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Underlined names are students advised by me. Italicized names are UCR students.
MAPA: Multi-Accelerator Pattern Allocation Policy for Multi-Tenant GPU Servers
Kiran Ranganath, Joshua D. Suetterlein, Joseph Manzano, Shuaiwen Leon Song, Daniel Wong
In Proceedings of the 2021 International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 2021 (Acceptance Rate: 26.8%).
BlockMaestro: Enabling Programmer-Transparent Task-based Execution in GPU Systems
Amirali Abdolrashidi, Hodjat Asghari Esfeden, Ali Jahanshahi, Kaustubh Singh, Nael Abu-Ghazaleh, Daniel Wong
In Proceedings of the 48th ACM/IEEE International Symposium on Computer Architecture (ISCA), 2021 (Acceptance Rate: 18.7%).
IEEE Computer Architecture Letters (CAL'20)
High-Performance Parallel Radix Sort on FPGA
Bashar Romanous, Mohammadreza Rezvani, Junjie Huang, Daniel Wong, Evangelos E. Papalexakis, Vassilis J. Tsotras, and Walid Najjar
In Proceedings of the 28th IEEE International Symposium On Field-Programmable Custom Computing Machines (FCCM), 2020 (Poster)
CORF: Coalescing Operand Register File for GPUs
Hodjat Asghari Esfeden, Farzad Khorasani, Hyeran Jeon, Daniel Wong, Nael Abu-Ghazaleh
In Proceedings of the 24th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2019 (Acceptance Rate: 21.1%)
IEEE Computer Architecture Letters (CAL'19)
IEEE Computer Architecture Letters (CAL'19)
Long-Term Reliability Management For Multitasking GPGPUs
Zeyu Sun, Taeyoung Kim, Marcus Chow, Shaoyi Peng, Han Zhou, Hyoseung Kim, Daniel Wong, Sheldon Tan
In Proceedings of the 2019 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD), 2019
Frequency Regulation Service Provision in Data Center
Wei Wang, Amirali Abdolrashidi, Nanpeng Yu, Daniel Wong
Applied Energy, Volume 251, October 2019 (IF: 8.4)
WIREFRAME: Supporting Data-dependent Parallelism through Dependency Graph Execution in GPUs
AmirAli Abdolrashidi, Devashree Tripathy, Mehmet Esat Belviranli, Laxmi N. Bhuyan, Daniel Wong
In Proceedings of the 50th IEEE/ACM International Symposium on Microarchitecture (MICRO), 2017 (Acceptance Rate: 18.6%)
Scaling the Energy Proportionality Wall with KnightShift
Daniel Wong, Murali Annavaram
In IEEE Micro’s “Top Picks from the Computer Architecture Conferences of 2012″ Issue, May/June 2013
KnightShift: Scaling the Energy Proportionality Wall through Server-level Heterogeneity
Daniel Wong, Murali Annavaram.
In Proceedings of the 45th IEEE/ACM International Symposium on Microarchitecture (MICRO), 2012 (Acceptance Rate: 17.5%)
Selected as 1 of 11 IEEE Micro Top Pick in Computer Architecture 2013
Adaptive and Speculative Slack Simulations of CMPs on CMPs
Jianwei Chen, Lakshmi Kumar Dabbiru, Daniel Wong, Murali Annavaram, Michel Dubois.
In Proceedings of the 2010 International Symposium on Microarchitecture (MICRO), 2010 (Acceptance Rate: 17.4%)
Joseph Bungo, Daniel Wong, Bringing GPU Accelerated Computing and Deep Learning to the Classroom, Journal of Computational Science Education (JOCSE), Volume 12, Issue 2. Presented in Seventh SC Workshop on Best Practices for HPC Training and Education (BPHTE), 2020.
Daniel Wong, S. Lloyd, M. Gokhale, A Memory-mapped Approach to Checkpointing. Technical Report LLNL-TR-635611, Lawrence Livermore National Laboratory (LLNL), Livermore, CA, 2013.
I. Karlin, A. Bhatele, B. Chamberlain, J. Cohen, Z. Devito, M. Gokhale, R. Haque, R. Hornung, J. Keasler, D. Laney, E. Luke, S. Lloyd, J. McGraw, R. Neely, D. Richards, M. Schulz, C.H. Still, F. Wang, Daniel Wong, LULESH Programming Model and Performance Ports Overview. Technical Report LLNL-TR-608824, Lawrence Livermore National Laboratory (LLNL), Livermore, CA, 2012.
Daniel Wong, Murali Annavaram, Scalable System-level Active Low Power Mode with Bounded Latency. Technical Report CENG-2012-5, Department of Electrical Engineering, University of Southern California, Los Angeles (California), 2012.
Daniel Wong, Murali Annavaram, Enhancing Server Energy Efficiency by Shifting Light Burden to an Assistant. 2nd Annual Ming Hsiegh Department of Electrical Engineering Research Festival, 2012. Honorable Mention Poster Award Also presented at Sixth USC-Tsinghua Symposium on Green Technology and Energy Informatics
Daniel Wong, R. Zink and S. Koenig, Teaching Artificial Intelligence and Robotics via Games [Poster Abstract], Proceedings of the AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI), 2010
Daniel Wong, M. Gokhale, Real-World Performance of Document-Similarity Web Attack Classifier In Embedded Hardware. LLNL Summer Intern Poster Symposium, 2010.
John O’Hollaren, Vairavan Laxman, Noah Olsman, Michael Benzimra, Daniel Wong, and Nielson Bernardo. SeaBee III. Technical report, University of Southern California Competition Robotics (USCR), University of Southern California, 2010.
Daniel Wong, D. Earl, F. Zyda and S. Koenig. Programming Pinball Machines for Fun and Education. Technical Report 08-901, Department of Computer Science, University of Southern California, Los Angeles (California), 2008.
GPU Computing 101: Why University Educators Are Pulling NVIDIA Teaching Kits into Their Classrooms, Nvidia, https://blogs.nvidia.com/blog/2019/05/23/nvidia-teaching-kits/, 2019
Interview, Nvidia's Turing Chip Opens Door to New Virtual Reality Realm, ECT News Network, https://www.ectnews.com/story/85506.html, 2018
Daniel Wong, S. Koenig, PinHorse: Teaching Old Pinball Machines New Tricks, www.pinballnews.com, 2009