Results 41 to 50 of about 27,095 (213)

Distributed training of large language models: A survey

Natural Language Processing Journal
The emergence of large language models (LLMs) such as ChatGPT has opened up groundbreaking possibilities, enabling a wide range of applications in diverse fields, including healthcare, law, and education.
Fanlong Zeng, Wensheng Gan, Yongheng Wang, Philip S. Yu   +3 more
doaj   +1 more source

Multilog and data or-parallelism

The Journal of Logic Programming, 1996
Summary: This paper describes the design, implementation, performance, and analysis of MultiLog -- a logic programming system whose distinguishing feature is the presence of multiple, concurrent binding environments (multiple substitutions) with a single thread of control.
openaire   +1 more source

Data Parallel Address Architecture [PDF]

IEEE Computer Architecture Letters, 2006
Data parallel memory systems must maintain a large number of outstanding memory references to fully use increasing DRAM bandwidth in the presence of increasing latency. At the same time, the throughput of modern DRAMs is very sensitive to access pattern's due to the time required to precharge and activate banks and to switch between read and write ...
Jung Ho Ahn, William J. Dally
openaire   +1 more source

A snapshot of parallelism in distributed deep learning training

Revista Colombiana de Computación
The accelerated development of applications related to artificial intelligence has generated the creation of increasingly complex neural network models with enormous amounts of parameters, currently reaching up to trillions of parameters.
Hairol Romero-Sandí, Gabriel Núñez, Elvis Rojas   +2 more
doaj   +3 more sources

AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid

Future Internet
Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid.
Jing Zou, Peizhe Xin, Chang Wang, Heli Zhang, Lei Wei, Ying Wang   +5 more
doaj   +1 more source

Enlarging the scope of vector-based computations: extending Fortran 90 by nested data parallelism

, 1997
This paper describes the integration of nested data parallelism into Fortran 90. Unlike flat data parallelism, nested data parallelism directly provides means for handling irregular data structures and certain forms of control parallelism, such as divide-
Simons, M., Darlington, J., Kohler, M., Guo, Y., Jahnichen, S., Chakravarty, M. M.T., Keller, G., Au, K. T.P., Pfannenstiel, W.   +8 more
core   +1 more source

Area-Efficient Parallel Reconfigurable Stream Processor for Symmetric Cryptograph

IEEE Access, 2021
Represented by application-specific instruction set processors (ASIPs) and array processors, existing cryptographic processors face challenges in application to mobile terminals with sensitive security requirements.
Yufei Zhu, Zuocheng Xing, Jinhui Xue, Zerun Li, Yifan Hu, Yang Zhang, Yongzhong Li   +6 more
doaj   +1 more source

Nested Data Parallelism for Image Processing Algorithms : Optimisations in Functional Programming Languages [PDF]

, 2015
Nested Data Parallelism ermöglicht den prägnanten und tre endenAusdruck irregulär-paralleler Programme und erreicht trotzdessen eine Performance vergleichbar zu Flat Data Parallelism. Dies wird durch eine Programmtransformation (’Vectorization’) erreicht.
Sahoo, Chandrakant Swaneet Kumar
core  

LC/DC: Lockless Containers and Data Concurrency a Novel Nonblocking Container Library for Multicore Applications

IEEE Access, 2013
Exploiting the parallelism in multiprocessor systems is a major challenge in modern computer science. Multicore programming demands a change in the way we design and use fundamental data structures.
Damian Dechev, Pierre Laborde, Steven D. Feldman   +2 more
doaj   +1 more source

SCALABLE PARALLEL TRAINING OF DEEP NEURAL NETWORKS FOR IMAGE CLASSIFICATION USING TENSOR PROCESSING UNITS

Компютерні системи та інформаційні технології
Image classification using machine learning techniques is crucial in fields such as medicine, ecology, and agriculture, where large datasets of images need to be processed efficiently.
Леся МОЧУРАД, Христина ДОЛИНСЬКА, Тетяна УФІМЦЕВА   +2 more
doaj   +1 more source