Projects

HPX

The goal for HPX is to create a high quality, freely available, open source implementation of the ParalleX model for conventional systems, such as classic Linux based Beowulf clusters or multi-socket highly parallel SMP nodes. At the same time, we want to have a very modular and well designed ... [More] runtime system architecture which would allow us to port our implementation onto new computer system architectures. We use real world applications to drive the development of the runtime system, coining out required functionalities and converging onto an stable API which will provide a smooth migration path for developers. The API exposed by HPX is modelled after the interfaces defined by the C++11 ISO standard and adheres to the programming guidelines used by the Boost collection of C++ libraries. [Less]

Charm++

A portable adaptive runtime system for parallel applications. Application developers create an object-based decomposition of the problem of interest, and the runtime system manages issues of communication, mapping, load balancing, fault tolerance, and more. Sequential code implementing the ... [More] methods of these parallel objects is written in C++. Calls to libraries in C++, C, and Fortran are common and straightforward. Charm++ is portable across individual workstations, clusters, accelerators (Cell SPEs, GPUs), and supercomputers such as those sold by IBM (Blue Gene, POWER) and Cray (XT3/4/5/6 and XE6). Applications based on Charm++ are used on at least 5 of the 20 most powerful computers in the world. [Less]

mmulfpuna

Matrix Multiplication. FPUNA @ 2008Matrix Multiplication algorithms performance comparison. We compare diferent versions of algorithm including: - Sequential simple. - 1D y 2D parallel algorithm implemented with pthreads. - ... The implementations are all made on C/C++, and correspond to a ... [More] Practice Work for the "Design of Parallel Algorithms Course" from FPUNA. Matrix Multiplication parallel algorithm performance comparison. As a second part of the project, we compare four diferent parallel algorithms for solve the matrix multiplication of two square dense matrices. The algorithms compared are: - 2D with block-cyclic distribution. - Cannon - DNS - Diagonal 2D We use OpenMPI for implementation. [Less]

PGB-VCP

Thesis project, graph(vertex) coloring problem with genetic programming binary

octopus-framework

Octopus is a parallel, distributed 3D framework for crafting scientific simulations that use adaptive mesh refinment (AMR) methods. The primary application focus is Eulerian astrophysics codes. Octopus uses the HPX C++11 runtime system and an octree-based AMR algorithm. The code is based off of work ... [More] originally done by Dominic Marcello. Octopus is developed at Louisiana State University through the joint effort of the STE||AR group at the Center for Computation and Technology and the Department of Physics. The primary developers are Bryce Adelstein-Lelbach and Zach Byerly. This work is supported by the National Science Foundation through award 1240655 (STAR). [Less]

Evolutionary computation framework

ECF is a C++ framework intended for application of any type of evolutionary computation. Current features include: * parameterless: genotype (individual structure) is the only mandatory parameter * genetic algorithm genotypes (bitstring, binary encoded real values, floating point vectors ... [More] , permutation vectors), genetic programming genotype (tree) * individuals may contain any genotypes in any number * algorithms: steady state tournament, generational roulette-wheel, particle swarm optimization (PSO) * parallel execution in many models (global paralel EA, distributed EA, hybrid parallel EA...) using MPI [Less]

min-max-modular-svm

min-max-modular-svm is a powerful parallel SVM on parallel systems, such as super computer and clusters. It is designed to solve very large scale learning problems that could not be easily solved by traditional classifiers. More over, new version of min-max-modular svm can achieve high efficiency in ... [More] incremental learning with minimum updating work. New features such as solving multi-label, hierarchical, and imbalanced learning problems will also be added soon. [Less]