LCPC 2020: Workshop on Languages and Compilers for Parallel Computing

Страна: США

Город: Stony Brook

Тезисы до: 30.07.2020

Даты: 14.10.20 — 16.10.20

Область наук: Физико-математические;

Е-мейл Оргкомитета:

Организаторы: Department of Computer Science, Stony Brook University


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)

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