9 citations · 25 across the 14 of their papers we have counts for
18 papers
E8 Lattice Key Quantization for Low-Bit KV-Cache Compression
Sasho Nedelkoski, Alexander Acker, Soeren Becker +2
Compressing the key-value (KV) cache is essential for serving long-context large language models (LLMs) under memory constraints. While current scalar quantization methods achieve…
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…
Collaborative Reasoning Distillation via Cross-Feedback and Coherent Curation
Taehoon Kim, Seunggeun Cho, Dongsu Han
Reasoning capabilities are critical for advancing Large Language Models, yet current approaches either require massive computational budgets or struggle to effectively distill reas…
About the Influence of Workflow Topology on Task Intensity Prediction through Graph Learning
Max Otto, Haci Ismail Aslan, Joel Witzke +2
Efficient resource provisioning for large-scale workflows on cloud infrastructures is a critical performance engineering challenge. These workflows are often structured as directed…
Scalable and Data-Driven Decision Support in the Maintenance, Repair, and Overhaul Process
Houkun Zhu, Helena Ebel, Dominik Scheinert +3
Several businesses apply maintenance, repair, and overhaul (MRO) principles to the life-cycle of their existing products. In cases like casted gas turbine component Product Lifecyc…
Optimizing Memory Allocation in Distributed Clusters with Predictive Modeling
Jonathan Bader, Edgar Blumenthal, Marten Eckardt +5
In modern distributed systems, efficient resource allocation is a vital aspect to maintain scalability, reduce operational costs, and ensure fast execution even across heterogeneou…
Beyond Microservices: Testing Web-Scale RCA Methods on GPU-Driven LLM Workloads
Dominik Scheinert, Alexander Acker, Thorsten Wittkopp +6
Large language model (LLM) services have become an integral part of search, assistance, and decision-making applications. However, unlike traditional web or microservices, the hard…
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…
Learning Process Energy Profiles from Node-Level Power Data
Jonathan Bader, Julius Irion, Jannis Kappel +4
The growing demand for data center capacity, driven by the growth of high-performance computing, cloud computing, and especially artificial intelligence, has led to a sharp increas…
Distributed Low-Communication Training with Decoupled Momentum Optimization
Sasho Nedelkoski, Alexander Acker, Odej Kao +2
The training of large models demands substantial computational resources, typically available only in data centers with high-bandwidth interconnects. However, reducing the reliance…
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…
Predicting the Performance of Scientific Workflow Tasks for Cluster Resource Management: An Overview of the State of the Art
Jonathan Bader, Kathleen West, Soeren Becker +5
Scientific workflow management systems support large-scale data analysis on cluster infrastructures. For this, they interact with resource managers which schedule workflow tasks on…
Flora: Efficient Cloud Resource Selection for Big Data Processing via Job Classification
Jonathan Will, Lauritz Thamsen, Jonathan Bader +1
Distributed dataflow systems like Spark and Flink enable data-parallel processing of large datasets on clusters of cloud resources. Yet, selecting appropriate computational resourc…
Experimentally Evaluating the Resource Efficiency of Big Data Autoscaling
Jonathan Will, Nico Treide, Lauritz Thamsen +1
Distributed dataflow systems like Spark and Flink enable data-parallel processing of large datasets on clusters. Yet, selecting appropriate computational resources for dataflow job…
KS+: Predicting Workflow Task Memory Usage Over Time
Jonathan Bader, Ansgar Lößer, Lauritz Thamsen +2
Scientific workflow management systems enable the reproducible execution of data analysis pipelines on cluster infrastructures managed by resource managers such as Kubernetes, Slur…
Sizey: Memory-Efficient Execution of Scientific Workflow Tasks
Jonathan Bader, Fabian Skalski, Fabian Lehmann +4
As the amount of available data continues to grow in fields as diverse as bioinformatics, physics, and remote sensing, the importance of scientific workflows in the design and impl…
SyncMesh: Improving Data Locality for Function-as-a-Service in Meshed Edge Networks
Daniel Habenicht, Kevin Kreutz, Soeren Becker +3
The increasing use of Internet of Things devices coincides with more communication and data movement in networks, which can exceed existing network capabilities. These devices ofte…
C3O: Collaborative Cluster Configuration Optimization for Distributed Data Processing in Public Clouds
Jonathan Will, Lauritz Thamsen, Dominik Scheinert +2
Distributed dataflow systems enable data-parallel processing of large datasets on clusters. Public cloud providers offer a large variety and quantity of resources that can be used…