Zobrazeno 1 - 10
of 1 271
pro vyhledávání: '"KEYES, DAVID"'
Autor:
Ltaief, Hatem, Alomairy, Rabab, Cao, Qinglei, Ren, Jie, Slim, Lotfi, Kurth, Thorsten, Dorschner, Benedikt, Bougouffa, Salim, Abdelkhalak, Rached, Keyes, David E.
We exploit the widening margin in tensor-core performance between [FP64/FP32/FP16/INT8,FP64/FP32/FP16/FP8/INT8] on NVIDIA [Ampere,Hopper] GPUs to boost the performance of output accuracy-preserving mixed-precision computation of Genome-Wide Associati
Externí odkaz:
http://arxiv.org/abs/2409.01712
Autor:
Abdulah, Sameh, Baker, Allison H., Bosilca, George, Cao, Qinglei, Castruccio, Stefano, Genton, Marc G., Keyes, David E., Khalid, Zubair, Ltaief, Hatem, Song, Yan, Stenchikov, Georgiy L., Sun, Ying
We present the design and scalable implementation of an exascale climate emulator for addressing the escalating computational and storage requirements of high-resolution Earth System Model simulations. We utilize the spherical harmonic transform to s
Externí odkaz:
http://arxiv.org/abs/2408.04440
Deep AndersoNN accelerates AI by exploiting the continuum limit as the number of explicit layers in a neural network approaches infinity and can be taken as a single implicit layer, known as a deep equilibrium model. Solving for deep equilibrium mode
Externí odkaz:
http://arxiv.org/abs/2407.19724
Autor:
Zhang, Xiran, Abdulah, Sameh, Cao, Jian, Ltaief, Hatem, Sun, Ying, Genton, Marc G., Keyes, David E.
Addressing the statistical challenge of computing the multivariate normal (MVN) probability in high dimensions holds significant potential for enhancing various applications. One common way to compute high-dimensional MVN probabilities is the Separat
Externí odkaz:
http://arxiv.org/abs/2405.14892
We address the estimation of seismic wavefields by means of Multidimensional Deconvolution (MDD) for various redatuming applications. While offering more accuracy than conventional correlation-based redatuming methods, MDD faces challenges due to the
Externí odkaz:
http://arxiv.org/abs/2404.01870
In recent years, we have witnessed the emergence of scientific machine learning as a data-driven tool for the analysis, by means of deep-learning techniques, of data produced by computational science and engineering applications. At the core of these
Externí odkaz:
http://arxiv.org/abs/2403.12188
Gaussian processes (GPs) are commonly used for geospatial analysis, but they suffer from high computational complexity when dealing with massive data. For instance, the log-likelihood function required in estimating the statistical model parameters f
Externí odkaz:
http://arxiv.org/abs/2403.07412
This paper presents a novel factorization-based, low-rank regularization method for solving multidimensional deconvolution problems in the frequency domain. In this approach, each frequency component of the unknown wavefield is represented as a compl
Externí odkaz:
http://arxiv.org/abs/2312.11004
Autor:
Abdulah, Sameh, Ejarque, Jorge, Marzouk, Omar, Ltaief, Hatem, Sun, Ying, Genton, Marc G., Badia, Rosa M., Keyes, David E.
HPC-based applications often have complex workflows with many software dependencies that hinder their portability on contemporary HPC architectures. In addition, these applications often require extraordinary efforts to deploy and execute at performa
Externí odkaz:
http://arxiv.org/abs/2312.07748