Topics
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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 …
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 …
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 …
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 …
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 …
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 …
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 …
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, …
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 …
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 …
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 …
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 …
Euro-Par 2016, 22nd International European Conference on Parallel and Distributed Computing
