Forenscope: a framework for live forensics. - Ellick Chan; Shivaram Venkataraman; Francis M. David; Amey Chaugule; Roy H. Campbell (ACSAC, 2010)

Scaling eCGA model building via data-intensive computing. - Abhishek Verma; Xavier Llorà; Shivaram Venkataraman; David E. Goldberg; Roy H. Campbell (IEEE Congress on Evolutionary Computation, 2010)

Consistent and Durable Data Structures for Non-Volatile Byte-Addressable Memory. - Shivaram Venkataraman; Niraj Tolia; Parthasarathy Ranganathan; Roy H. Campbell (FAST, 2011)

Characterizing Data Structures for Volatile Forensics. - Ellick Chan; Shivaram Venkataraman; Nadia Tkach; Kevin Larson; Alejandro Gutierrez; Roy H. Campbell (SADFE, 2011)

Cake: enabling high-level SLOs on shared storage systems. - Andrew Wang 002; Shivaram Venkataraman; Sara Alspaugh; Randy H. Katz; Ion Stoica (SoCC, 2012)

Using R for Iterative and Incremental Processing. - Shivaram Venkataraman; Indrajit Roy 001; Alvin AuYoung; Robert S. Schreiber (HotCloud, 2012)

Sweet Storage SLOs with Frosting. - Andrew Wang 002; Shivaram Venkataraman; Sara Alspaugh; Ion Stoica; Randy H. Katz (HotCloud, 2012)

Probabilistically Bounded Staleness for Practical Partial Quorums. - Peter Bailis; Shivaram Venkataraman; Michael J. Franklin; Joseph M. Hellerstein; Ion Stoica (Proc. VLDB Endow., 2012)

Presto: distributed machine learning and graph processing with sparse matrices. - Shivaram Venkataraman; Erik Bodzsar; Indrajit Roy 001; Alvin AuYoung; Robert S. Schreiber (EuroSys, 2013)

The Case for Tiny Tasks in Compute Clusters. - Kay Ousterhout; Aurojit Panda; Josh Rosen; Shivaram Venkataraman; Reynold Xin; Sylvia Ratnasamy; Scott Shenker; Ion Stoica (HotOS, 2013)

PBS at work: advancing data management with consistency metrics. - Peter Bailis; Shivaram Venkataraman; Michael J. Franklin; Joseph M. Hellerstein; Ion Stoica (SIGMOD Conference, 2013)

The Power of Choice in Data-Aware Cluster Scheduling. - Shivaram Venkataraman; Aurojit Panda; Ganesh Ananthanarayanan; Michael J. Franklin; Ion Stoica (OSDI, 2014)

Record Placement Based on Data Skew Using Solid State Drives. - Jun Suzuki 004; Shivaram Venkataraman; Sameer Agarwal 002; Michael J. Franklin; Ion Stoica (BPOE@ASPLOS/VLDB, 2014)

Quantifying eventual consistency with PBS. - Peter Bailis; Shivaram Venkataraman; Michael J. Franklin; Joseph M. Hellerstein; Ion Stoica (Commun. ACM, 2014)

linalg: Matrix Computations in Apache Spark. - Reza Bosagh Zadeh; Xiangrui Meng; Burak Yavuz; Aaron Staple; Li Pu; Shivaram Venkataraman; Evan Randall Sparks; Alexander Ulanov; Matei Zaharia (CoRR, 2015)

Matrix Computations and Optimization in Apache Spark. - Reza Bosagh Zadeh; Xiangrui Meng; Alexander Ulanov; Burak Yavuz; Li Pu; Shivaram Venkataraman; Evan Randall Sparks; Aaron Staple; Matei Zaharia (KDD, 2016)

Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics. - Shivaram Venkataraman; Zongheng Yang; Michael J. Franklin; Benjamin Recht; Ion Stoica (NSDI, 2016)

SparkR: Scaling R Programs with Spark. - Shivaram Venkataraman; Zongheng Yang; Davies Liu; Eric Liang; Hossein Falaki; Xiangrui Meng; Reynold Xin; Ali Ghodsi 002; Michael J. Franklin; Ion Stoica; Matei Zaharia (SIGMOD Conference, 2016)

Apache Spark: a unified engine for big data processing. - Matei Zaharia; Reynold S. Xin; Patrick Wendell; Tathagata Das; Michael Armbrust; Ankur Dave; Xiangrui Meng; Josh Rosen; Shivaram Venkataraman; Michael J. Franklin; Ali Ghodsi 002; Joseph Gonzalez 001; et al. (Commun. ACM, 2016)

Large Scale Kernel Learning using Block Coordinate Descent. - Stephen Tu; Rebecca Roelofs; Shivaram Venkataraman; Benjamin Recht (CoRR, 2016)

MLlib: Machine Learning in Apache Spark. - 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; et al. (J. Mach. Learn. Res., 2016)

Occupy the cloud: distributed computing for the 99%. - Eric Jonas; Qifan Pu; Shivaram Venkataraman; Ion Stoica; Benjamin Recht (SoCC, 2017)

KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics. - Evan Randall Sparks; Shivaram Venkataraman; Tomer Kaftan; Michael J. Franklin; Benjamin Recht (ICDE, 2017)

Breaking Locality Accelerates Block Gauss-Seidel. - Stephen Tu; Shivaram Venkataraman; Ashia C. Wilson; Alex Gittens; Michael I. Jordan; Benjamin Recht (ICML, 2017)

CherryPick: Adaptively Unearthing the Best Cloud Configurations for Big Data Analytics. - Omid Alipourfard; Hongqiang Harry Liu; Jianshu Chen; Shivaram Venkataraman; Minlan Yu; Ming Zhang 005 (NSDI, 2017)

Drizzle: Fast and Adaptable Stream Processing at Scale. - Shivaram Venkataraman; Aurojit Panda; Kay Ousterhout; Michael Armbrust; Ali Ghodsi 002; Michael J. Franklin; Benjamin Recht; Ion Stoica (SOSP, 2017)

Occupy the Cloud: Distributed Computing for the 99%. - Eric Jonas; Shivaram Venkataraman; Ion Stoica; Benjamin Recht (CoRR, 2017)

Hemingway: Modeling Distributed Optimization Algorithms. - Xinghao Pan; Shivaram Venkataraman; Zizheng Tai; Joseph Gonzalez 001 (CoRR, 2017)

System Design for Large Scale Machine Learning. - Shivaram Venkataraman (2017)

Bridging the GAP: towards approximate graph analytics. - Anand Padmanabha Iyer; Aurojit Panda; Shivaram Venkataraman; Mosharaf Chowdhury; Aditya Akella; Scott Shenker; Ion Stoica (GRADES/NDA@SIGMOD/PODS, 2018)

Towards Fast and Scalable Graph Pattern Mining. - Anand Padmanabha Iyer; Zaoxing Liu; Xin Jin 008; Shivaram Venkataraman; Vladimir Braverman; Ion Stoica (HotCloud, 2018)

Focus: Querying Large Video Datasets with Low Latency and Low Cost. - Kevin Hsieh; Ganesh Ananthanarayanan; Peter Bodík; Shivaram Venkataraman; Paramvir Bahl; Matthai Philipose; Phillip B. Gibbons; Onur Mutlu (OSDI, 2018)

ASAP: Fast, Approximate Graph Pattern Mining at Scale. - Anand Padmanabha Iyer; Zaoxing Liu; Xin Jin 008; Shivaram Venkataraman; Vladimir Braverman; Ion Stoica (OSDI, 2018)

Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation. - Jack Kosaian; K. V. Rashmi; Shivaram Venkataraman (CoRR, 2018)

numpywren: serverless linear algebra. - Vaishaal Shankar; Karl Krauth; Qifan Pu; Eric Jonas; Shivaram Venkataraman; Ion Stoica; Benjamin Recht; Jonathan Ragan-Kelley (CoRR, 2018)

Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems. - Luo Mai; Kai Zeng 002; Rahul Potharaju; Le Xu; Steve Suh; Shivaram Venkataraman; Paolo Costa; Terry Kim; Saravanam Muthukrishnan; Vamsi Kuppa; Sudheer Dhulipalla; Sriram Rao (Proc. VLDB Endow., 2018)

Serverless Event-Stream Processing over Virtual Actors. - Philip A. Bernstein; Todd Porter; Rahul Potharaju; Alejandro Z. Tomsic; Shivaram Venkataraman; Wentao Wu 001 (CIDR, 2019)

The Case for Unifying Data Loading in Machine Learning Clusters. - Aarati Kakaraparthy; Abhay Venkatesh; Amar Phanishayee; Shivaram Venkataraman (HotCloud, 2019)

Accelerating Deep Learning Inference via Freezing. - Adarsh Kumar 001; Arjun Balasubramanian; Shivaram Venkataraman; Aditya Akella (HotCloud, 2019)

Cracking open the DNN black-box: Video Analytics with DNNs across the Camera-Cloud Boundary. - John Emmons; Sadjad Fouladi; Ganesh Ananthanarayanan; Shivaram Venkataraman; Silvio Savarese; Keith Winstein (HotEdgeVideo@MobiCom, 2019)

Shuffling, Fast and Slow: Scalable Analytics on Serverless Infrastructure. - Qifan Pu; Shivaram Venkataraman; Ion Stoica (NSDI, 2019)

Parity models: erasure-coded resilience for prediction serving systems. - Jack Kosaian; K. V. Rashmi; Shivaram Venkataraman (SOSP, 2019)

Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads. - Myeongjae Jeon; Shivaram Venkataraman; Amar Phanishayee; Junjie Qian; Wencong Xiao; Fan Yang 024 (USENIX ATC, 2019)

SysML: The New Frontier of Machine Learning Systems. - 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; et al. (CoRR, 2019)

Parity Models: A General Framework for Coding-Based Resilience in ML Inference. - Jack Kosaian; K. V. Rashmi; Shivaram Venkataraman (CoRR, 2019)

Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads. - Kshiteej Mahajan; Arjun Singhvi; Arjun Balasubramanian; Varun Batra; Surya Teja Chavali; Shivaram Venkataraman; Aditya Akella; Amar Phanishayee; Shuchi Chawla 001 (CoRR, 2019)

Archipelago: A Scalable Low-Latency Serverless Platform. - Arjun Singhvi; Kevin Houck; Arjun Balasubramanian; Mohammed Danish Shaikh; Shivaram Venkataraman; Aditya Akella (CoRR, 2019)

Serverless linear algebra. - Vaishaal Shankar; Karl Krauth; Kailas Vodrahalli; Qifan Pu; Benjamin Recht; Ion Stoica; Jonathan Ragan-Kelley; Eric Jonas; Shivaram Venkataraman (SoCC, 2020)

Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs. - Konstantinos Kanellis; Ramnatthan Alagappan; Shivaram Venkataraman (HotStorage, 2020)

Blink: Fast and Generic Collectives for Distributed ML. - Guanhua Wang; Shivaram Venkataraman; Amar Phanishayee; Jorgen Thelin; Nikhil R. Devanur; Ion Stoica (MLSys, 2020)

Themis: Fair and Efficient GPU Cluster Scheduling. - Kshiteej Mahajan; Arjun Balasubramanian; Arjun Singhvi; Shivaram Venkataraman; Aditya Akella; Amar Phanishayee; Shuchi Chawla 001 (NSDI, 2020)

Accordion: Adaptive Gradient Communication via Critical Learning Regime Identification. - Saurabh Agarwal; Hongyi Wang 001; Kangwook Lee 001; Shivaram Venkataraman; Dimitris S. Papailiopoulos (CoRR, 2020)

Learning-Based Coded Computation. - Jack Kosaian; K. V. Rashmi; Shivaram Venkataraman (IEEE J. Sel. Areas Inf. Theory, 2020)

Atoll: A Scalable Low-Latency Serverless Platform. - Arjun Singhvi; Arjun Balasubramanian; Kevin Houck; Mohammed Danish Shaikh; Shivaram Venkataraman; Aditya Akella (SoCC, 2021)

Doing more by doing less: how structured partial backpropagation improves deep learning clusters. - Adarsh Kumar 001; Kausik Subramanian; Shivaram Venkataraman; Aditya Akella (DistributedML@CoNEXT, 2021)

Adaptive Gradient Communication via Critical Learning Regime Identification. - Saurabh Agarwal; Hongyi Wang 001; Kangwook Lee 001; Shivaram Venkataraman; Dimitris S. Papailiopoulos (MLSys, 2021)

Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo. - Le Xu; Shivaram Venkataraman; Indranil Gupta; Luo Mai; Rahul Potharaju (NSDI, 2021)

Marius: Learning Massive Graph Embeddings on a Single Machine. - Jason Mohoney; Roger Waleffe; Henry Xu; Theodoros Rekatsinas; Shivaram Venkataraman (OSDI, 2021)

KAISA: an adaptive second-order optimizer framework for deep neural networks. - J. Gregory Pauloski; Qi Huang; Lei Huang 019; Shivaram Venkataraman; Kyle Chard; Ian T. Foster; Zhao Zhang 007 (SC, 2021)

Accelerating Deep Learning Inference via Learned Caches. - Arjun Balasubramanian; Adarsh Kumar 001; Yuhan Liu; Han Cao; Shivaram Venkataraman; Aditya Akella (CoRR, 2021)

Learning Massive Graph Embeddings on a Single Machine. - Jason Mohoney; Roger Waleffe; Yiheng Xu; Theodoros Rekatsinas; Shivaram Venkataraman (CoRR, 2021)

AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning. - Yuhan Liu; Saurabh Agarwal; Shivaram Venkataraman (CoRR, 2021)

Demonstration of Marius: Graph Embeddings with a Single Machine. - Anders Carlsson; Anze Xie; Jason Mohoney; Roger Waleffe; Shanan Peters; Theodoros Rekatsinas; Shivaram Venkataraman (Proc. VLDB Endow., 2021)

The Roaming Edge and its Applications. - Suman Banerjee 001; Remzi H. Arpaci-Dusseau; Shenghong Dai; Kassem Fawaz; Mohit Gupta 001; Kangwook Lee 001; Shivaram Venkataraman (GetMobile Mob. Comput. Commun., 2021)

On the Utility of Gradient Compression in Distributed Training Systems. - Saurabh Agarwal; Hongyi Wang 001; Shivaram Venkataraman; Dimitris S. Papailiopoulos (MLSys, 2022)

Not All GPUs Are Created Equal: Characterizing Variability in Large-Scale, Accelerator-Rich Systems. - Prasoon Sinha; Akhil Guliani; Rutwik Jain; Brandon Tran; Matthew D. Sinclair; Shivaram Venkataraman (SC, 2022)

Marius++: Large-Scale Training of Graph Neural Networks on a Single Machine. - Roger Waleffe; Jason Mohoney; Theodoros Rekatsinas; Shivaram Venkataraman (CoRR, 2022)

BagPipe: Accelerating Deep Recommendation Model Training. - Saurabh Agarwal; Ziyi Zhang; Shivaram Venkataraman (CoRR, 2022)

LlamaTune: Sample-Efficient DBMS Configuration Tuning. - Konstantinos Kanellis; Cong Ding 002; Brian Kroth; Andreas Müller 024; Carlo Curino; Shivaram Venkataraman (Proc. VLDB Endow., 2022)

MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks. - Roger Waleffe; Jason Mohoney; Theodoros Rekatsinas; Shivaram Venkataraman (EuroSys, 2023)

Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning. - Pengfei Zheng; Rui Pan 003; Tarannum Khan; Shivaram Venkataraman; Aditya Akella (NSDI, 2023)

Mirage: Towards Low-interruption Services on Batch GPU Clusters with Reinforcement Learning. - Qiyang Ding; Pengfei Zheng; Shreyas Kudari; Shivaram Venkataraman; Zhao Zhang 007 (SC, 2023)

Bagpipe: Accelerating Deep Recommendation Model Training. - Saurabh Agarwal; Chengpo Yan; Ziyi Zhang; Shivaram Venkataraman (SOSP, 2023)

F2: Designing a Key-Value Store for Large Skewed Workloads. - Konstantinos Kanellis; Badrish Chandramouli; Shivaram Venkataraman (CoRR, 2023)

PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices. - Minghao Yan; Hongyi Wang 001; Shivaram Venkataraman (CoRR, 2023)

Nautilus: A Benchmarking Platform for DBMS Knob Tuning. - Konstantinos Kanellis; Johannes Freischuetz; Shivaram Venkataraman (DEEM@SIGMOD, 2024)

Blox: A Modular Toolkit for Deep Learning Schedulers. - Saurabh Agarwal; Amar Phanishayee; Shivaram Venkataraman (EuroSys, 2024)

CHAI: Clustered Head Attention for Efficient LLM Inference. - Saurabh Agarwal; Bilge Acun; Basil Hosmer; Mostafa Elhoushi; Yejin Lee 010; Shivaram Venkataraman; Dimitris Papailiopoulos; Carole-Jean Wu (ICML, 2024)

Does Compressing Activations Help Model Parallel Training? - Song Bian 002; Dacheng Li; Hongyi Wang 001; Eric P. Xing; Shivaram Venkataraman (MLSys, 2024)

PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters. - Rutwik Jain; Brandon Tran; Keting Chen; Matthew D. Sinclair; Shivaram Venkataraman (SC, 2024)

GraphSnapShot: Graph Machine Learning Acceleration with Fast Storage and Retrieval. - Dong Liu; Roger Waleffe; Meng Jiang; Shivaram Venkataraman (CoRR, 2024)

Incremental IVF Index Maintenance for Streaming Vector Search. - Jason Mohoney; Anil Pacaci; Shihabur Rahman Chowdhury; Umar Farooq Minhas; Jeffrey Pound; Cédric Renggli; Nima Reyhani; Ihab F. Ilyas; Theodoros Rekatsinas; Shivaram Venkataraman (CoRR, 2024)

SYMPHONY: Improving Memory Management for LLM Inference Workloads. - Saurabh Agarwal; Anyong Mao; Aditya Akella; Shivaram Venkataraman (CoRR, 2024)

Eva: Cost-Efficient Cloud-Based Cluster Scheduling. - Tzu-Tao Chang; Shivaram Venkataraman (EuroSys, 2025)

TUNA: Tuning Unstable and Noisy Cloud Applications. - Johannes Freischuetz; Konstantinos Kanellis; Brian Kroth; Shivaram Venkataraman (EuroSys, 2025)

Decoding Speculative Decoding. - Minghao Yan; Saurabh Agarwal; Shivaram Venkataraman (NAACL (Long Papers), 2025)

Quake: Adaptive Indexing for Vector Search. - Jason Mohoney; Devesh Sarda; Mengze Tang; Shihabur Rahman Chowdhury; Anil Pacaci; Ihab F. Ilyas; Theodoros Rekatsinas; Shivaram Venkataraman (OSDI, 2025)

Scaling Inference-Efficient Language Models. - Song Bian 002; Minghao Yan; Shivaram Venkataraman (CoRR, 2025)

LV-XAttn: Distributed Cross-Attention for Long Visual Inputs in Multimodal Large Language Models. - Tzu-Tao Chang; Shivaram Venkataraman (CoRR, 2025)

Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networks. - Roger Waleffe; Devesh Sarda; Jason Mohoney; Emmanouil-Vasileios Vlatakis-Gkaragkounis; Theodoros Rekatsinas; Shivaram Venkataraman (CoRR, 2025)

The Cambridge Report on Database Research. - Anastasia Ailamaki; Samuel Madden 001; Daniel Abadi; Gustavo Alonso; Sihem Amer-Yahia; Magdalena Balazinska; Philip A. Bernstein; Peter A. Boncz; Michael J. Cafarella; Surajit Chaudhuri; Susan B. Davidson; David J. DeWitt; et al. (CoRR, 2025)

From Good to Great: Improving Memory Tiering Performance Through Parameter Tuning. - Konstantinos Kanellis; Sujay Yadalam; Fanchao Chen; Michael Swift; Shivaram Venkataraman (CoRR, 2025)

PGT-I: Scaling Spatiotemporal GNNs with Memory-Efficient Distributed Training. - Seth Ockerman; Amal Gueroudji; Tanwi Mallick; Yixuan He; Line Pouchard; Robert B. Ross; Shivaram Venkataraman (CoRR, 2025)

PLoRA: Efficient LoRA Hyperparameter Tuning for Large Models. - Minghao Yan; Zhuang Wang; Zhen Jia; Shivaram Venkataraman; Yida Wang (CoRR, 2025)

ARMS: Adaptive and Robust Memory Tiering System. - Sujay Yadalam; Konstantinos Kanellis; Michael Swift; Shivaram Venkataraman (CoRR, 2025)

Tesserae: Scalable Placement Policies for Deep Learning Workloads. - Song Bian 002; Saurabh Agarwal; Md. Tareq Mahmood; Shivaram Venkataraman (CoRR, 2025)