2 citations · 3 across the 8 of their papers we have counts for
8 papers
CornerCase: Automated Extremal Testing of Protocol Implementations using LLMs
Rathin Singha, Kuan Qian, Srinath Saikrishnan +6
Many software bugs in network protocol implementations arise near specification boundaries, such as inputs just within or outside allowed ranges, or messages that are valid in isol…
Dynamic Rebatching for Efficient Early-Exit Inference with DREX
Xuting Liu, Daniel Alexander, Siva Kesava Reddy Kakarla +2
Early-Exit (EE) is a Large Language Model (LLM) architecture that accelerates inference by allowing easier tokens to be generated using only a subset of the model's layers. However…
A Performance Analyzer for a Public Cloud's ML-Augmented VM Allocator
Roozbeh Bostandoost, Pooria Namyar, Siva Kesava Reddy Kakarla +8
Cloud operators increasingly deploy multiple ML models in their VM allocation pipelines. In such settings, individually benign predictions can shift and compound, severely degradin…
Robust Heuristic Algorithm Design with LLMs
Pantea Karimi, Dany Rouhana, Pooria Namyar +3
We posit that we can generate more robust and performant heuristics if we augment approaches using LLMs for heuristic design with tools that explain why heuristics underperform and…
Extremal Testing for Network Software using LLMs
Rathin Singha, Harry Qian, Srinath Saikrishnan +4
Physicists often manually consider extreme cases when testing a theory. In this paper, we show how to automate extremal testing of network software using LLMs in two steps: first,…
Towards Safer Heuristics With XPlain
Pantea Karimi, Solal Pirelli, Siva Kesava Reddy Kakarla +5
Many problems that cloud operators solve are computationally expensive, and operators often use heuristic algorithms (that are faster and scale better than optimal) to solve them m…
Eywa: Automating Model Based Testing using LLMs
Rajdeep Mondal, Rathin Singha, Todd Millstein +3
Model-based testing (MBT), whereby a model of the system under test is analyzed to generate high-coverage test cases, has been used to test protocol implementations. A key barrier…
Rethinking Machine Learning Collective Communication as a Multi-Commodity Flow Problem
Behnaz Arzani, Siva Kesava Reddy Kakarla, Miguel Castro +3
We show communication schedulers' recent work proposed for ML collectives does not scale to the increasing problem sizes that arise from training larger models. These works also of…