11 citations · 18 across the 15 of their papers we have counts for
5 papers · 1 filter
Revisiting Cauchy-Schwarz Norm Bounds for SDC Detection in LLM Inference
Anton Altenbernd, Dylan Everingham, Philipp Wiesner +1
Large Language Models (LLMs) execute billions of floating-point operations per output token across many hardware accelerators. At this scale, hardware-induced faults can bypass sys…
Reconciling Bottom-Up Metrics with Top-Down Reporting for Cloud Carbon Accounting
Philipp Wiesner, Loïc Lannelongue, Alexander Acker +1
Organizations seeking to reduce the carbon footprint of their cloud applications must rely on two largely disconnected ways of measuring it, each with important limitations. Bottom…
Evaluating MFU as a Proxy for GPU Power for Energy-Aware Simulation of LLM Training
Niklas Enskat, Philipp Wiesner
High-fidelity performance simulators are essential for designing and configuring efficient AI systems, yet today's tools lack the ability to predict power consumption. Established…
Exploring Silent Data Corruption as a Reliability Challenge in LLM Training
Anton Altenbernd, Philipp Wiesner, Odej Kao
As Large Language Models (LLMs) scale in size and complexity, the consequences of failures during training become increasingly severe. A major challenge arises from Silent Data Cor…
Distributed LLM Pretraining During Renewable Curtailment Windows: A Feasibility Study
Philipp Wiesner, Soeren Becker, Brett Cornick +3
Training large language models (LLMs) requires substantial compute and energy. At the same time, renewable energy sources regularly produce more electricity than the grid can absor…