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

15 citations · 15 across the 4 of their papers we have counts for

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5 papers · 1 filter

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.DC202215 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

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…