Journal Information
Parallel Computing (PARCO)
http://www.journals.elsevier.com/parallel-computing/
Impact Factor:
1.281
Publisher:
Elsevier
ISSN:
0167-8191
Viewed:
9482
Tracked:
28

Call For Papers
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.

Parallel Computing features original research work, tutorial and review articles as well as novel or illustrative accounts of application experience with (and techniques for) the use of parallel computers. Contributions can cover:

• 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
• Application 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
Last updated by Dou Sun in 2019-11-24
Special Issues
Special Issue on Hardware/Software Co-design for Sparse and Irregular Applications
Submission Date: 2021-03-01

Many well established or emerging high-performance computing applications are said to exhibit irregular behaviors, because they present fine-grained unpredictable memory access patterns, irregularity in the contro control structures, and/or network communication of variable sizes. They operate on ever growing, not rigidly structured, data sets, thus they have significant degree of parallelism. However, for these same reasons they may also be synchronization intensive, and very difficult to load balance on parallel architectures. Irregular applications pertain both to well established and emerging fields, such as Computer Aided Design (CAD), bioinformatics, data mining, machine learning on sparse data structures (e.g. Graph Neural Networks), analysis of social, transportation, communication and other types of networks, and computer security. Additionally, the newest high-performance computing applications really are converging towards a mix of conventional scientific simulation, machine learning and data analytics, hence combining regular with irregular irregular phases. Current high-performance systems rely on data locality, regular computations, and easily partitionable data sets to exploit parallelism and increase performance. This happens both at the hardware level, where general purpose and specialized processors (e.g., GPUs, or domain-specific accelerators mostly focused to machine learning) aim at reaching high flop-rates with vector or tensor units for computation on dense data structures and at reducing latencies with large caches and deep memory hierarchies, and at the software level, where most of the runtimes, libraries, and/or algorithms mostly exploit data partitioning and data movement reduction. However, the current solutions that focus on leveraging these features do not cope well with the more complex sparse data structures and behaviors of irregular applications. Addressing the issues of irregular applications on current and future system architectures will become critical to solve the scientific challenges of the next few years, and will require a collaborative codesign process of both the hardware and the software. This special issue seeks works that explore hardware/software co-design approaches for developing and optimizing execution of irregular applications on high-performance systems, at all levels of the stack: micro- and systemarchitecture, network, languages, libraries, runtimes, compilers, analysis, algorithms. Specifically, this issue aims at collecting those novel research solutions that connect domain-specific architectures and emerging high-performance applications that exhibit irregular behaviors. Topics of interest, of both theoretical and practical significance, include but are not limited to: Micro- and System-architectures Network and memory architectures Manycore, hybrid, and custom architectures (Tensor architectures, GPUs, FPGAs, near-memory designs) Heterogeneous approaches and methods for exploiting domain-specific systems Modeling, evaluation and characterization of domain-specific architectures for memory intensive and irregular applications also from theoretical perspective Innovative algorithmic techniques Parallelization techniques for sparse data structures Languages and programming models Library and runtime support Compiler and analysis techniques Case studies of irregular applications (e.g. Knowledge Graphs, Machine Learning on sparse data-structures, Data Mining, Security, Bioinformatics) Support for irregular applications on novel computing architectures (quantum, neuromorphic)
Last updated by Dou Sun in 2020-08-24
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CCFCOREQUALISShortFull NameSubmissionNotificationConference
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