Results 51 to 60 of about 27,095 (213)

Exploiting Amorphous Data Parallelism to Speed-Up Massive Time-Dependent Shortest-Path Computations [PDF]

, 2019
We aim at exploiting parallelism in shared-memory multiprocessing systems, in order to speed up the execution time with as small redundancy in work as possible, for an elementary task that comes up frequently as a subroutine in the daily maintenance of ...
Paraskevopoulos, Andreas, Zaroliagis, Christos, Papadopoulos, Anastasios, Kontogiannis, Spyros   +3 more
core   +1 more source

Hybrid Task- and Data-Parallelization on Heterogeneous Platform Using Model-Based Tool and Library Function Generation

IEEE Access
Modern embedded systems such as autonomous vehicles and robotics increasingly rely on high-performance computing to satisfy real-time and data-intensive demands.
Shanwen Wu, Qi Li, Masato Edahiro
doaj   +1 more source

THE PERFORMANCE OF CONVOLUTIONAL NEURAL NETWORKS USING AN ACCELERATOR

Компютерні системи та інформаційні технології
The effectiveness of convolutional neural networks (CNNs) has been demonstrated across various fields, including computer vision, natural language processing, medical imaging, and autonomous systems.
Tymur ISAIEV, Tetiana KYSIL
doaj   +1 more source

Exploring Various Levels of Parallelism in High-Performance CRC Algorithms

IEEE Access, 2019
Modern processors have increased the capabilities of instruction-level parallelism (ILP) and thread-level parallelism (TLP). These resources, however, typically exhibit poor utilization on conventional cyclic redundancy check (CRC) algorithms.
Mucong Chi, Dazhong He, Jun Liu
doaj   +1 more source

Efficient electro-magnetic analysis of a GPU bitsliced AES implementation

Cybersecurity, 2020
The advent of CUDA-enabled GPU makes it possible to provide cloud applications with high-performance data security services. Unfortunately, recent studies have shown that GPU-based applications are also susceptible to side-channel attacks.
Yiwen Gao, Yongbin Zhou, Wei Cheng
doaj   +1 more source

Towards a streaming model for nested data parallelism

, 2013
The language-integrated cost semantics for nested data parallelism pioneered by NESL provides an intuitive, high-level model for predicting performance and scalability of parallel algorithms with reasonable accuracy.
Andrzej Filinski, Filinski, Andrzej, Frederik M. Madsen, Madsen, Frederik Meisner   +3 more
core   +1 more source

Streaming nested data parallelism on multicores

, 2016
The paradigm of nested data parallelism (NDP) allows a variety of semi-regular computation tasks to be mapped onto SIMD-style hardware, including GPUs and vector units.
Andrzej Filinski, Filinski, Andrzej, Frederik M. Madsen, Madsen, Frederik Meisner   +3 more
core   +1 more source

Distributed Model Training based on Data Parallelism in Edge Computing-enabled Elastic Optical Networks

, 2021
IEEE The emergence of edge computing provides an effective solution to execute distributed model training (DMT). The deployment of training data among edge nodes affects the training efficiency and network resource usage.
Li, Yajie, Li, Jun, Zeng, Zebin, Zhao, Yongli, Zhang, Jie, Yan, Boyuan   +5 more
core   +1 more source

Strategies and Principles of Distributed Machine Learning on Big Data

Engineering, 2016
The rise of big data has led to new demands for machine learning (ML) systems to learn complex models, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as ...
Eric P. Xing, Qirong Ho, Pengtao Xie, Dai Wei   +3 more
doaj   +1 more source

To parallelize or not to parallelize, control and data flow issue

CoRR, 2013
New trends towards multiple core processors imply using standard programming models to develop efficient, reliable and portable programs for distributed memory multiprocessors and workstation PC clusters. Message passing using MPI is widely used to write efficient, reliable and portable applications.
openaire   +2 more sources