Victoria María Sanz

A Hybrid Parallel Search Algorithm for Solving Combinatorial Optimization Problems on Multicore Clusters

Multicore clusters are widely used to solve combinatorial optimization problems, which require high computing power and a large amount of memory. In this sense, Hash Distributed A* (HDA*) parallelizes A*, a combinatorial optimization algorithm, using the MPI library. HDA* scales well on multicore clusters and on multicore machines. Additionally, there exist several versions of HDA* that were adapted for multicore machines, using the Pthreads library. In this paper, we present Hybrid HDA* (HHDA*), a hybrid parallel search algorithm based on HDA* that combines message-passing (MPI) with shared-memory programming (Pthreads) to better exploit the computing power and memory of multicore clusters. We evaluate the performance and memory consumption of HHDA* on a multicore cluster, using the 15-puzzle as a case study. The results reveal that HHDA* achieves a slightly higher average performance and uses considerably less memory than HDA*. These improvements allowed HHDA* to solve one of the hardest 15-Puzzle instances.

Improving Hash Distributed A* for Shared Memory Architectures Using Abstraction

The A* algorithm is generally used to solve combinatorial optimization problems, but it requires high computing power and a large amount of memory, hence, efficient parallel A* algorithms are needed. In this sense, Hash Distributed A* (HDA*) parallelizes A* by applying a decentralized strategy and a hash-based node distribution scheme. However, this distribution scheme results in frequent node transfers among processors. In this paper, we present Optimized AHDA*, a version of HDA* for shared memory architectures, that uses an abstraction-based node distribution scheme and a technique to group several nodes before transferring them to the corresponding thread. Both methods reduce the amount of node transfers and mitigate communication and contention. We assess the effect of each technique on algorithm performance. Finally, we evaluate the scalability of the proposed algorithm, when it is run on a multicore machine, using the 15-puzzle as a case study.

Parallel processing puzzle N 2 -1 on cluster architectures performance analysis

An analysis of a parallel solution of N2-1 puzzle using clusters, is presented. This problem is interesting due to its complexity and related applications, particularly in the field of robotics. A variation of classic heuristics for forecasting the work to be done in order to reach a solution is analyzed, and it is shown that its use significantly improves the time of sequential algorithm A . Then, a parallel solution on a distributed architecture is presented and speedup is analyzed based on the number of processors, efficiency, and the possible superlinearity when scaling the problem.