Results 21 to 30 of about 186,522 (338)
Parallel Efficient Data Loading [PDF]
Proceedings of the 8th International Conference on Data Science, Technology and Applications, 2019 In this paper we discuss how we architected and developed a parallel data loader for LeanXcale database. The loader is characterized for its efficiency and parallelism. LeanXcale can scale up and scale out to very large numbers and loading data in the traditional way it is not exploiting its full potential in terms of the loading rate it can reach. For
Jiménez Peris, Ricardo +5 more
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Parallel Optimization Method of Unstructured-grid Computing in CFD for DomesticHeterogeneous Many-core Architecture [PDF]
Jisuanji kexue, 2022 Sunway TaihuLight ranked first in the global supercomputer top 500 list 2016－2018 with a peak performance of 125.4 PFlops.Its computing power is mainly attributed to the domestic SW26010 many-core RISC processor.CFD unstructured-grid computing has always
CHEN Xin, LI Fang, DING Hai-xin, SUN Wei-ze, LIU Xin, CHEN De-xun, YE Yue-jin, HE Xiang
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Hybrid Image-/Data-Parallel Rendering Using Island Parallelism
2022 IEEE 12th Symposium on Large Data Analysis and Visualization (LDAV), 2022 Conference Name: IEEE Symposium on Large Data Analysis and Visualization (LDAV) Date of Conference: 16-16 October 2022 In parallel ray tracing, techniques fall into one of two camps: image-parallel techniques aim at increasing frame rate by replicating scene data across nodes and splitting the rendering work across different ranks, and data-parallel ...
Zellmann, S. +5 more
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Novel VLSI Architectures and Micro-Cell Libraries for Subscalar Computations
IEEE Access, 2022 Parallelism is the key to enhancing the throughput of computing structures. However, it is well established that the presence of data-flow dependencies adversely impacts the exploitation of such parallelism. This paper presents a case for a new computing
Kumar Sambhav Pandey, Hitesh Shrimali
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Modular design of data-parallel graph algorithms [PDF]
, 2013 Amorphous Data Parallelism has proven to be a suitable vehicle for implementing concurrent graph algorithms effectively on multi-core architectures.
Christianson, B. +2 more
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Spark deployment and performance evaluation on the MareNostrum supercomputer [PDF]
, 2015 In this paper we present a framework to enable data-intensive Spark workloads on MareNostrum, a petascale supercomputer designed mainly for compute-intensive applications.
Ayguadé Parra, Eduard +9 more
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ISPRS International Journal of Geo-Information, 2017 Parallel processing in the real-time visualization of three-dimensional Geographic Information Systems (3DGIS) has tended to concentrate on algorithm levels in recent years, and most of the existing methods employ multiple threads in a Central Processing
Dongbo Zhou +4 more
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Towards accelerating model parallelism in distributed deep learning systems.
PLoS ONE, 2023 Modern deep neural networks cannot be often trained on a single GPU due to large model size and large data size. Model parallelism splits a model for multiple GPUs, but making it scalable and seamless is challenging due to different information sharing ...
Hyeonseong Choi +3 more
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Applied Sciences, 2021 The rapid development of artificial intelligence technology has made deep neural networks (DNNs) widely used in various fields. DNNs have been continuously growing in order to improve the accuracy and quality of the models.
Yingchi Mao +4 more
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Parallelizing the Data Cube [PDF]
, 2001 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dehne, Frank +3 more
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