Aditya Akella

Large-scale ML training jobs are frequently interrupted by hardware and software anomalies, failures, and management events. Existing solutions like checkpoint-restart or runtime reconfiguration suffer from long downtimes and degraded performance. We present TrainMover, a resilient LLM training r...

Testing applications under realistic resource contention is challenging because production workloads are often inaccessible due to privacy and proprietary concerns. Existing approaches either use simplistic resource stressors that fail to capture temporal dynamics and multi-resource interactions,...

We present CadLLM, a training-free method to accelerate the inference throughput of diffusion-based LLMs (dLLMs). We first investigate on the dynamic nature of the token unmasking confidence across blocks and steps. Based on this observation, we then present a lightweight adaptive approach that c...