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| Parallel Computing (journal) | |
|---|---|
| Title | Parallel Computing |
| Discipline | Computer science |
| Abbreviation | Parallel Comput. |
| Publisher | Elsevier |
| Country | Netherlands |
| History | 1974–present |
| Frequency | 12/year |
| Issn | 0167-8191 |
Parallel Computing (journal) is a peer-reviewed scientific journal covering research on high-performance computing, parallel algorithms, and distributed systems. The journal publishes original research articles, surveys, and special issues on topics ranging from hardware architectures to software frameworks and performance analysis. Its audience includes researchers affiliated with institutions such as Massachusetts Institute of Technology, Stanford University, Carnegie Mellon University, University of California, Berkeley, and ETH Zurich.
Established in 1974, the journal emerged during the growth of parallel architectures exemplified by projects at Bell Labs, IBM Research, Los Alamos National Laboratory, Sandia National Laboratories, and Lawrence Livermore National Laboratory. Early editorial leadership drew contributors from University of Illinois Urbana-Champaign, University of Cambridge, University of Edinburgh, University of Tokyo, and Imperial College London. The journal documented milestones alongside machines like the Cray-1, Thinking Machines CM-2, Connection Machine, Intel Paragon, and Fujitsu Gyoukou, and paralleled conferences such as ACM/IEEE Supercomputing Conference, International Conference on Parallel Processing, IEEE International Parallel and Distributed Processing Symposium, Euro-Par, and PDP. Over decades, it reflected shifts driven by initiatives at DARPA, European Commission, National Science Foundation, Japan Science and Technology Agency, and CERN.
Parallel Computing addresses research on architectures like GPU, FPGA, RISC-V, IBM POWER, and ARM-based manycore systems; programming models such as MPI (Message Passing Interface), OpenMP, CUDA, OpenCL, Chapel (programming language), and X10 (programming language); compilers and tools from groups at Google, Microsoft Research, NVIDIA Research, AMD Research, and Intel Labs; performance modeling referencing benchmarks like LINPACK, SPEC, STREAM benchmark, Graph500, and HPCC; and applications spanning projects at NASA, European Space Agency, Human Genome Project, Large Hadron Collider, and Climate Research Unit. The journal also covers scheduling and resource management research related to Kubernetes, Slurm Workload Manager, Hadoop, Spark, and Apache Mesos.
The editorial board has historically included scholars affiliated with Princeton University, Harvard University, Yale University, Columbia University, University of Oxford, University of Cambridge, Technical University of Munich, KTH Royal Institute of Technology, and Peking University. Editors coordinate peer review with reviewers from venues such as ACM SIGPLAN, ACM SIGARCH, IEEE Computer Society, SIAM, and national academies including National Academy of Sciences (United States), Royal Society, and Chinese Academy of Sciences. The journal employs single- or double-blind review processes in line with standards used by Journal of Parallel and Distributed Computing, IEEE Transactions on Parallel and Distributed Systems, and Concurrency and Computation: Practice and Experience.
Parallel Computing is indexed in major services including Science Citation Index, Scopus, Inspec, DBLP, Google Scholar, MathSciNet, and CrossRef. Abstracting and indexing enable visibility alongside titles in databases managed by Clarivate, Elsevier, IEEE Xplore, ACM Digital Library, and SpringerLink and support metrics used by organizations such as Eigenfactor, SCImago, and National Science Foundation reporting.
Published by Elsevier, the journal follows a subscription-based model with options for open access via article processing charges consistent with Elsevier policies. Authors affiliated with institutions like University of Michigan, University of Toronto, McGill University, Australian National University, and Tsinghua University may use transformative agreements negotiated by consortia including Jisc, Big Ten Academic Alliance, CRUE and VSNU. The journal participates in initiatives such as Plan S, OpenAIRE, and retains archival records in repositories like Portico and CLOCKSS.
Parallel Computing has influenced research cited in works from laboratories and institutions including Los Alamos National Laboratory, Argonne National Laboratory, Oak Ridge National Laboratory, Rutherford Appleton Laboratory, and Lawrence Berkeley National Laboratory. Its articles contribute to fields represented at conferences like SC Conference, ICPP, PACT, HiPC, and ISPASS, and are referenced by standards bodies such as IEEE Standards Association and ISO. Bibliometric indicators from Journal Citation Reports and Scopus have been used by university departments at Cornell University, University of Illinois, Ohio State University, University of Waterloo, and National University of Singapore for evaluation.
The journal has published influential papers on topics advancing work on parallel algorithms (e.g., developments related to PRAM model, distributed hash tables), performance studies of systems including Blue Gene and Titan, and software contributions linked to PETSc, Trilinos, HPCG benchmark, and METIS. Special issues have focused on themes coordinated with events such as International Supercomputing Conference, Euro-Par, ACM Symposium on Principles of Distributed Computing, and collaborations with projects funded by Horizon 2020 and European Research Council.
Category:Computer science journals