4 citations · 11 across the 11 of their papers we have counts for
5 papers · 1 filter
TrainMover: An Interruption-Resilient Runtime for ML Training
ChonLam Lao, Jiaqi Gao, Jiamin Cao +13
Large-scale ML training jobs are frequently interrupted by hardware and software anomalies, failures, and management events. Existing solutions like checkpoint-restart or runtime r…
Accelerating Stateful Network Applications with Performance Prediction on SoC SmartNICs
Shaoke Xi, Jiaqi Gao, Mengqi Liu +7
Offloading stateful network functions to multi-threaded SoC SmartNICs promises significant performance and cost benefits. However, realizing this potential is hindered by two funda…
Enhancing Large-Scale AI Training Efficiency: The C4 Solution for Real-Time Anomaly Detection and Communication Optimization
Jianbo Dong, Bin Luo, Jun Zhang +22
The emergence of Large Language Models (LLMs) has necessitated the adoption of distributed training techniques, involving the deployment of thousands of GPUs to train a single mode…
Relational Network Verification
Xieyang Xu, Yifei Yuan, Zachary Kincaid +4
Relational network verification is a new approach to validating network changes. In contrast to traditional network verification, which analyzes specifications for a single network…
CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generation
Yifei Xu, Yuning Chen, Xumiao Zhang +8
Among the thriving ecosystem of cloud computing and the proliferation of Large Language Model (LLM)-based code generation tools, there is a lack of benchmarking for code generation…