RDX @ HotNets25

source/_data/SymbioticLab.bib

@@ -2157,3 +2157,16 @@ @InProceedings{mlenergy-benchmark:neuripsdb25
     As the adoption of Generative AI in real-world services grow explosively, energy has emerged as a critical bottleneck resource. However, energy remains a metric that is often overlooked, under-explored, or poorly understood in the context of building ML systems. We present the ML.ENERGY Benchmark, a benchmark suite and tool for measuring inference energy consumption under realistic service environments, and the corresponding ML.ENERGY Leaderboard, which have served as a valuable resource for those hoping to understand and optimize the energy consumption of their generative AI services. In this paper, we explain four key design principles for benchmarking ML energy we have acquired over time, and then describe how they are implemented in the ML.ENERGY Benchmark. We then highlight results from the early 2025 iteration of the benchmark, including energy measurements of 40 widely used model architectures across 6 different tasks, case studies of how ML design choices impact energy consumption, and how automated optimization recommendations can lead to significant (sometimes more than 40\%) energy savings without changing what is being computed by the model. The ML.ENERGY Benchmark is open-source and can be easily extended to various customized models and application scenarios.
   }
 }
+
+@InProceedings{rdx:hotnets25,
+  author    = {Yibo Huang and Yiming Qiu and Daqian Ding and Patrick Tser Jern Kon and Yiwen Zhang and Yuzhou Mao and Archit Bhatnagar and Mosharaf Chowdhury and Srinivas Devadas and Jiarong Xing and Ang Chen},
+  booktitle = {HotNets},
+  title     = {Remote Direct Code Execution},
+  year      = {2025},
+  publist_confkey = {HotNets'25},
+  publist_link = {paper || rdx-hotnets25.pdf},
+  publist_topic = {Datacenter Networking},
+  publist_abstract = { 
+    We make a case for remote direct code execution (RDX)—a vision that elevates the power of RDMA from memory access to code execution. Concretely, we target runtime extension frameworks such as Wasm filters, BPF programs, and UDF functions, where RDX enables an agent-less architecture that unlocks capabilities such as fast extension injection, update consistency guarantees, and minimal resource contentions. We envision a technical roadmap for RDX around a new CodeFlow abstraction, encompassing programming remote extensions, exposing management stubs, remotely validating and JIT compiling code, seamlessly linking code to local context, managing remote extension state, and synchronizing code to targets. The case studies and initial results demonstrate the end-to-end feasibility of RDX, and its potential to spark the next wave of RDMA innovations.
+  }
+}

source/publications/index.md

@@ -321,6 +321,10 @@ venues:
   HotNets:
     category: Workshops
     occurrences:
+    - key: HotNets'25
+      name: The 24th ACM Workshop on Hot Topics in Networks (HotNets'25)
+      date: 2025-11-17
+      url: https://conferences.sigcomm.org/hotnets/2025/
     - key: HotNets'23
       name: The 22nd ACM Workshop on Hot Topics in Networks (HotNets'23)
       date: 2023-11-28