activity
20182026
most citedHPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization

15 citations · 16 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DC2026

StreamGuard: Low-Overhead Resilience for Real-time HPC Data Streams

Hai Duc Nguyen, Bogdan Nicolae, Tekin Bicer +4

Real-time scientific workflows operate on continuous data streams and must produce timely, high-quality results despite executing on complex, failure-prone infrastructure. Hardware…

cs.DC2026

When More Cores Hurts: The Vector Database Scaling Paradox in HPC

Seth Ockerman, Song Young Oh, Amal Gueroudji +12

Vector databases have been designed and optimized for cloud environments; however, emerging scientific AI workloads (e.g., molecular search, meteorological trajectory detection, an…

cs.DC2024

Thallus: An RDMA-based Columnar Data Transport Protocol

Jayjeet Chakraborty, Matthieu Dorier, Philip Carns +3

The volume of data generated and stored in contemporary global data centers is experiencing exponential growth. This rapid data growth necessitates efficient processing and analysi…

cs.DC2022★ 15 cited

HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization

Matthieu Dorier, Romain Egele, Prasanna Balaprakash +5

Distributed data storage services tailored to specific applications have grown popular in the high-performance computing (HPC) community as a way to address I/O and storage challen…

cs.DC2018★ 1 cited

A Cross-Layer Solution in Scientific Workflow System for Tackling Data Movement Challenge

Dong Dai, Robert Ross, Dounia Khaldi +4

Scientific applications in HPC environment are more com-plex and more data-intensive nowadays. Scientists usually rely on workflow system to manage the complexity: simply define mu…