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Found 5 papers, 1 papers with code
Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach
Driven by the technological trend, this paper focuses on hierarchical resource partitioning on modern GPUs, and as an example, we utilize a combination of two different features available on recent NVIDIA GPUs in a hierarchical manner: MPS (Multi-Process Service), a finer-grained logical partitioning; and MIG (Multi-Instance GPU), a coarse-grained physical partitioning.
Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments
In federated learning, all networked clients contribute to the model training cooperatively.
Quantum Algorithms for Solving Ordinary Differential Equations via Classical Integration Methods
Identifying computational tasks suitable for (future) quantum computers is an active field of research.
Quantum Physics
Correlation-wise Smoothing: Lightweight Knowledge Extraction for HPC Monitoring Data
Modern High-Performance Computing (HPC) and data center operators rely more and more on data analytics techniques to improve the efficiency and reliability of their operations.
DCDB Wintermute: Enabling Online and Holistic Operational Data Analytics on HPC Systems
However, while monitoring is a common reality in HPC, there is no well-stated and comprehensive list of requirements, nor matching frameworks, to support holistic and online ODA.
