F. Campeotto, A. Dovier, and E. Pontelli.
A Declarative Concurrent System for Protein Structure Prediction on GPU.
Journal of Experimental & Theoretical Artificial Intelligence (JETAI).
On line since february 2015
This paper provides a novel perspective in the Protein Structure Prediction (PSP) problem. The PSP problem focuses on determining putative 3D structures of a protein starting from its primary sequence. The proposed approach relies on a Multi-Agent System (MAS) perspective, where concurrent agents explore the folding of different parts of a protein. The strength of the approach lies in the agents’ ability to apply different types of knowledge, expressed in the form of declarative constraints, to prune the search space of folding alternatives. The paper makes also an important contribution in demonstrating the suitability of a General-Purpose Graphical Processing Unit (GPGPU) approach to implement such MAS infrastructure, with significant performance improvements over the sequential implementation and other methods.
Cliccando sulla foto (item 18) c’è una lezione on-line sul ruolo dei giochi in AI (realizzata per il progetto flash forward 2 organizzato dalle Università di Udine e Trieste).
Agostino Dovier @ECAI2014. Pictures by Enrico Pontelli
The paper A GPU Implementation of Large Neighborhood Search for Solving Constraint Optimization Problems by F. Campeotto, A. Dovier, F. Fioretto, and E. Pontelli will be presented (as full paper) at ECAI 2014
Constraint programming has gained prominence as an effective and declarative paradigm for modeling and solving complex combinatorial problems. Techniques based on local search have proved practical to solve real-world problems, providing a good compromise between optimality and efficiency. In spite of the natural presence of concurrency, there has been relatively limited effort to use novel massively parallel architectures, such as those found in modern Graphical Processing Units (GPUs), to speedup local search techniques in constraint programming. This paper describes a novel framework which exploits parallelism from a popular local search method (the Large Neighborhood Search method), using GPUs.
Members of our favourite lab will be involved in the exciting SMART GRIDS project just funded by NSF. Congratulations to Enrico.
For details read: http://newscenter.nmsu.edu/10029/
The paper: Exploring the Use of GPUs in Constraint Solving, by F. Campeotto, A. Dal Palù, A. Dovier, F. Fioretto, and E. Pontelli is accepted in PADL 2014 conference http://www.ist.unomaha.edu/padl2014/ (Springer), San Diego, CA, January 22-23, 2014.
This paper presents an experimental study aimed at assessing the feasibility of parallelizing constraint propagation|with particu lar focus on arc-consistency using Graphical Processing Units (GPUs). GPUs support a form of data parallelism that appears to be suitable to the type of processing required to cycle through constraints and domain values during consistency checking and propagation. The paper illustrates an implementation of a constraint solver capable of hybrid propagations (i.e., alternating CPU and GPU), and demonstrates the potential for competitiveness against sequential implementations.
CLPLAB is involved in two new projects with local companies:
- Sistema HW/SW di raccolta dati real-time in ambito automotive per soluzioni di mobilità sostenibile (partner industriale: Qualibit srl)
- Sistema di pianificazione adattiva per soluzioni di mobilità sostenibile (partner industriale TELLUS)
Now, let’s work.
The paper: Protein Structure Prediction on GPU: a Declarative Approach in a Multi-agent Framework, by F. Campeotto, A. Dovier, and E. Pontelli is accepted in ICPP conference (IEEE) http://icpp2013.ens-lyon.fr/ Lyon (FR), October 2013.
This paper provides a novel perspective in the Protein Structure Prediction (PSP) problem. The PSP problem focuses on determining putative 3D structures of a protein starting from its primary sequence. The proposed approach relies on a multi-agents approach, where concurrent agents explore the folding of different parts of a protein. The strength of the approach lies in the agents’ ability to apply different types of knowledge (expressed in the form of declarative constraints to prune the local space of folding alternatives. The paper demonstrates the suitability of a GPU approach to implement such multi-agent infrastructure, with significant improvements in speed and quality of solutions w.r.t. other methods (e.g., based on fragments assembly approaches).
Autonomous agents coordination: Action languages meet CLP(FD) and Linda
The paper presents a knowledge representation formalism, in the form of a high-level Action Description Language (ADL) for multi-agent systems, where autonomous agents reason and act in a shared environment. Agents are autonomously pursuing individual goals, but are capable of interacting through a shared knowledge repository. In their interactions through shared portions of the world, the agents deal with problems of synchronization and concurrency; the action language allows the description of strategies to ensure a consistent global execution of the agents’ autonomously derived plans. A distributed planning problem is formalized by providing the declarative specifications of the portion of the problem pertaining to a single agent. Each of these specifications is executable by a stand-alone CLP-based planner. The coordination among agents exploits a Linda infrastructure. The proposal is validated in a prototype implementation developed in SICStus Prolog.
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