Zobrazeno 1 - 10
of 2 685
pro vyhledávání: '"gpus"'
Publikováno v:
Frontiers in Artificial Intelligence, Vol 7 (2024)
Training Deep Neural Networks (DNNs) places immense compute requirements on the underlying hardware platforms, expending large amounts of time and energy. An important factor contributing to the long training times is the increasing dataset complexit
Externí odkaz:
https://doaj.org/article/d6f9b553a14041159bedc14dd9e23170
Publikováno v:
IEEE Access, Vol 12, Pp 126135-126144 (2024)
Kernel fusion is a crucial optimization technique for GPU applications, particularly deep neural networks, where it involves combining multiple consecutive kernels into a single larger kernel. This approach aims to enhance performance by reducing the
Externí odkaz:
https://doaj.org/article/c28dd8fd91494bd4a7834ab6e0dfcf1d
Publikováno v:
IEEE Access, Vol 12, Pp 110353-110360 (2024)
The rapid expansion of generative artificial intelligence (AI) technologies is projected to significantly affect electricity use in the global data center sector. Earlier research has proposed using data centers for load-balancing the future power gr
Externí odkaz:
https://doaj.org/article/1f9e4e90847d4600930090b6b68f5b58
Publikováno v:
IEEE Access, Vol 12, Pp 34354-34377 (2024)
The Single Instruction Multiple Data (SIMD) architecture, supported by various high-performance computing platforms, efficiently utilizes data-level parallelism. The SIMD model is used in traditional CPUs, dedicated vector systems, and accelerators s
Externí odkaz:
https://doaj.org/article/e2f812d1195041239c538012d680ae65
Autor:
Nathaniel Morgan, Caleb Yenusah, Adrian Diaz, Daniel Dunning, Jacob Moore, Erin Heilman, Evan Lieberman, Steven Walton, Sarah Brown, Daniel Holladay, Russell Marki, Robert Robey, Marko Knezevic
Publikováno v:
Information, Vol 15, Iss 11, p 716 (2024)
Efficiently simulating solid mechanics is vital across various engineering applications. As constitutive models grow more complex and simulations scale up in size, harnessing the capabilities of modern computer architectures has become essential for
Externí odkaz:
https://doaj.org/article/0f7ea2f9aefd43419d400f7978867e44
Autor:
Nathaniel Morgan, Caleb Yenusah, Adrian Diaz, Daniel Dunning, Jacob Moore, Erin Heilman, Calvin Roth, Evan Lieberman, Steven Walton, Sarah Brown, Daniel Holladay, Marko Knezevic, Gavin Whetstone, Zachary Baker, Robert Robey
Publikováno v:
Information, Vol 15, Iss 11, p 673 (2024)
This paper presents software advances to easily exploit computer architectures consisting of a multi-core CPU and CPU+GPU to accelerate diverse types of high-performance computing (HPC) applications using a single code implementation. The paper descr
Externí odkaz:
https://doaj.org/article/b90005bba94b461cbec2521162b0c6d5
Publikováno v:
Applied Sciences, Vol 14, Iss 21, p 9967 (2024)
The simulation of realistic systems plays a crucial role in modern sciences. Complex organs such as the brain can be described by mathematical models to reproduce biological behaviors. In the brain, the hippocampus is a critical region for memory and
Externí odkaz:
https://doaj.org/article/9b61955bb10c46ef88d75dc0fa615f0e
Publikováno v:
Revista Iberoamericana de Automática e Informática Industrial RIAI, Vol 21, Iss 1, Pp 1-16 (2023)
La conducción autónoma despierta un interés cada vez mayor en la industria, no solo en el sector de la automoción, sino también en el transporte de personas o mercancías por carretera o ferrocarril y en entornos de fabricación más controlados
Externí odkaz:
https://doaj.org/article/1b5bf1090f6842a3b2f3d7bbd46ffcec
Publikováno v:
Nuclear Engineering and Technology, Vol 55, Iss 7, Pp 2516-2533 (2023)
Several practical methods for accelerating the depletion calculation in a GPU-based Monte Carlo (MC) code PRAGMA are presented including the multilevel spectral collapse method and the vectorized Chebyshev rational approximation method (CRAM). Since
Externí odkaz:
https://doaj.org/article/ba5895a67c1f4027bafe2d718fe9946b
Autor:
Tian Zhou, Fangyu Zheng, Guang Fan, Lipeng Wan, Wenxu Tang, Yixuan Song, Yi Bian, Jingqiang Lin
Publikováno v:
Transactions on Cryptographic Hardware and Embedded Systems, Vol 2024, Iss 2 (2024)
The remarkable performance capabilities of AI accelerators offer promising opportunities for accelerating cryptographic algorithms, particularly in the context of lattice-based cryptography. However, current approaches to leveraging AI accelerators o
Externí odkaz:
https://doaj.org/article/22734d8eaee6410c8372164f3255bbf5