程大钊

武汉大学计算机学院 计算机科学系 人工智能研究所 教授 (博导)

研究方向

程大钊，武汉大学计算机学院 教授、博导。主要从事存算一体化、分布式计算等方面的研究工作，近期致力于研究面向人工智能负载的新型存内计算架构，具有丰富的计算机系统研究基础和经验。先后入选为湖北省“百人计划”特聘教授，“武汉英才”创新人才。同时，担任武汉大学计算机学院副院长，CCF分布式计算与系统专委会常务委员、国家重点研发计划和基金委重点项目首席科学家。2016-2020年在美国北卡大学计算机系担任Tenure Track助理教授，曾担任国家科技部、广东省和湖北省科技厅重点项目、美国和加拿大国家科学基金等评审专家。在权威计算机系统领域的期刊和会议上发表论文50篇并申请专利10多项，其中第一/通讯作者发表30余篇（含CCF A类20余篇）。先后主持了国家重点研发计划1项、国家自然科学基金委重点项目1项，美国NSF面上项目2项和其它省部级项目若干，共主持过科研项目经费约1700万元。同时应邀在多个国际国内学术组织中任职，担任4个学术期刊客座编委，5个国际会议的主席，28个国际会议的技术委员会委员。

实验室主页：icslab.whu.edu.cn

欢迎对分布式系统和AI System感兴趣的同学加入，有意向者请把简历、成绩单和专业排名发邮件至：dcheng@whu.edu.cn ！

教育背景

(1) 2011-8至2016-5, 美国科罗拉多大学, 计算机科学, 博士

(2) 2006-9至2009-6, 中国科学技术大学, 模式识别与人工智能，硕士

(3) 2002-9至2006-6, 合肥工业大学, 自动化, 学士

工作经验

(1) 2020-9至现在, 武汉大学, 计算机学院, 教授

(2) 2016-7至2020-9, 美国北卡罗来纳大学, 计算机科学系, 助理教授

教授课程

《大数据分析与处理》。

发表论文

§ 期刊论文

[1] T Li; Z Qiu; D Cheng; W Wang; X Shi; Y Wang*: Privacy-Preserving Participant Grouping for Mobile Social Sensing over Edge Clouds. IEEE Transactions on Network Science and Engineering (TNSE), 2021.

[2] D Cheng; Y Wang; D Dai*: Dynamic Resource Provisioning for Iterative Workloads on Apache Spark. IEEE Transactions on Cloud Computing (TCC), 2021.（SCI一区）

[3] W Rang; D Yang; D Cheng*: Dependency-aware Tensor Scheduler for Industrial AI Applications. IEEE Industrial Electronics Magazine (IEM), 2021.（SCI一区）

[4] W Rang; D Yang; D Cheng*: Yu Wang; Data Life Aware Model Updating Strategy for Stream-based Online Deep Learning. IEEE Transactions on Parallel and Distributed Systems (TPDS), 2021.（CCF A类）

[5] D Yang, D Cheng*, W Rang, Y Wang: Joint Optimization of MapReduce Scheduling and Network Policy in Hierarchical Data Centers. IEEE Transactions on Cloud Computing (TCC), 2019.（SCI一区）

[6] D Cheng*, X Zhou, Y Xu, L Liu, C Jiang: Deadline-aware MapReduce job scheduling with dynamic resource availability. IEEE transactions on parallel and distributed systems (TPDS), 2018:30 (4), 814-826.（CCF A类）

[7] D Cheng*, X Zhou, Z Ding, Y Wang, M Ji: Heterogeneity aware workload management in distributed sustainable datacenters. IEEE Transactions on Parallel and Distributed Systems (TPDS),2018:30 (2), 375-387.（CCF A类）

[8] D Cheng*, X Zhou, Y Wang, C Jiang: Adaptive scheduling parallel jobs with dynamic batching in spark streaming. IEEE Transactions on Parallel and Distributed Systems (TPDS), 2018:29 (12), 2672-2685.（CCF A类）

[9] D Cheng*, X Zhou, P Lama, M Ji, C Jiang: Energy efficiency aware task assignment with dvfs in heterogeneous hadoop clusters. IEEE Transactions on Parallel and Distributed Systems (TPDS), 2017:29 (1), 70-82.（CCF A类）

[10] D Cheng, X Zhou*, P Lama, J Wu, C Jiang: Cross-platform resource scheduling for spark and mapreduce on yarn. IEEE Transactions on Computers (TC). 2017:66 (8), 1341-1353.（CCF A类）

[11] D Cheng, J Rao, Y Guo, C Jiang, X Zhou*: Improving performance of heterogeneous mapreduce clusters with adaptive task tuning. IEEE Transactions on Parallel and Distributed Systems (TPDS), 2016:28 (3), 774-786.（CCF A类）

[12] Y Guo, J Rao, D Cheng, X Zhou*: ishuffle: Improving hadoop performance with shuffle-on-write. IEEE transactions on parallel and distributed systems (TPDS), 2016:28 (6), 1649-1662.

[13] D Cheng, J Rao, C Jiang, X Zhou*: Elastic power-aware resource provisioning of heterogeneous workloads in self-sustainable datacenters. IEEE Transactions on Computers (TC). 2015:65 (2), 508-521.（CCF A类）

[14] D Cheng, Y Guo, C Jiang, X Zhou*: Self-tuning batching with dvfs for performance improvement and energy efficiency in internet servers. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 2015:10 (1), 1-32.

§ 会议论文

[1] W Rang, D Yang, D Cheng: A Shared Memory Cache Layer across Multiple Executors in Apache Spark.2020 IEEE International Conference on Big Data (Big Data), 477-482.

[2] D Yang, W Rang, D Cheng: Mitigating Stragglers in the Decentralized Training on Heterogeneous Clusters.Proceedings of the 21st International Middleware Conference (Middleware), 386-399.

[3] W Rang, D Yang, D Cheng*, K Suo, W Chen: Data Life Aware Model Updating Strategy for Stream-based Online Deep Learning.2020 IEEE International Conference on Cluster Computing (CLUSTER), 392-398.

[4] K Suo, Y Shi, X Xu, D Cheng, W Chen:Tackling Cold Start in Serverless Computing with Container Runtime Reusing.Proceedings of the Workshop on Network Application Integration/CoDesign, 54-55.

[5] D Yang, D Cheng: Efficient gpu memory management for nonlinear dnns. Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing (HPDC), 185-196.

[6] J Tian, S Di, C Zhang, X Liang, S Jin, D Cheng, D Tao*, F Cappello: Wavesz: A hardware-algorithm co-design of efficient lossy compression for scientific data. Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP), 74-88.

[7] D Yang, W Rang, D Cheng, Y Wang, J Tian, D Tao:Elastic Executor Provisioning for Iterative Workloads on Apache Spark. 2019 IEEE International Conference on Big Data (Big Data), 413-422.

[8] TBG Perez, X Zhou, D Cheng: Reference-distance eviction and prefetching for cache management in spark. Proceedings of the 47th International Conference on Parallel Processing (ICPP), 1-10  12.

[9] D Yang, W Rang, D Cheng: Joint optimization of mapreduce scheduling and network policy in hierarchical clouds. Proceedings of the 47th International Conference on Parallel Processing (ICPP), 1-10.

[10] P Lama, S Wang, X Zhou, D Cheng: Performance isolation of data-intensive scale-out applications in a multi-tenant cloud. 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 85-94.

[11] D Cheng, Y Chen, X Zhou, D Gmach, D Milojicic: Adaptive scheduling of parallel jobs in spark streaming. IEEE INFOCOM 2017-IEEE Conference on Computer Communicationsc(INFOCOM), 1-9.（CCF A类）

[12] D Cheng, P Lama, C Jiang, X Zhou: Towards energy efficiency in heterogeneous hadoop clusters by adaptive task assignment. 2015 IEEE 35th International Conference on Distributed Computing Systems (ICDCS). 359-368.

[13] D Cheng, J Rao, C Jiang, X Zhou: Resource and deadline-aware job scheduling in dynamic hadoop clusters. 2015 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 956-965.

[14] Y Guo, J Rao, D Cheng, C Jiang, CZ Xu, X Zhou: Storeapp: A shared storage appliance for efficient and scalable virtualized hadoop clusters. 2015 IEEE Conference on Computer Communications (INFOCOM), 594-602.

[15] D Cheng, J Rao, Y Guo, X Zhou: Improving mapreduce performance in heterogeneous environments with adaptive task tuning. Proceedings of the 15th International Middleware Conference (Middleware), 97-108.

[16] D Cheng, C Jiang, X Zhou: Heterogeneity-aware workload placement and migration in distributed sustainable datacenters. 2014 IEEE 28th International Parallel and Distributed Processing Symposium (IPDPS), 307-316.

[17] D Cheng, Y Guo, X Zhou: Self-tuning batching with dvfs for improving performance and energy efficiency in servers. 2013 IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS),40-49.

科研课题

1.美国自然科学基金委员会，面上项目2项；

2.北卡莱罗纳州科研基金委员会，青年基金项目1项。

研究团队

2020年9月至今，在武汉大学计算机学院担任教授期间，已组建了拥有4名博士和5名硕士研究生的科研团队。

知识产权

[1] Y Chen, DS Milojicic, D Cheng: Managing data processing resources. US Patent 10,037,230

学术服务

IEEE Transactions on Cloud Computing (TOC) 审稿人

IEEE Transactions on Parallel and Distributed Systems (TPDS) 审稿人

IEEE Conference on Computer Communications (INFOCOM’14,15,16) 审稿人

USENIX International Conference on Autonomic Computing (ICAC’13,14,15) 审稿人

成果展示

其他