PECS 2024

Workshop on Performance and Energy Efficiency in Concurrent and Distributed Systems

Call for Papers

Today, concurrent and distributed software and hardware systems take place in most computing applications, as well as they play a central role in recent computing paradigms like cloud, edge, and fog computing.

However, the increasing level of parallelism and heterogeneity has made concurrent and distributed systems even more complex to analyze and optimize. In particular, this concerns energy efficiency and performance aspects, which are known to be highly interrelated. Nonetheless, they play an important role, also because of the global electricity demand by the IT sector.

In concurrent and distributed systems, there are various factors that affect energy efficiency and performance in a complex way, such as the concurrent use of computing resources, distributed communication, the presence of (distributed) data dependencies and the need for synchronization of concurrent threads/processes. However, these and other issues may also offer new opportunities to be explored in the design of methods and tools to improve the energy efficiency and performance of these systems.

PECS aims to establish a venue for both academia and industry to discuss challenges and perspectives, and to explore methods, techniques and tools for energy efficiency and performance analysis and optimization in concurrent and distributed systems.

PECS calls for high-quality research papers on all aspects of energy efficiency, performance and their interrelations in concurrent and/or distributed computing systems, focusing on (but not limited to) the following topics:

This edition is colocated with the 33th ACM International Symposium on High-Performance Parallel and Distributed Computing (HPDC).

Important Dates

Paper Submission March 9, 2024
Paper Notification of Acceptance April 7, 2024
Camera-Ready Submission April 16, 2024
Workshop Day June 3, 2024

Paper Submission

See authors information.

2024 Workshop on Performance and Energy Efficiency in Concurrent and Distributed Systems. All rights reserved.