961 citations · 1.4k across the 33 of their papers we have counts for
6 papers · 1 filter
Archipelago: A Scalable Low-Latency Serverless Platform
Arjun Singhvi, Kevin Houck, Arjun Balasubramanian +3
The increased use of micro-services to build web applications has spurred the rapid growth of Function-as-a-Service (FaaS) or serverless computing platforms. While FaaS simplifies…
Blink: Fast and Generic Collectives for Distributed ML
Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee +3
Model parameter synchronization across GPUs introduces high overheads for data-parallel training at scale. Existing parameter synchronization protocols cannot effectively leverage…
Themis: Fair and Efficient GPU Cluster Scheduling
Kshiteej Mahajan, Arjun Balasubramanian, Arjun Singhvi +4
Modern distributed machine learning (ML) training workloads benefit significantly from leveraging GPUs. However, significant contention ensues when multiple such workloads are run…
Parity Models: A General Framework for Coding-Based Resilience in ML Inference
Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman
Machine learning models are becoming the primary workhorses for many applications. Production services deploy models through prediction serving systems that take in queries and ret…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…
Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads
Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee +3
With widespread advances in machine learning, a number of large enterprises are beginning to incorporate machine learning models across a number of products. These models are typic…