10th International Symposium on High-Level Parallel Programming and Applications

July 10-11th 2017, Valladolid (Spain)

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 10th Symposium on High-Level Parallel Programming and Applications will be held July 10-11th in the cultural city of Valladolid, Spain.

Topics

HLPP 2017 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

Sponsors