.png){kind=icon}
.png){kind=icon}
Return
Accelerating an algorithm for perishable inventory control on heterogeneous platforms
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jpdc.2016.12.021.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper analyses and evaluates parallel implementations of an optimization algorithm for perishable inventory control problems. This iterative algorithm has high computational requirements when solving large problems. Therefore, the use of parallel and distributed computing reduces the execution time and improves the quality of the solutions. This work investigates two implementations on heterogeneous platforms: (1) a MPI-PTHREADS version; and (2) a multi-GPU version. A comparison of, these implementations has been carried out. Experimental results show the benefits of using parallel and distributed codes to solve this kind of problems. Furthermore, the distribution of the workload among the available processing elements is a challenging problem. This distribution of tasks can be modelled as a Bin-Packing problem. This implies that the selection of the set of tasks assigned to every processing element requires the design of a heuristic capable of efficiently balancing the workload statically with no significant overhead. This heuristic has been used for the parallel implementations of the optimization for perishable inventory control problem. (C) 2016 Elsevier Inc. All rights reserved.
Keywords:
Perishable inventory control
GPU computing
Heterogeneous computing
Optimization
Monte-Carlo simulation
Bin-Packing problem
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
3.8K
Citations:
4.8K