USENIX

Extracting Training Data from Large Language Models

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Power-aware Deep Learning Model Serving with μ-Serve

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

EL3XIR: Fuzzing COTS Secure Monitors

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Fingerprinting Obfuscated Proxy Traffic with Encapsulated TLS Handshakes

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Practical Byzantine Fault Tolerance

This paper describes a new replication algorithm that is able to tolerate Byzantine faults. We believe that Byzantine-fault-tolerant algorithms will be increasingly important in the future because ...
USENIX

Beware of Fragmentation: Scheduling GPU-Sharing Workloads with Fragmentation Gradient Descent

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Hydro: Surrogate-Based Hyperparameter Tuning Service in Datacenters

Hyperparameter tuning is an essential step in deep learning model development that provides better model performance at the cost of substantial resources. While existing systems can improve tuning ...
USENIX

The Most Dangerous Codec in the World: Finding and Exploiting Vulnerabilities in H.264 Decoders

Modern video encoding standards such as H.264 are a marvel of hidden complexity. But with hidden complexity comes hidden security risk. Decoding video in practice means interacting with dedicated ...
USENIX

Gandiva: Introspective Cluster Scheduling for Deep Learning

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Transparent GPU Sharing in Container Clouds for Deep Learning Workloads

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Campo: Cost-Aware Performance Optimization for Mixed-Precision Neural Network Training

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Ansor: Generating High-Performance Tensor Programs for Deep Learning

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...