Alvin R. Lebeck

Professor of Computer Science

My interests span atoms to applications, with a foundation centered in computer architecture and systems. I enjoy a combination of interdisciplinary and conventional research.

Appointments and Affiliations

  • Professor of Computer Science
  • Core Faculty in Innovation & Entrepreneurship

Contact Information

  • Office Location: 308 Research Dr., Dept. of Computer Science, Durham, NC 27708
  • Office Phone: +1 919 265 7129
  • Email Address: alvy@cs.duke.edu
  • Websites:

Education

  • B.S. University of Wisconsin, Madison, 1989
  • M.S. University of Wisconsin, Madison, 1991
  • Ph.D. University of Wisconsin, Madison, 1995

Awards, Honors, and Distinctions

  • Alan D. Berenbaum Distinguished Service Award. ACM SIGARCH. 2020
  • Recognition of Service Award. Association of Computing Machinery. 2019
  • Fellow. IEEE. 2017
  • Honorable Mention, IEEE MICRO Top Picks from Computer Architecture Conferences . IEEE MICRO. 2017
  • Best Paper. 6th International Workshop on Network on Chip Architectures. 2013
  • IEEE MICRO Top Picks from Computer Architecture Conferences . IEEE. 2010
  • IEEE MICRO Top Picks from Computer Architecture Conferences . IEEE MICRO. 2009
  • Best Paper. 31st Annual ACM/IEEE International Symposium on Microarchitecture. 1998
  • NSF CAREER. National Science Foundation. 1997
  • Outstanding Graduate Student Researcher. Department of Computer Sciences, University of Wisconsin at Madison. 1995

Courses Taught

  • COMPSCI 210D: Introduction to Computer Systems

In the News

Representative Publications

  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).
  • Kong, X., J. Chen, W. Bai, Y. Xu, M. Elhaddad, S. Raindel, J. Padhye, A. R. Lebeck, and D. Zhuo. “Understanding RDMA Microarchitecture Resources for Performance Isolation.” In Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023, 31–48, 2023.
  • Snyder, J., A. R. Lebeck, and D. Zhuo. “RDMA Congestion Control: It Is Only for the Compliant.” IEEE Micro 43, no. 1 (January 1, 2023): 76–82. https://doi.org/10.1109/MM.2022.3208746.
  • Snyder, J., and A. R. Lebeck. “Fast Convergence to Fairness for Reduced Long Flow Tail Latency in Datacenter Networks.” In Proceedings - 2022 IEEE 36th International Parallel and Distributed Processing Symposium, IPDPS 2022, 1007–17, 2022. https://doi.org/10.1109/IPDPS53621.2022.00102.
  • Zhang, X., R. Bashizade, Y. Wang, S. Mukherjee, and A. R. Lebeck. “Statistical robustness of Markov chain Monte Carlo accelerators.” In International Conference on Architectural Support for Programming Languages and Operating Systems - ASPLOS, 959–74, 2021. https://doi.org/10.1145/3445814.3446697.
  • Bashizade, Ramin, Xiangyu Zhang, Sayan Mukherjee, and Alvin R. Lebeck. “Accelerating Markov Random Field Inference with Uncertainty Quantification.” CoRR abs/2108.00570 (2021).