![](data:image/svg+xml;charset=utf-8,<svg height="683" width="1024" xmlns="http://www.w3.org/2000/svg" version="1.1"/>)
Between March 1st and 3rd, the 31st edition of the Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2023) took place in Naples, Italy. This year, the 31st PDP edition is part of the promotional activities of the ADMIRE project, which will also be an exhibitor in the Conference Demo Area along with E4 Computer Engineering. The ADMIRE project aims to avoid congestion and balance computational performance with storage performance when processing extremely large datasets. The main objective of the ADMIRE project is to establish this control by creating an active input and output stack that dynamically adjusts computing and storage requirements through intelligent global coordination, computation and input/output flexibility, and scheduling of storage resources at all levels of the storage hierarchy.
This year, the paper entitled “Revisiting self-adaptation for efficient decision-making at run-time in parallel executions” won the Best ADMIRE project paper. This work was developed by Dr. Adriano Vogel under the advice of professor Dr. Dalvan Griebler and professor Dr. Luiz Gustavo Fernandes from PUCRS, and professor Dr. Marco Danelutto from UNIPI.
Below is the abstract of the paper:
“Self-adaptation is a potential alternative to provide a higher level of autonomic abstractions and run-time responsiveness in parallel executions. However, the recurrent problem is that self-adaptation is still limited in flexibility and efficiency. For instance, there is a lack of mechanisms to apply adaptation actions and efficient decision-making strategies to decide which configurations should be conveniently enforced at run-time. In this work, we are interested in providing and evaluating potential abstractions achievable with self-adaptation transparently managing parallel executions. Therefore, we provide a new mechanism to support self-adaptation in applications with multiple parallel stages executed in multi-cores. Moreover, we reproduce, reimplement, and evaluate in our scenario an existing decision-making strategy. The observations from the results show that the proposed mechanism for self-adaptation can provide new parallelism abstractions and autonomous responsiveness at run-time. On the other hand, there is a need for more accurate decision-making strategies to enable efficient executions of applications in resource-constrained scenarios like multi-cores.”
See the complete list of awards on the official website.
By: Gabriella Andrade