HLPP 2016
9th International Symposium on High-Level Parallel Programming and Applications

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Aims and scope of HLPP
As processor and system manufacturers increase the amount of both inter- and intra-chip parallelism it becomes crucial to provide the software industry with high-level, clean and efficient tools for parallel programming. Parallel and distributed programming methodologies are currently dominated by low-level techniques such as send/receive message passing, or equivalently unstructured shared memory mechanisms. Higher-level, structured approaches offer many possible advantages and have a key role to play in the scalable exploitation of ubiquitous parallelism. 
Since 2001 the HLPP series of workshops/symposia has been a forum for researchers developing state-of-the-art concepts, tools and applications for high-level parallel programming. The general emphasis is on software quality, programming productivity and high-level performance models. The 9th Symposium on High-Level Parallel Programming and Applications will be held July 4-5 in Muenster, Germany. 

HLPP 2016 invites papers on all topics in high-level parallel programming, its tools and applications including, but not limited to, the following aspects:

  • High-level programming, performance models (BSP, CGM, LogP, MPM, etc.) and tools
  • Declarative parallel programming methodologies based on functional, logical, data-flow, and other paradigms
  • Algorithmic skeletons, patterns, etc. and constructive methods
  • High-level parallelism in programming languages and libraries (e.g, Haskell, Scala, etc.): semantics and implementation
  • Verification of declarative parallel and distributed programs
  • Efficient code generation, auto-tuning and optimization for parallel programming
  • Model-driven software engineering for parallel systems
  • Domain-specific languages: design, implementation and applications
  • High-level programming models for heterogeneous/hierarchical platforms with accelerators, e.g., GPU, Xeon Phi, etc.
  • High-level parallel methods for large structured and semi-structured datasets
  • Applications of parallel systems using high-level languages and tools
  • Teaching experience with high-level tools and methods

Sponsored by Huawei Technologies France

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