Parallel Computing

杂志介绍

Parallel Computing杂志介绍

《Parallel Computing》是一本以English为主的未开放获取国际优秀期刊，中文名称并行计算，本刊主要出版、报道计算机科学-COMPUTER SCIENCE, THEORY & METHODS领域的研究动态以及在该领域取得的各方面的经验和科研成果，介绍该领域有关本专业的最新进展，探讨行业发展的思路和方法，以促进学术信息交流，提高行业发展。该刊已被国际权威数据库SCIE收录，为该领域相关学科的发展起到了良好的推动作用，也得到了本专业人员的广泛认可。该刊最新影响因子为2，最新CiteScore 指数为3.5。

本刊近期中国学者发表的论文主要有：

• Online cost-rejection rate scheduling for resource requests in hybrid clouds

  Author: Yanhua Cao, Li Lu, Jiadi Yu, Shiyou Qian, Yanmin Zhu, Minglu Li

• Hybrid storage architecture and efficient MapReduce processing for unstructured data

  Author: Weiming Lu, Yaoguang Wang, Jingyuan Jiang, Jian Liu, Yapeng Shen, Baogang Wei

• ParConnect reproducibility report

  Author: Lei Yang, Yilong Li, Zhenxin Fu, Zhuohan Li, Wenbin Hou, Haoze Wu, Xiaolin Wang, Yun Liang

• A scalable interface-resolved simulation of particle-laden flow using the lattice Boltzmann method

  Author: Nicholas Geneva, Cheng Peng, Xiaoming Li, Lian-Ping Wang

英文介绍

Parallel Computing杂志英文介绍

Parallel Computing is an international journal presenting the practical use of parallel computer systems, including high performance architecture, system software, programming systems and tools, and applications. Within this context the journal covers all aspects of high-end parallel computing from single homogeneous or heterogenous computing nodes to large-scale multi-node systems.

Parallel Computing features original research work and review articles as well as novel or illustrative accounts of application experience with (and techniques for) the use of parallel computers. We also welcome studies reproducing prior publications that either confirm or disprove prior published results.

Particular technical areas of interest include, but are not limited to:

-System software for parallel computer systems including programming languages (new languages as well as compilation techniques), operating systems (including middleware), and resource management (scheduling and load-balancing).

-Enabling software including debuggers, performance tools, and system and numeric libraries.

-General hardware (architecture) concepts, new technologies enabling the realization of such new concepts, and details of commercially available systems

-Software engineering and productivity as it relates to parallel computing

-Applications (including scientific computing, deep learning, machine learning) or tool case studies demonstrating novel ways to achieve parallelism

-Performance measurement results on state-of-the-art systems

-Approaches to effectively utilize large-scale parallel computing including new algorithms or algorithm analysis with demonstrated relevance to real applications using existing or next generation parallel computer architectures.

-Parallel I/O systems both hardware and software

-Networking technology for support of high-speed computing demonstrating the impact of high-speed computation on parallel applications