Journal Information
Journal of Parallel and Distributed Computing (JPDC)
Impact Factor:

Call For Papers
The Journal of Parallel and Distributed Computing (JPDC) is directed to researchers, scientists, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing. The goal of the journal is to publish in a timely manner original research, critical review articles, and relevant survey papers on the theory, design, implementation, evaluation, programming, and applications of parallel and/or distributed computing systems. The journal provides an effective forum for communication among researchers and practitioners from various scientific areas working in a wide variety of problem areas, sharing a fundamental common interest in improving the ability of parallel and distributed computer systems to solve increasing numbers of difficult and complex problems as quickly and as efficiently as possible.

The scope of the journal includes (but is not restricted to) the following topics as they relate to parallel and/or distributed computing:

• Theory of parallel and distributed computing
• Parallel algorithms and their implementation
• Innovative computer architectures
• Shared-memory multiprocessors
• Peer-to-peer systems
• Distributed sensor networks
• Pervasive computing
• Optical computing
• Software tools and environments
• Languages, compilers, and operating systems
• Fault-tolerant computing
• Applications and performance analysis
• Bioinformatics
• Cyber trust and security
• Parallel programming
• Grid computing
Last updated by Dou Sun in 2018-07-15
Special Issues
Special Issue on Scalable Algorithms and Architectures for Computational Life Sciences Applications
Submission Date: 2019-11-30

High-performance computing (HPC) has become an integral part of research and development in computational life sciences, which includes bioinformatics/computational biology, and medical and health informatics. The large size and complexity of biological data sets, coupled with the inherent complexity of the underlying biological problems, are driving the design of scalable parallel and distributed algorithms to solve biologically motivated problems. Algorithms in this field are being re-engineered or redesigned to be able to execute on a variety of parallel and distributed architectures, using a variety of parallel models and programming paradigms. The goal of this special issue is to provide a venue to present the latest research advances in the interdisciplinary area of computational life sciences. More specifically, we are interested in articles that describe recent advances in the design, development and application of scalable high-performance computing solutions to data- and compute-intensive problems arising from all areas of computational life sciences. In particular, the special issue invites authors to submit original, previously unpublished work that are squarely at the interface between the "pillars" of modern day computational life sciences and HPC. Extensions to original works previously published in workshops or conferences will also be considered and authors should clearly identify the additions in the work to justify consideration. Position and survey papers, and papers that describe empirical case studies and best practices suitable for practitioners in the field, are also welcome. Topics: For a submission to be considered, it should span at least one area from each of these two pillars. More specifically, we encourage submissions from all areas of biology that can benefit from HPC, and from all areas of HPC that need new development to address the class of computational problems that originate from biology. Areas of interest within computational life sciences include (but not limited to) Biological sequence analysis (genome assembly, long/short read data structures, read mapping, clustering, variant analysis, error correction, genome annotation) Computational structural biology (protein structure, RNA structure) Functional genomics (transcriptomics, RNAseq/microarrays, proteomics) Systems biology and networks (biological network analysis, gene regulatory networks, metabolomics, molecular pathways) Tools for integrated multi-omics and biological databases (network construction, modeling, link inference) Phylogeny (phylogenetic tree reconstruction, molecular evolution) Microbes and microbiomes (taxonomical binning, classification, clustering, annotation) Biomedical health analytics and biomedical imaging (electronic health records, precision medicine, image analysis) Biomedical literature mining (text mining, ontology, natural language processing) Computational epidemiology (infectious diseases, diffusion mechanisms) Phenomics and precision agriculture (IoT technologies, feature extraction) Areas of interest within HPC include (but are not limited to) Parallel and distributed algorithms (scalable machine learning, parallel graph/sequence analytics, combinatorial pattern matching, optimization, parallel data structures, compression) Data-intensive computing techniques (communication-avoiding/synchronization-reducing techniques, locality-preserving techniques, big data streaming techniques) Parallel architectures (multicore, manycore, CPU/GPU, FPGA, system-on-chip, hardware accelerators, energy-aware architectures, hardware/software co-design) Memory and storage technologies (processing-in-memory, NVRAM, burst buffers, 3D RAM, parallel/distributed I/O) Parallel programming models (libraries, domain specific languages, compiler/runtime systems) Scientific workflows (data management, data wrangling, automated workflows, productivity) Empirical evaluations (performance modeling, case-studies)
Last updated by Dou Sun in 2019-08-24
Special Issue on Blockchain-enabled Secure Communications in Smart Cities
Submission Date: 2019-12-01

Recent developments and success with Internet of Things (IoT) have led to a renewed interest in wireless sensor network (WSN) research, as WSN is a key component for the development in IoT. One particular ongoing research focus is on ensuring the security of the sensor network used in an IoT setting. For instance, the security and reliability in the dynamic update process of WSN is essential for communication security in smart cities. In addition, the security level of each node in WSN will influence the security of the whole network, and consequently IoT devices and its applications. In order to enhance the security level of WSN, a large number of protocols, architectures, system designs and algorithms have been designed to provide a higher security level for WSNs. Another more recent research and technological trend is Blockchain, partly due to Bitcoin. The characteristics of blockchain, such as decentralized operation, trustless management, and reliability, can be leveraged in WSN / IoT security. In other words, there are plenty of research opportunities in this area (i.e., Blockchain-enabled, -facilitated, or -based security solutions for WSN and IoT). Hence, this special issue seeks to solicit and publish current state-of-art advances, particularly those that bridge academia, industry and government, in a smart city context. Topics of particular interest include, but not limited to: Blockchain-enabled communication techniques Blockchain-based mobile sensing architectures, framework, and models Security and privacy issues in blockchain-based mobile sensing Secure communications in blockchain-enabled distributed sensing Trust management in blockchain communications Critical techniques in blockchain-enhanced communications for smart grid Blockchain-enhanced mobile sensing in smart city Blockchain-enhanced communications in 5G Blockchain-enhanced communications in cloud/fog/edge computing
Last updated by Dou Sun in 2019-08-24
Special Issue on Parallel Computing for Data Science
Submission Date: 2019-12-01

Data Science is a rapidly blossoming field of study with a highly multidisciplinary characteristic. Data Science can be defined as the convergence of Computer Science, programming, mathematical modeling, data analytics, academic expertise, traditional AI research and applying statistical techniques through scientific programming tools, streaming computing platforms, and linked data to extract new knowledge discovery through data patterns and provide new insights from distributed computing platforms. Data science often involves processing huge amounts of data, since the previously exponential growth in the speed of individual CPU has slowed down and the amount of data continues to increase, leveraging computers effectively must entail parallel computation. Therefore, it is critical to provide well scale performance with parallel computing techniques and apply traditional research with machine learning and deep learning algorithm to design novel patterns and architectures. It is also important to consider that, nowadays, technologies provide to researchers the ability to collect a huge amount of data, making possible to deal with problems that, only a few years ago, were out of their reach. Such a wealth of data, also called Big Data, requires the development of tools and methodologies with a high scalability degree and able to process virtually unbounded amounts of data within the Data Science scenario. The confluence of big data, massively powerful cloud computing platforms, and the need of businesses from all sectors to leverage their data repositories has created a high-growth environment and demand for parallel data science methodologies. In this perspective, Data Science needs to embrace data parallel computing techniques for efficient data analysis and analytics. Parallel algorithms for numerical processing, Parallel Data search, and other parallel computing algorithms can also facilitate advanced Data analytics and insights. The goal is to combine data and processes into a configurable, structured set of steps that implement automated computational solutions of an application with capabilities including provenance management, execution management and reporting tools, integration of distributed computation and data management technologies, ability to ingest local and remote scripts, and sensor management and data streaming interfaces. Furthermore, such Data Science workflows will provide in-depth treatment of the evolution of high performance, parallel computing architectures and how these architectures and computational ecosystems support Data Science. This special issue seeks high quality contributions in the field of Parallel and Distributed Computing for Data Science. Submissions will be judged on their originality, significance, clarity, relevance, and technical correctness. Topics of interests include: Data Analytics with High Performance Computing; Parallel Computing techniques for Machine Learning; Parallel Data Analysis; High performance data networking, management, and analytics; Parallel Algorithms for Data Science; Parallel programming methodologies for Data Science; Parallel Data Structures; Multicore Systems, Clusters and GPU for Data Science; Parallel and Distributed deep learning; Parallelization schema for learning algorithms;
Last updated by Dou Sun in 2019-08-24
Special Issue on Enabling Technologies for Energy Cloud
Submission Date: 2020-01-15

While distributed renewable energy resources continue to grow exponentially, and the grid becomes more digitized, the utilities' customer relationships and their operations get even more complex. The more evolution of dynamic demand-response, smart homes and billing, and social media applications will significantly change the way utilities interact with customers (e.g., utilities' customers have started to act as prosumers by generating their own power and sell it back to the national electricity supplier). The traditional age-old grid supports one-way flow of energy from centralized generation to end consumers, which is not the optimum operational model nowadays. In order to provide two-way energy flows (i.e., grid-consumer-grid and consumer-consumer), we need to move away from hub-and-spoke model toward a multidirectional giant network-of-networks called Energy Cloud. The ability to adapt to the large-scale changing environment of Energy Cloud allows consumers to connect essentially all electrical devices from the supply grid to the individual users into one giant energy cloud network. However, the optimization of Energy Cloud is a challenge and requires further research and development to significantly reduce the total energy cost and building a secure, and stable network. The aim of this Special Issue is to bring together researchers from academia, industry, and individuals working on relevant research areas to share their latest accomplishments and research findings within the research community. The special issue aims to cover topics that include, but not limited to, the below topics: Optimization of energy cloud Applied machine learning for energy cloud Artificial intelligence for network-of-networks Cyber-physical system of energy cloud Network-of-networks security Privacy in cyber-physical systems Two ways energy sharing Demand-side management Enabling technologies (e.g., social media) impact on energy eloud Energy cloud for off-grid smart homes Applications of energy cloud (network-of-networks) Critical infrastructure protection for energy cloud Sensor analytics in energy cloud
Last updated by Dou Sun in 2019-08-24
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bFUNInternational conference on Fun with Algorithms2012-01-232012-02-202012-06-04
BigSDMInternational Conference on Big Scientific Data Managemen2018-09-302018-10-202018-11-30
cbb1SECInternational Conference on ICT Systems Security and Privacy Protection2019-12-012020-02-122020-05-26
baHOT CHIPSSymposium on High Performance Chips2017-04-072017-05-012017-08-20
VLSIInternational Conference on VLSI2018-10-272018-11-102018-11-24
ba2ICCDInternational Conference on Computer Design2019-06-212019-09-092018-10-07
ca2ASP-DACAsia and South Pacific Design Automation Conference2019-07-122019-09-092020-01-13