会議情報
LCPC 2020: International Workshop on Languages and Compilers for Parallel Computing
https://lcpc2020.cs.stonybrook.edu/
提出日:
2020-08-13 Extended
通知日:
2020-09-04
会議日:
2020-10-14
場所:
New York City, USA
年:
33
QUALIS: b4   閲覧: 8675   追跡: 4   出席: 1

場所
論文募集
Since its inception in 1988, the Workshop on Languages and Compilers for Parallel Computing (LCPC) has been a leading venue for cutting-edge research on all aspects of parallel programming systems -- from parallel programming models, languages, compilers, runtimes and tools, to results related to new parallel applications or systems. Its scope is particularly broad: it encompasses foundational results, as well as practical experience reports and bold new ideas for future systems.

In addition to its traditional themes in parallel programming systems, relevant topics include advances in programming systems for heterogeneous and reconfigurable computing, mobile computing, IoT and cloud computing, and papers in data analytics, machine learning and cognitive computing. Along with research in new computing domains such as analog, neuromorphic and quantum computing, LCPC particularly encourages submissions in areas that are enabled or enhanced by parallelism and work that combines scientific computing with data analytics and machine learning.

Specific topics of interest for LCPC 2020 include, but are not limited to:

    Compilers for parallel computing, including heterogeneous systems
    Static, dynamic and adaptive optimization of parallel programs
    Just-in-time compiling, including for scripting languages
    Parallel programming models and languages for traditional and emerging architectures
    Languages and tools for programming quantum computing systems
    Formal methods in analysis, verification, and software engineering of parallel programs
    Intermediate representations for general-purpose and domain-specific compilation
    Parallel runtime systems and libraries
    Performance analysis and debugging tools for parallel programs
    Parallel algorithms and concurrent data structures
    Parallel applications for Big Data, Machine Learning, Embedded Systems, Bio, IoT
    Fault tolerance in parallel systems
    Parallel communication idioms and libraries (e.g. MPI, OpenSHMEM)
最終更新 Dou Sun 2020-08-10
合格率
時間提出受け入れ受け入れ(%)
1999482756.3%
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関連仕訳帳
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