Dates: August 31 – September 1, 2017 | Registration to be announced
Location: Santiago de Compostela, Spain

Euro-Par 2017

Euro-Par is the prime European conference covering all aspects of parallel and distributed processing, ranging from theory to practice, from small to the largest parallel and distributed systems and infrastructures, from fundamental computational problems to full-fledged applications, from architecture, compiler, language and interface design and implementation, to tools, support infrastructures, and application performance aspects.

Euro-Par’s unique organization into topics provides an excellent forum for focused technical discussion, as well as interaction with a large, broad and diverse audience.

Conference topics

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 to tackle that complexity.

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.

Scheduling and Load Balancing

New computer systems supply an opportunity to improve the performance and the energy consumption of the applications by the exploitation of several parallelism levels. Heterogeneity and complexity are the main characteristics of modern architectures. Thereby, the optimal exploitation of modern platforms becomes a challenge. The scheduling and load balancing techniques are relevant topics for the optimal exploitation of modern computers in terms of performance, energy consumption, the cost of using resources and so on.

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.

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.

Cluster and Cloud Computing

The success of Cloud Computing has driven the advent of the Utility Computing (UC) paradigm. Cloud Computing is not a concept anymore, but a reality with many providers around the world. 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 for the latter dedicated resources are usually involved.

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.

Parallel and Distributed Programming, Interfaces, and Languages

Parallel and distributed applications require 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, portable, and scalable parallel programs via adequate parallel and distributed programming model, interface and language support.

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 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.

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.

Parallel Numerical Methods and Applications

The need for high-performance computations is driven by the need for large-scale simulations in science and engineering, finance, life sciences etc. This demand goes hand in hand with the necessity to develop highly scalable numerical methods and algorithms that are able to efficiently exploit modern computer architectures. The scalability of these algorithms and methods and their suitability to efficiently utilize the available high performance, but in general heterogeneous, computer resources, is a key point to improve the performance of Computational Science and Engineering applications.

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 coprocessors, as well as more customizable devices, such as FPGA-based systems, and streaming dataflow architectures.

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