↑ Return to Conference

Topics

Permanent link to this article: http://europar2016.inria.fr/conference/topics/

1. Support Tools and Environments

Despite an impressive body of research, parallel and distributed programming remains a complex task prone to subtle software issues that can affect both the correctness and the performance of the application. This topic focuses on tools and techniques to help tackling that complexity. We solicit contributions on tools and environments that address any of the …

View page »

2. Performance and Power Modeling, Prediction and Evaluation

In recent years, a range of novel methods and tools have been developed for the evaluation, design, and modeling of parallel and distributed systems and applications. At the same time, the term ‘performance’ has broadened to also include scalability and energy efficiency, and touching reliability and robustness in addition to the classic resource-oriented notions. The …

View page »

3. Scheduling and Load Balancing

As parallelism now permeates all levels of modern computer systems, it opens up new opportunities for improving application performance but, at the same time, it also increases the complexity of the resource management challenge. In this environment, the importance of scheduling and load balancing as key topics in parallel computing continues to grow. In addition …

View page »

4. High Performance Architectures and Compilers

This topic deals with architecture design, languages, and compilation for parallel high performance systems. The areas of interest range from microprocessors to large-scale parallel machines (including multi-/many-core, possibly heterogeneous, architectures); from general-purpose to specialized hardware platforms (e.g., graphic coprocessors, low-power embedded systems); and from architecture design to compiler technology and language design. On the compilation …

View page »

5. Parallel and Distributed Data Management and Analytics

Many areas of science, industry, and commerce are producing extreme-scale data that must be processed—stored, managed, analyzed—in order to extract useful knowledge. This topic seeks papers in all aspects of distributed and parallel data management and data analysis. For example, HPC in situ data analytics, cloud and grid data-intensive processing, parallel storage systems, and scalable …

View page »

6. Cluster and Cloud Computing

The success of Cloud Computing solutions such as the ones provided by Amazon or Google has driven the advent of the Utility Computing (UC) paradigm. The use of massive storage and computing resources accessible remotely in a seamless way has become essential for many applications in various areas. Cloud Computing evolved from Cluster Computing where …

View page »

7. Distributed Systems and Algorithms

Parallel computing is heavily dependent on and interacting with the developments and challenges concerning distributed systems, such as load balancing, asynchrony, failures, malicious and selfish behavior, long latencies, network partitions, disconnected operations, distributed computing models and concurrent data structures, and heterogeneity. This track of Euro-Par provides a forum for both theoretical and practical research, of …

View page »

8. Parallel and Distributed Programming, Interfaces, Languages

Parallel and distributed applications requires adequate programming abstractions and models, efficient design tools, parallelization techniques and practices. This topic is open for presentations of new results and practical experience in this domain. efficient and effective parallel languages, interfaces, libraries and frameworks, as well as solid practical and experimental validation. It emphasizes research on high-performance, correct, …

View page »

9. Multicore and Manycore Parallelism

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 …

View page »

10. Theory and Algorithms for Parallel Computation and Networking

Parallel computing is everywhere, on smartphones, laptops; at online shopping sites, universities, computing centers; behind the search engines. Efficiency and productivity at these scales and contexts are only possible by scalable parallel algorithms using efficient communication schemes, routing and networks. Theoretical tools enabling scalability, modeling and understanding parallel algorithms, and data structures for exploiting parallelism …

View page »

11. Parallel Numerical Methods and Applications

The rush to higher performance computers is driven by the need for large-scale simulations in science and engineering. This need goes in hand with the demand for highly scalable numerical methods and algorithms that allow for the use of nowadays massively parallel systems or modern architectures (as many-core and hybrid platforms) and deliver the expected …

View page »

12. Accelerator Computing

Hardware accelerators of various kinds offer a potential for achieving massive performance in applications that can leverage their high degree of parallelism and customization. Examples include graphics processors (GPUs), manycore” co-processors, as well as more custom devices, customizable FPGA-based systems, and streaming data-flow architectures. The research challenge for this topic is to explore new avenues …

View page »