Advances in Parallel Programming: Languages, Models and Algorithms
摘要截稿:
全文截稿: 2019-05-01
影响因子: 2.296
期刊难度:
CCF分类: B类
中科院JCR分区:
• 大类 : 计算机科学 - 3区
• 小类 : 计算机:理论方法 - 3区
Overview
Parallelism becomes ubiquitous. It is available in many ways, including multicore processors, GPUs, co-processors, and many-core processors. These hardware emerging trends impose demanding challenges on modern parallel programming languages, models and algorithms. Hence, there is a need for high-level parallel programming paradigms that address all these forms of hardware parallelism in a manner that is abstract enough, user-friendly, ease of learning, scalable and performance portable. This special issue aims to present new developments in programming homogenous and heterogeneous parallel systems. It focuses on language-based parallel programming such as OpenMP, Python, Microsoft .NET parallel extensions (TPL and PPL), Java parallel extensions, PGAS languages, and GPGPU language-based programming models such as CUDA, OpenCL and OpenACC. Contributions on other high-level programming models and supportive environments for parallel and distributed systems are equally welcome.
This special issue seeks high quality contributions in the field of High-level Parallel Programming. The authors of the best articles of PPAM-WLPP 2019 workshop selected by the program committee and the guest editors will be invited to submit extended versions of their work to this special issue. The submission should contain at least 30% of new content. Papers not presented at the workshop but that contribute to the High-level Parallel Programming area are also welcome. Submissions will be reviewed by the program committee members of the PPAM-WLPP 2019 workshop and other reviewers.
Topics of interests include:
Language and library implementations
Proposals for, and evaluation of, language extensions
Applications and algorithms development experiences
Comparisons between programming models
Benchmark suites and performance studies
Debuggers and performance analysis tools
Compiler implementation and optimization
Optimization techniques
Performance portability
Hybrid models (OpenMP-MPI etc.)
Parallel languages and models for heterogeneous systems