Concurrency and Computation: Practice and Experience

Aims & Scope

Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1.5
2024	1.50

SJR (SCImago Journal Rank)

Year	Value
2024	0.437

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	84

Journal Rank

Year	Value
2024	12920

Journal Citation Indicator

Year	Value
2024	5256

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science and Mathematics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers

  Citation: 1394

  Authors: Anton, Rajkumar

• Distributed computing in practice: the Condor experience

  Citation: 1221

  Authors: Douglas, Todd, Miron

• Scientific workflow management and the Kepler system

  Citation: 1160

  Authors: Bertram, Ilkay, Chad, Dan, Efrat, Matthew, Edward A., Jing, Yang

• GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing

  Citation: 868

  Authors: Rajkumar, Manzur

• StarPU: a unified platform for task scheduling on heterogeneous multicore architectures

  Citation: 813

  Authors: Cédric, Samuel, Raymond, Pierre‐André

• Economic models for resource management and scheduling in Grid computing

  Citation: 536

  Authors: Rajkumar, David, Jonathan, Heinz

• The LINPACK Benchmark: past, present and future

  Citation: 516

  Authors: Jack J., Piotr, Antoine

• FireWorks: a dynamic workflow system designed for high‐throughput applications

  Citation: 455

  Authors: Anubhav, Shyue Ping, Wei, Bharat, Xiaohui, Michael, Miriam, Guido, Gian‐Marco, Geoffroy, Daniel, Kristin A.

• Taverna: lessons in creating a workflow environment for the life sciences

  Citation: 407

  Authors: Tom, Mark, Matthew, M. Nedim, Justin, Kevin, Carole, Antoon, Duncan, Darren, Peter, Phillip, Matthew R., Martin, Robert, Anil, Chris

• HPCT<scp>OOLKIT</scp>: tools for performance analysis of optimized parallel programs

  Citation: 400

  Authors: L., S., M., M., G., J., N. R.

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