Journal Information
IEEE Transactions on Parallel and Distributed Systems (TPDS)
https://www.computer.org/csdl/journal/td
Impact Factor:
3.402
Publisher:
IEEE
ISSN:
1045-9219
Viewed:
18918
Tracked:
83

Call For Papers
IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to:

    Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing.

    Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems.

    Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation.

    Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.
Last updated by Dou Sun in 2020-05-07
Special Issues
Special Issue on Parallel and Distributed Computing Techniques for AI, ML, and DL 
Submission Date: 2020-07-01

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) have established themselves in a multitude of domains because of their ability to process and model unstructured input data. As these fields are becoming increasingly integrated into our daily lives, there is a significant amount of interest among the community in improving AI/ML/DL through the use of parallel and distributed computing techniques (sometimes referred to as “PDC for AI/ML/DL”) as well as to apply AI/ML/DL techniques to improve traditional parallel and distributed computing systems (sometimes referred to as “AI/ML/DL for PDC”). In this special section, we hope to bring together community research in this area into a curated selection of articles. About TPDS special sections TPDS has recently started a new initiative called “special sections.” Compared with regular submissions to TPDS, special sections have some differences: (1) submissions are focused on special topics of interest (similar to special issues); (2) special sections have fixed deadlines for submission and notifications; and (3) special sections have a standing committee of reviewers similar to conferences. This is the first such special section that we are planning. Timeline The timeline for the submission and review process is as follows (all deadlines are midnight anywhere on earth (https://www.worldtimeserver.com/time-zones/aoe/). Round 1: Submission deadline: July 1st, 2020 (no extensions) First-round review notification: August 5th, 2020 (5 weeks for reviews) Notification would be one of ACCEPT, REJECT, MAJOR REVISIONS, or MINOR REVISIONS Round 2a (only for papers that get a minor revision in Round 1): Second-round submission deadline: August 19th, 2020 (2 weeks for re-submission) Second-round review notification: September 2nd, 2020 (2 weeks for reviews) Notification would be one of ACCEPT or REJECT Round 2b (only for papers that get a major revision in Round 1): Second-round submission deadline: September 2nd, 2020 (4 weeks for re-submission) Second-round review notification: September 30th, 2020 (4 weeks for reviews) Notification would be one of ACCEPT, REJECT, or MINOR REVISIONS Round 3 (only for papers that got a minor revision in Round 2b): Third-round submission deadline: October 14th, 2020 (2 weeks for re-submission) Third-round review notification: October 28th, 2020 (2 weeks for reviews) Notification would be one of ACCEPT or REJECT Topics of interest The special section is dedicated to parallel and distributed computing (PDC) techniques for AI/ML/DL. That includes both “PDC for AI/ML/DL”- and “AI/ML/DL for PDC”-oriented articles (please see the description above). Topics of interest include, but are not limited to: AI/ML/DL for PDC and PDC for AI/ML/DL Data parallelism and model parallelism Efficient hardware for AI, ML, and DL Hardware-efficient training and inference Performance modeling of AI/ML/DL applications Scalable optimization methods for AI/ML/DL Scalable hyper-parameter optimization Scalable neural architecture search Scalable IO for AI/ML/DL Systems, compilers, and languages for AI/ML/DL at scale Testing, debugging, and profiling AI/ML/DL applications Visualization for AI/ML/DL at scale Submission instructions Submissions to the special section will be received as TPDS regular papers (survey and comment-style papers are not allowed). Please check submission instructions including page limit, manuscript format, and submission guidance on the TPDS Author Information page. Please note that review versions of the papers are limited to 12 pages, and overlength page charges are only for the final versions of the papers. Similar to regular TPDS papers, you can extend previous conference papers and submit them to this special section. However, please note the following: All papers need to have sufficient new content (extension material). While the amount of new content is subjective and depends on the reviewer, we estimate that most reviewers expect close to 70% new material. Acceptance of the paper is based on the new content. Old content from previous conference papers is mainly to help reviewers understand the context. Old content should be clearly cited from the original source. Old content should be rephrased, and not copied verbatim, to avoid self-plagiarism. Submissions are *NOT* double blind. Authors can disclose their names, and they can freely cite their previous work without referring to it in a third-party fashion. Authors can submit papers till the deadline through ScholarOne. Once you start the submission process, in Step 1 of the process, you’ll be asked to pick a “Type” for the paper. Please pick “SS for Parallel and Distributed Computing Techniques for AL, ML and DL.” Co-editors Pavan Balaji (Argonne National Laboratory) Jidong Zhai (Tsinghua University) Min Si (Argonne National Laboratory) Committee members Adrián Castelló, Universitat Jaume I de Castello, Spain Amelie Chi Zhou, Shenzhen University, China Ang Li, Pacific Northwest National Laboratory, USA Bin Ren, College of William and Mary, USA Bronis de Supinski, Lawrence Livermore National Laboratory, USA Dandan Song, Beijing Institute of Technology, China David Liu, State University of New York at Binghamton, USA Feng Zhang, Renmin University, China Guangming Tan, Institute of Computing Technology, Chinese Academy of Sciences, China Huansong Fu, Amazon, USA Jintao Meng, Shenzhen Institutes of Advanced Technologies, China Jorge G. Barbosa, Universidade do Porto, Portugal Quan Chen, Shanghai Jiaotong University, China Shanjiang Tang, Tianjin University, China Sridutt Bhalachandra, Lawrence Berkeley National Laboratory, USA Stefano Markidi, KTH Royal Institute of Technology, Sweden Sunita Chandrasekaran, University of Delaware, USA Xiaoyi Lu, The Ohio State University, USA Zhiyi Huang, University of Otago, New Zealand
Last updated by Dou Sun in 2020-05-07
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