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Dong Dai I am moving to Univeristy of Delaware as an Associate Professor. Please visit the new site Here. Previously, I was an Assitant Professor at University of North Carolina at Charlotte in Computer Science Department, where I work on Data-intensive and High-performance systems. I work in optimizing and designing intelligent infrastructure for high-performance data-intensive systems, such as parallel file systems, metadata management, graph storage, and resource management. I lead the Data Intelligence Research (DIR) Lab, also serve as the Associate Director of the High Performance Computing Systems (HPCS) Lab at UNC-Charlotte. |
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ResearchI am interested in developing intelligent infrastructure for high-performance and robust data-intensive computing. The complete paper list can be seen in Google Scholar. Below is list of representative pulbications. Note * are Ph.D, Master, or Undergraduate students mentored by me. |
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Cross-System Analysis of Job Characterization and Scheduling in Large-Scale Computing Clusters
Di Zhang*, Monish Soundar Raj*, Bing Xie, Sheng Di, Dong Dai, IPDPS, 2024 Github Repo (TBA.), Web App We conduct a comparative study of multiple workloads in HPC and AI clusters. Based on the analysis, we have eight takeaways that can be used for designing better schedulers. |
 
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DGAP: Efficient Dynamic Graph Analysis on Persistent Memory
Abdullah Al Raqibul Islam*, Dong Dai, SC, 2023 Github Repo We propose a novel graph analysis framework, DGAP, to efficiently support dynamic graph analysis on Optane persistent memory. |
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A Reinforcement Learning Based Backfilling Strategy for HPC Batch Jobs
the original paper has been updated for correcting some minor issues. Elliot Kolker-Hicks*, Di Zhang*, Dong Dai, PMBS@SC, 2023 Github Repo (code has been updated) We show better job runtime prediction does not always lead to better backfilling, and propose to use reinforcement learning to learn an optimized backfilling strategy. |
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Early Exploration of Using ChatGPT for Log-based Anomaly Detection on Parallel File Systems Logs
Chris Egersdoerfer*, Di Zhang*, Dong Dai, HPDC (poster), 2023 We explore to use ChatGPT for log-based anomaly detection on parallel file systems logs. It shows promising accuracy and understanding about the logs. |
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Drill: Log-based Anomaly Detection for Large-scale Storage Systems Using Source Code Analysis.
Di Zhang*, Chris Egersdoerfer*, Tabassum Mahmud, Mai Zheng, Dong Dai, IPDPS, 2023 Github Repo / talk Drill is a state-of-the-art log-based anomaly detection system for large-scale storage systems using both content and context of the logs. |
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FaultyRank: A Graph-based Parallel File System Checker
Saisha Kamat*, Abdullah Al Raqibul Islam*, Mai Zheng, Dong Dai, IPDPS, 2023 Github Repo / talk FaultyRank is the first graph-based parallel file system checker that can detect and fix metadata inconsistencies and corruptions. It runs faster and more accurate than the state-of-the-art checker. |
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VCSR: Mutable CSR Graph Format Using Vertex-Centric Packed Memory Array
Abdullah Al Raqibul Islam*, Dazhao Cheng, Dong Dai, CCGRID, 2022 Github Repo / talk VCSR is a new mutable CSR graph format using packed memory array. Its new vertex-centric design enables fast graph updates and efficient graph traversal. |
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SchedInspector: A Batch Job Scheduling Inspector Using Reinforcement Learning
Di Zhang*, Dong Dai, Bing Xie, HPDC, 2022 Github Repo / talk SchedInspector opportunistically delays ready jobs to improve the overall performance of the existing job scheduling policies via reinforcement learning. |
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A Study of Failure Recovery and Logging of High-Performance Parallel File Systems
Runzhou Han, Om Rameshwar Gatla, Mai Zheng, Jinrui Cao, Di Zhang*, Dong Dai, Yong Chen, Jonathan Cook, TOS, 2022 A comprehensive review of PFault, our ICS'18 paper. |
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SentiLog: Anomaly Detecting on Parallel File Systems via Log-based Sentiment Analysis
Di Zhang*, Dong Dai, Runzhou Han, Mai Zheng, HotStorage, 2021, Best Paper Nominee Github Repo / talk SentiLog proposes to use sentiment analysis to detect anomalies on parallel file systems. |
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RLScheduler: An AutomatedHPC Batch Job Scheduler Using Reinforcement Learning
Di Zhang*, Dong Dai, Youbiao He, Forrest Sheng Bao, Bing Xie, SC, 2020 Github Repo / talk RLScheduler uses reinforcement learning (PPO) to automaticlaly learn a batch job scheduler. It achieves the best flexibility, performance, and adaptability among all the schedulers. |
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A Performance Study of Optane Persistent Memory: From Indexing Data Structures’ Perspective
Abdullah Al Raqibul Islam*, Anirudh Narayanan*, Christopher York*, Dong Dai, MSST, 2020 Github Repo / talk In this paper, we systematically evaluated the performance of indexing data structures on Intel Optane persistent memory and obtained interesting observations. |
... (Full list at Google Scholar)
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... (Full list in my CV)
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Based on source code. |