Arjun Balasubramanian, Varun Batra, Surya Teja Chavali, Shivaram Venkataraman, Aditya Akella, Amar Phanishayee, Shuchi Chawla
2018	A*	`conf`	ASAP: Fast, Approximate Graph Pattern Mining at Scale. OSDI Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica
2018	—	`conf`	Bridging the GAP: towards approximate graph analytics. GRADES/NDA@SIGMOD/PODS Anand Padmanabha Iyer, Aurojit Panda, Shivaram Venkataraman, Mosharaf Chowdhury, Aditya Akella, Scott Shenker, Ion Stoica
2018	J	`jnl`	Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems. Proc. VLDB Endow. Luo Mai, Kai Zeng, Rahul Potharaju, Le Xu, Steve Suh, Shivaram Venkataraman, Paolo Costa, Terry Kim, Saravanam Muthukrishnan, Vamsi Kuppa, Sudheer Dhulipalla, Sriram Rao
2018	A*	`conf`	Focus: Querying Large Video Datasets with Low Latency and Low Cost. OSDI Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodík, Shivaram Venkataraman, Paramvir Bahl, Matthai Philipose, Phillip B. Gibbons, Onur Mutlu
2018	J	`jnl`	Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation. CoRR Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
2018	—	`conf`	Towards Fast and Scalable Graph Pattern Mining. HotCloud Anand Padmanabha Iyer, Zaoxing Liu, Xin Jin, Shivaram Venkataraman, Vladimir Braverman, Ion Stoica
2018	J	`jnl`	numpywren: serverless linear algebra. CoRR Vaishaal Shankar, Karl Krauth, Qifan Pu, Eric Jonas, Shivaram Venkataraman, Ion Stoica, Benjamin Recht, Jonathan Ragan-Kelley
2017	A*	`conf`	Breaking Locality Accelerates Block Gauss-Seidel. ICML Stephen Tu, Shivaram Venkataraman, Ashia C. Wilson, Alex Gittens, Michael I. Jordan, Benjamin Recht
2017	Misc	`conf`	CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics. NSDI Omid Alipourfard, Hongqiang Harry Liu, Jianshu Chen, Shivaram Venkataraman, Minlan Yu, Ming Zhang
2017	A*	`conf`	Drizzle: Fast and Adaptable Stream Processing at Scale. SOSP Shivaram Venkataraman, Aurojit Panda, Kay Ousterhout, Michael Armbrust, Ali Ghodsi, Michael J. Franklin, Benjamin Recht, Ion Stoica
2017	J	`jnl`	Hemingway: Modeling Distributed Optimization Algorithms. CoRR Xinghao Pan, Shivaram Venkataraman, Zizheng Tai, Joseph Gonzalez
2017	A*	`conf`	KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics. ICDE Evan Randall Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht
2017	J	`jnl`	Occupy the Cloud: Distributed Computing for the 99%. CoRR Eric Jonas, Shivaram Venkataraman, Ion Stoica, Benjamin Recht
2017	—	`conf`	Occupy the cloud: distributed computing for the 99%. SoCC Eric Jonas, Qifan Pu, Shivaram Venkataraman, Ion Stoica, Benjamin Recht
2017	—	`—`	System Design for Large Scale Machine Learning. Shivaram Venkataraman
2016	J	`jnl`	Apache Spark: a unified engine for big data processing. Commun. ACM Matei Zaharia, Reynold S. Xin, Patrick Wendell, Tathagata Das, Michael Armbrust, Ankur Dave, Xiangrui Meng, Josh Rosen, Shivaram Venkataraman, Michael J. Franklin, Ali Ghodsi, Joseph Gonzalez, Scott Shenker, Ion Stoica
2016	Misc	`conf`	Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics. NSDI Shivaram Venkataraman, Zongheng Yang, Michael J. Franklin, Benjamin Recht, Ion Stoica
2016	J	`jnl`	KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics. CoRR Evan Randall Sparks, Shivaram Venkataraman, Tomer Kaftan, Michael J. Franklin, Benjamin Recht
2016	J	`jnl`	Large Scale Kernel Learning using Block Coordinate Descent. CoRR Stephen Tu, Rebecca Roelofs, Shivaram Venkataraman, Benjamin Recht
2016	J	`jnl`	MLlib: Machine Learning in Apache Spark. J. Mach. Learn. Res. Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan Randall Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, D. B. Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar
2016	A*	`conf`	Matrix Computations and Optimization in Apache Spark. KDD Reza Bosagh Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan Randall Sparks, Aaron Staple, Matei Zaharia
2016	—	`conf`	SparkR: Scaling R Programs with Spark. SIGMOD Conference Shivaram Venkataraman, Zongheng Yang, Davies Liu, Eric Liang, Hossein Falaki, Xiangrui Meng, Reynold Xin, Ali Ghodsi, Michael J. Franklin, Ion Stoica, Matei Zaharia
2015	J	`jnl`	MLlib: Machine Learning in Apache Spark. CoRR Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan Randall Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, D. B. Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar
2015	J	`jnl`	linalg: Matrix Computations in Apache Spark. CoRR Reza Bosagh Zadeh, Xiangrui Meng, Burak Yavuz, Aaron Staple, Li Pu, Shivaram Venkataraman, Evan Randall Sparks, Alexander Ulanov, Matei Zaharia
2014	J	`jnl`	Quantifying eventual consistency with PBS. Commun. ACM Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
2014	J	`jnl`	Quantifying eventual consistency with PBS. VLDB J. Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
2014	—	`conf`	Record Placement Based on Data Skew Using Solid State Drives. BPOE@ASPLOS/VLDB Jun Suzuki, Shivaram Venkataraman, Sameer Agarwal, Michael J. Franklin, Ion Stoica
2014	A*	`conf`	The Power of Choice in Data-Aware Cluster Scheduling. OSDI Shivaram Venkataraman, Aurojit Panda, Ganesh Ananthanarayanan, Michael J. Franklin, Ion Stoica
2013	—	`conf`	PBS at work: advancing data management with consistency metrics. SIGMOD Conference Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
2013	A	`conf`	Presto: distributed machine learning and graph processing with sparse matrices. EuroSys Shivaram Venkataraman, Erik Bodzsar, Indrajit Roy, Alvin AuYoung, Robert S. Schreiber
2013	A	`conf`	The Case for Tiny Tasks in Compute Clusters. HotOS Kay Ousterhout, Aurojit Panda, Josh Rosen, Shivaram Venkataraman, Reynold Xin, Sylvia Ratnasamy, Scott Shenker, Ion Stoica
2012	—	`conf`	Cake: enabling high-level SLOs on shared storage systems. SoCC Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Randy H. Katz, Ion Stoica
2012	J	`jnl`	Probabilistically Bounded Staleness for Practical Partial Quorums CoRR Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
2012	J	`jnl`	Probabilistically Bounded Staleness for Practical Partial Quorums. Proc. VLDB Endow. Peter Bailis, Shivaram Venkataraman, Michael J. Franklin, Joseph M. Hellerstein, Ion Stoica
2012	—	`conf`	Sweet Storage SLOs with Frosting. HotCloud Andrew Wang, Shivaram Venkataraman, Sara Alspaugh, Ion Stoica, Randy H. Katz
2012	—	`conf`	Using R for Iterative and Incremental Processing. HotCloud Shivaram Venkataraman, Indrajit Roy, Alvin AuYoung, Robert S. Schreiber
2011	—	`conf`	Characterizing Data Structures for Volatile Forensics. SADFE Ellick Chan, Shivaram Venkataraman, Nadia Tkach, Kevin Larson, Alejandro Gutierrez, Roy H. Campbell
2011	A	`conf`	Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory. FAST Shivaram Venkataraman, Niraj Tolia, Parthasarathy Ranganathan, Roy H. Campbell
2010	A	`conf`	Forenscope: a framework for live forensics. ACSAC Ellick Chan, Shivaram Venkataraman, Francis M. David, Amey Chaugule, Roy H. Campbell
2010	B	`conf`	Scaling eCGA model building via data-intensive computing. IEEE Congress on Evolutionary Computation Abhishek Verma, Xavier Llorà, Shivaram Venkataraman, David E. Goldberg, Roy H. Campbell