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pro vyhledávání: '"scientific applications"'
Scientific applications typically generate large volumes of floating-point data, making lossy compression one of the most effective methods for data reduction, thereby lowering storage requirements and improving performance in large-scale application
Externí odkaz:
http://arxiv.org/abs/2412.07015
The evolution of floating-point computation has been shaped by algorithmic advancements, architectural innovations, and the increasing computational demands of modern technologies, such as artificial intelligence (AI) and high-performance computing (
Externí odkaz:
http://arxiv.org/abs/2411.12090
In scientific fields such as quantum computing, physics, chemistry, and machine learning, high dimensional data are typically represented using sparse tensors. Tensor contraction is a popular operation on tensors to exploit meaning or alter the input
Externí odkaz:
http://arxiv.org/abs/2410.10094
Autor:
Fink, Zane, Parasyris, Konstantinos, Rathi, Praneet, Georgakoudis, Giorgis, Menon, Harshitha, Bremer, Peer-Timo
Recent advancements in Machine Learning (ML) have substantially improved its predictive and computational abilities, offering promising opportunities for surrogate modeling in scientific applications. By accurately approximating complex functions wit
Externí odkaz:
http://arxiv.org/abs/2407.18352
As software systems increase in complexity, conventional monitoring methods struggle to provide a comprehensive overview or identify performance issues, often missing unexpected problems. Observability, however, offers a holistic approach, providing
Externí odkaz:
http://arxiv.org/abs/2408.15439
Lossy compression is one of the most effective methods for reducing the size of scientific data containing multiple data fields. It reduces information density through prediction or transformation techniques to compress the data. Previous approaches
Externí odkaz:
http://arxiv.org/abs/2409.18295