Zobrazeno 1 - 10
of 600
pro vyhledávání: '"Keyes, David E."'
Publikováno v:
Neural Information Processing Systems (NeurIPS). Machine Learning with New Compute Paradigms (MLNCP) Workshop, October 2024
We present a novel approach for accelerating AI performance by leveraging Anderson extrapolation, a vector-to-vector mapping technique based on a window of historical iterations. By identifying the crossover point where a mixing penalty is incurred,
Externí odkaz:
http://arxiv.org/abs/2410.19460
This paper explores the performance optimization of out-of-core (OOC) Cholesky factorization on shared-memory systems equipped with multiple GPUs. We employ fine-grained computational tasks to expose concurrency while creating opportunities to overla
Externí odkaz:
http://arxiv.org/abs/2410.09819
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
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
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
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
Autor:
Abdulah, Sameh, Alamri, Faten, Nag, Pratik, Sun, Ying, Ltaief, Hatem, Keyes, David E., Genton, Marc G.
In the last few decades, the size of spatial and spatio-temporal datasets in many research areas has rapidly increased with the development of data collection technologies. As a result, classical statistical methods in spatial statistics are facing c
Externí odkaz:
http://arxiv.org/abs/2211.03119
Autor:
Abdulah, Sameh, Ejarque, Jorge, Marzouk, Omar, Ltaief, Hatem, Sun, Ying, Genton, Marc G., Badia, Rosa M., Keyes, David E.
Publikováno v:
In Future Generation Computer Systems December 2024 161:248-258
Hierarchical $\mathcal{H}^2$-matrices are asymptotically optimal representations for the discretizations of non-local operators such as those arising in integral equations or from kernel functions. Their $O(N)$ complexity in both memory and operator
Externí odkaz:
http://arxiv.org/abs/2109.05451