11 citations · 18 across the 15 of their papers we have counts for
17 papers
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…
Efficiency Will Not Lead to Sustainable Reasoning AI
Philipp Wiesner, Daniel W. O'Neill, Francesca Larosa +1
AI research is increasingly moving toward complex problem solving, where models are optimized not only for pattern recognition but for multi-step reasoning. Historically, computing…
What happens when nanochat meets DiLoCo?
Alexander Acker, Soeren Becker, Sasho Nedelkoski +3
Although LLM training is typically centralized with high-bandwidth interconnects and large compute budgets, emerging methods target communication-constrained training in distribute…
Optimizing Microgrid Composition for Sustainable Data Centers
Julius Irion, Philipp Wiesner, Jonathan Bader +1
As computing energy demand continues to grow and electrical grid infrastructure struggles to keep pace, an increasing number of data centers are being planned with colocated microg…
Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations
Miray Özcan, Philipp Wiesner, Philipp Weiß +1
The environmental impact of Large Language Models (LLMs) is rising significantly, with inference now accounting for more than half of their total lifecycle carbon emissions. Howeve…
Moving Beyond Marginal Carbon Intensity: A Poor Metric for Both Carbon Accounting and Grid Flexibility
Philipp Wiesner, Odej Kao
Marginal Carbon Intensity (MCI) has been promoted as an effective metric for carbon-aware computing. Although it is already considered as impractical for carbon accounting purposes…
Choosing the Right Battery Model for Data Center Simulations
Paul Kilian, Philipp Wiesner, Odej Kao
As demand for computing resources continues to rise, the increasing cost of electricity and anticipated regulations on carbon emissions are prompting changes in data center power s…
-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation
Haci Ismail Aslan, Philipp Wiesner, Ping Xiong +1
Graph Neural Networks (GNNs) are playing an increasingly important role in the efficient operation and security of computing systems, with applications in workload scheduling, anom…
Carbon-Aware Quality Adaptation for Energy-Intensive Services
Philipp Wiesner, Dennis Grinwald, Philipp Weiß +3
The energy demand of modern cloud services, particularly those related to generative AI, is increasing at an unprecedented pace. To date, carbon-aware computing strategies have pri…
LogRCA: Log-based Root Cause Analysis for Distributed Services
Thorsten Wittkopp, Philipp Wiesner, Odej Kao
To assist IT service developers and operators in managing their increasingly complex service landscapes, there is a growing effort to leverage artificial intelligence in operations…
Federated Learning over Connected Modes
Dennis Grinwald, Philipp Wiesner, Shinichi Nakajima
Statistical heterogeneity in federated learning poses two major challenges: slow global training due to conflicting gradient signals, and the need of personalization for local dist…
Offloading Real-Time Tasks in IIoT Environments under Consideration of Networking Uncertainties
Ilja Behnke, Philipp Wiesner, Paul Voelker +1
Offloading is a popular way to overcome the resource and power constraints of networked embedded devices, which are increasingly found in industrial environments. It involves movin…
Carbon-Awareness in CI/CD
Henrik Claßen, Jonas Thierfeldt, Julian Tochman-Szewc +2
While the environmental impact of digitalization is becoming more and more evident, the climate crisis has become a major issue for society. For instance, data centers alone accoun…