43 citations · 218 across the 16 of their papers we have counts for
8 papers · 1 filter
Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness
Zeyuan Tan, Xiulong Yuan, Congjie He +7
Systems for serving inference requests on graph neural networks (GNN) must combine low latency with high throughout, but they face irregular computation due to skew in the number o…
Faabric: Fine-Grained Distribution of Scientific Workloads in the Cloud
Simon Shillaker, Carlos Segarra, Eleftheria Mappoura +3
With their high parallelism and resource needs, many scientific applications benefit from cloud deployments. Today, scientific applications are executed on dedicated pools of VMs,…
Dropbear: Machine Learning Marketplaces made Trustworthy with Byzantine Model Agreement
Alex Shamis, Peter Pietzuch, Antoine Delignat-Lavaud +2
Marketplaces for machine learning (ML) models are emerging as a way for organizations to monetize models. They allow model owners to retain control over hosted models by using clou…
Pronto: Federated Task Scheduling
Andreas Grammenos, Evangelia Kalyvianaki, Peter Pietzuch
We present a federated, asynchronous, memory-limited algorithm for online task scheduling across large-scale networks of hundreds of workers. This is achieved through recent advanc…
Faasm: Lightweight Isolation for Efficient Stateful Serverless Computing
Simon Shillaker, Peter Pietzuch
Serverless computing is an excellent fit for big data processing because it can scale quickly and cheaply to thousands of parallel functions. Existing serverless platforms isolate…
ServerMix: Tradeoffs and Challenges of Serverless Data Analytics
Pedro García-López, Marc Sánchez-Artigas, Simon Shillaker +6
Serverless computing has become very popular today since it largely simplifies cloud programming. Developers do not need to longer worry about provisioning or operating servers, an…
CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers
Alexandros Koliousis, Pijika Watcharapichat, Matthias Weidlich +3
Deep learning models are trained on servers with many GPUs, and training must scale with the number of GPUs. Systems such as TensorFlow and Caffe2 train models with parallel synchr…
SecureCloud: Secure Big Data Processing in Untrusted Clouds
Florian Kelbert, Franz Gregor, Rafael Pires +7
We present the SecureCloud EU Horizon 2020 project, whose goal is to enable new big data applications that use sensitive data in the cloud without compromising data security and pr…