Topic 9. Multicore and Manycore Methods and Tools

Modern homogeneous and heterogeneous multicore and manycore architectures are now part of the high-end, embedded, and mainstream computing scene and can offer impressive performance for many applications. This architecture trend has been driven by the need to reduce power consumption, increase processor utilization, and deal with the memory-processor speed gap. However, the complexity of these new architectures has created several programming challenges, and achieving performance on these systems is often a difficult task. This topic seeks to explore productive programming of multi- and manycore systems, as well as stand-alone systems with large numbers of cores like GPUs and various types of accelerators; this can also include hybrid and heterogeneous systems with different types of multicore processors. It focuses on novel research and solutions in the form of programming models, algorithms, languages, compilers, libraries, runtime and analysis tools to increase the programmability of multicore, many-core, and heterogeneous systems, in the context of general-purpose, high-performance, and embedded parallel computing.

Focus

• Programming techniques, models, frameworks and languages
• Advances in algorithms and data-structures
• Lock-free algorithms, transactional-memories
• Compiler optimizations and techniques
• Libraries and runtime systems
• Tools for discovering and understanding parallelism
• Performance and power trade-offs and scalability
• Innovative applications and case studies
• Hardware support for programming models and runtime systems
• Models, methods and tools for innovative many-core architectures

Chairs

Global chair

Christoph Kessler, Linköping University, Sweden

Local chair

Marco Danelutto, University of Pisa, Italy

Chair

Rudolf Eigenmann, University of Delaware, USA

Chair

Arturo González Escribano, Universidad de Valladolid, Spain

Chair

Kevin Hammond, University of St. Andrews, UK

Chair

Jesper Träff, TU Wien, Austria

Chair

Ana Lucia Varbanescu, University of Amsterdam, The Netherlands