Modern homogeneous and heterogeneous multicore and manycore architectures are now part of the high-end 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 parallel computing, including HPC.
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 scalability
- Innovative applications and case studies
- Hardware support for programming models and runtime systems
Committee
Chair: Michela Taufer (University of Delaware, USA)
Local chair: Renaud Lachaize (University Grenoble Alpes, France)
Michela Becchi (University of Missouri, USA)
Sunita Chandrasekaran (University of Delaware, USA)
Anne Elster (NTNU and Univ. of Texas at Austin, USA)
Naoya Maruyama (RIKEN, Japan)
Dimitrios Nikolopoulos (Queen’s University of Belfast, Ireland)
Sabela Ramos Garea (ETH Zurich, Switzerland)
Guido Juckeland (Helmholtz Zentrum Dresden Rossendorf, Germany)
Graham Lopez (Oak Ridge National Laboratory, USA)
Vania Marangozova-Martin (University Grenoble Alpes, France)
