Bio

I am a 3rd year PhD student, co-advised by Prof. Sonia Fahmy and Prof. Vamsi Addanki. My research focuses on a variety of problems revolving around datacenter networks. Currently, I am exploring collective communication workloads and reconfigurable networks.

Research Interests: I am currently interested in the design and development of efficient, scalable, and fault-tolerant data center networks, with a focus on both the theoretical foundations and practical implementation aspects of modern network architectures.

Recent Publications

  1. SIGCOMM ’26
    Harvest: Adaptive Photonic Switching Schedules for Collective Communication in Scale-up Domains
    Mahir Rahman, Samuel Joseph, Nihar Kodkani, Behnaz Arzani, and Vamsi Addanki.
    Proceedings of the ACM SIGCOMM 2026 Conference, Denver, Colorado, USA, 2026.
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    As chip-to-chip silicon photonics gain traction for their bandwidth and energy efficiency, their circuit-switched nature raises a fundamental question for collective communication: when and how should the interconnect be reconfigured to realize these benefits? Establishing direct optical paths can reduce congestion and propagation delay; however, each reconfiguration incurs non-negligible overhead, making naive per-step reconfiguration impractical.
         We present Harvest, a systematic approach for synthesizing topology reconfiguration schedules that minimize collective completion time in photonic interconnects. Given a collective communication algorithm and its fixed communication schedule, Harvest determines how the interconnect should evolve over the course of the collective, explicitly balancing reconfiguration delay against congestion and propagation delay. We reduce the synthesis problem into a dynamic program with an underlying topology optimization subproblem and show that the approach applies to arbitrary collective communication algorithms. Furthermore, we exploit the algorithmic structure of a well-known AllReduce algorithm (Recursive Doubling) to synthesize optimal reconfiguration schedules without using any optimizers. By parameterizing the formulation using reconfiguration delay, Harvest naturally adapts to various photonic technologies. Using packet-level and flow-level evaluations, as well as hardware emulation on commercial GPUs, we show that the schedules synthesized by Harvest significantly reduce collective completion time across multiple collective algorithms compared to static interconnects and reconfigure-every-step baselines.
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    @inproceedings{harvestSigcomm2026,
      author = {Rahman, Mahir and Joseph, Samuel and Kodkani, Nihar and Arzani, Behnaz and Addanki, Vamsi},
      title = {Harvest: Adaptive Photonic Switching Schedules for Collective Communication in Scale-up Domains},
      year = {2026},
      booktitle = {Proceedings of the ACM SIGCOMM 2026 Conference}
    }