HLPP 2015 : The 8th International Symposium on High-level Parallel Programming and Applications

HLPP 2015 : The 8th International Symposium on High-level Parallel Programming and Applications

Link: http://www.di.unipi.it/HLPP2015/
When Jul 2, 2015 - Jul 3, 2015
Where Pisa, Italy
Submission Deadline Apr 24, 2015
Notification Due May 28, 2015
Final Version Due Jun 21, 2015

Call For Papers

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 8th Symposium on High-Level Parallel Programming and Applications will be held July 2-3 in the historic center of Pisa. 


HLPP 2015 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 and performance models (BSP, CGM, LogP, MPM, etc.) and tools 
- Declarative parallel programming methodologies 
- Algorithmic skeletons and constructive methods 
- Declarative parallel programming languages and libraries: semantics and implementation 
- Verification of declarative parallel and distributed programs 
- Software synthesis, automatic code generation for parallel programming 
- Model-driven software engineering with parallel programs 
- High-level programming models for heterogeneous/hierarchical platforms 
- 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 


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