4 citations · 11 across the 11 of their papers we have counts for
12 papers
A Few GPUs, A Whole Lotta Scale: Faithful LLM Training Emulation with PrismLLM
Shaoke Xi, ChonLam Lao, Boyi Jia +11
Large language model (LLM) training today runs on clusters spanning thousands of GPUs. While this scale enables rapid model advances, developing, debugging, and performance-tuning…
Pythia: Exploiting Workflow Predictability for Efficient Agent-Native LLM Serving
Shan Yu, Junyi Shu, Yuanjiang Ni +14
As LLM applications grow more complex, developers are increasingly adopting multi-agent architectures to decompose workflows into specialized, collaborative components, introducing…
Model-Based Diagnosis: Automating End-to-End Diagnosis of Network Failures
Changrong Wu, Yiyao Yu, Myungjin Lee +4
Fast diagnosis and repair of enterprise network failures is critically important since disruptions cause major business impacts. Prior works focused on diagnosis primitives or proc…
EROICA: Online Performance Troubleshooting for Large-scale Model Training
Yu Guan, Zhiyu Yin, Haoyu Chen +11
Troubleshooting performance problems of large model training (LMT) is immensely challenging, due to unprecedented scales of modern GPU clusters, the complexity of software-hardware…
ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production
Yuxing Xiang, Xue Li, Kun Qian +3
With the widespread adoption of Large Language Models (LLMs), serving LLM inference requests has become an increasingly important task, attracting active research advancements. Pra…
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…
Statically Verifying Continuous Integration Configurations
Mark Santolucito, Jialu Zhang, Ennan Zhai +1
Continuous Integration (CI) testing is a popular software development technique that allows developers to easily check that their code can build successfully and pass tests across…
PriFi: Low-Latency Anonymity for Organizational Networks
Ludovic Barman, Italo Dacosta, Mahdi Zamani +5
Organizational networks are vulnerable to traffic-analysis attacks that enable adversaries to infer sensitive information from the network traffic - even if encryption is used. Typ…