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Browsing by Author "San Juan Sebastián, Pablo"

Now showing items 1-7 of 7

Diseño e implementación de software paralelo para tratamiento de datos en memoria masiva

San Juan Sebastián, Pablo (Universitat Politècnica de València, 2015-06-25)

[EN] This work focuses on designing and coding an out-of-core algorithm to solve the cholesky decomposition over a multicore machine.
Efficient and portable GEMM-based convolution operators for deep neural network training on multicore processors

Barrachina, Sergio; Dolz, Manuel F.; San Juan, Pablo; Quintana-Ortí, Enrique S. (Elsevier, 2022-09)

[EN] Convolutional Neural Networks (CNNs) play a crucial role in many image recognition and classification tasks, recommender systems, brain-computer interfaces, etc. As a consequence, there is a notable interest in ...
Experiments with the NNMFPACK library: influence of beta parameter in the NNMF approximation error

San Juan Sebastián, Pablo; Vidal Maciá, Antonio Manuel; García Mollá, Víctor Manuel; Martínez Zaldívar, Francisco José; Ranilla, J.; Alonso, P.; Alonso-González, M.; Cortina, R. (CMMSE, 2015-07)

[EN] NnmfPack is a numerical library designed to compute efficiently the Non-Negative Matrix Factorization (NNMF). It has been conceived for shared memory heterogeneous parallel systems, and it supports, from its conception, ...
High performance and energy efficient inference for deep learning on multicore ARM processors using general optimization techniques and BLIS

Castelló, Adrián; SERGIO BARRACHINA; DOLZ ZARAGOZÁ, MANUEL FRANCISCO; Enrique S. Quintana-Ortí; San Juan-Sebastian, Pablo; Tomás Domínguez, Andrés Enrique (Elsevier, 2022-04)

[EN] We evolve PyDTNN, a framework for distributed parallel training of Deep Neural Networks (DNNs), into an efficient inference tool for convolutional neural networks. Our optimization process on multicore ARM processors ...
HPC algorithms for nonnegative decompositions

San Juan Sebastián, Pablo (Universitat Politècnica de València, 2018-11-26)

Muchos problemas procedentes de aplicaciones del mundo real pueden ser modelados como problemas matemáticos con magnitudes no negativas, y por tanto, las soluciones de estos problemas matemáticos solo tienen sentido si son ...
Low precision matrix multiplication for efficient deep learning in NVIDIA Carmel processors

San Juan-Sebastian, Pablo; Rodríguez-Sánchez, Rafael; Igual, Francisco D.; Alonso-Jordá, Pedro; Quintana-Ortí, Enrique S. (Springer-Verlag, 2021-10)

[EN] We introduce a high performance, multi-threaded realization of the gemm kernel for the ARMv8.2 architecture that operates with 16-bit (half precision)/queryKindly check and confirm whether the corresponding author is ...
Updating/downdating the NonNegative Matrix Factorization

San Juan Sebastián, Pablo; Vidal Maciá, Antonio Manuel; García Mollá, Víctor Manuel (Elsevier, 2016-12-08)

The Non-Negative Matrix Factorization (NNMF) is a recent numerical tool that, given a nonnegative data matrix, tries to obtain its factorization as the approximate product of two nonnegative matrices. Nowadays, this ...