2001 |
Parallelizing a Dense Matching Region Growing Algorithm for an Image Interpolation Application. Journal Article Proceedings of the 5th International Conference on Parallel and Distributed Techniques (PDPTA), 2001. |
0000 |
An efficient approach to solve very large dense linear systems with verified computing on clusters Journal Article doi Numerical Linear Algebra with Applications, 22 (2), pp. 299-316, 0000. |
Parallel Applications Modelling Group
GMAP is a research group at Pontifical Catholic University of Rio Grande do Sul (PUCRS). Historically, the group has carried out several researches about modeling and adapting real world and robust applications from different domains (physics, mathematics, geology, image processing, biology, aerospace, and many others) to run efficiently in High-Performance Computing (HPC) architectures. GMAP researchers are also working on ways to design and provide new parallelism abstractions for the next generation computer architectures and algorithms. The goal is to simplify parallel programming for application programmers, providing new domain-specific languages, libraries, and frameworks.
Research Lines
High-level and Structured Parallelism Abstractions
The research line HSPA (High-level and Structured Parallelism Abstractions) aims to create programming interfaces for the user/programmer who is not able in dealing with parallel programming paradigm. The idea is to offer a higher level of abstraction, where the performance of applications is not compromised. The interfaces developed in this research line go toward specific domains that can later extend to other areas. The scope of the study is broad as regards the use of technologies for the development of the interface and parallelism.
Parallel Application Modeling
The Parallel Applications Modelling research line is centered on the study of high performance solutions for problems originated in other areas of knowledge (Biology, Geology, Physics, etc). The development of high performance solutions for problems requires a grasp in many levels of specialized knowledge. Besides an analysis of the algorithmic complexity of the problem to be parallelized, it is also necessary to have knowledge about the developing environments and program tests, parallel programming techniques, performance evaluations and high performance architectures.
Energy Efficiency in High Performance Environments
This research line aims to study and propose software solutions to reduce energy consumption in high-performance environments. Target environments of research developed in this line are: clusters, grids, and clouds. We can highlight as examples of proposed solutions the creation of scheduling policies as well as the use of hardware techniques, aimed to reduce energy consumption in running large applications.
Team

Prof. Dr. Luiz Gustavo Leão Fernandes
Group Head
He works with big data and stream processing, application modeling for high performance computing, cloud computing, performance evaluation, energy efficiency and verified computing for high performance computing, scientific computing, and methodologies, languages, and libraries for parallel programming.

Prof. Dr. Dalvan Griebler
Research Coordinator
He works with high-level parallelism abstractions, compiler design, source-to-source transformations, high-performance computing, applications, architectures, parallel programming tools, open source cloud computing platforms, and virtualization technologies.
Last Papers
Projects
GMAP is involved in various research projects in many areas of computing. To see a list of all completed or ongoing projects, please visit the Projects page.
Last News
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