Using statistical tests for improving state-of-the-art heuristics for the probabilistic traveling salesman problem with deadlines

The Probabilistic Traveling Salesman Problem with Deadlines (PTSPD) is a Stochastic Vehicle Routing Problem with a computationally demanding objective function. Currently heuristics using an approximation of the objective function based on Monte Carlo Sampling are the state-of-the-art methods for the PTSPD. We show that those heuristics can be significantly improved by using statistical tests in combination with the sampling-based evaluation of solutions for the pairwise comparison of solutions

Effect of aluminum doping on the structural, morphological, electrical and optical properties of ZnO thin films prepared by sol-gel dip coating

689-693Aluminum doped zinc oxide (AZO) thin films with 0-5 at.% aluminum content have been prepared by sol-gel dip coating technique. The thickness of the films has been measured using alpha step method. The structural and morphological properties have been studied, respectively, using X-ray diffraction (XRD) and scanning electron microscopy (SEM). Higher intensity zinc oxide (ZnO) peak (002) has been observed in 1 at.% aluminum doped film with 450 °C of annealing temperature. The grains are more densely packed in the films doped in 1 at.% aluminum content. The grains have tendency to decrease in size as the aluminum content increases. Electrical resistivity measurement technique reveals that electrical resistance decreases with increase of film thickness. The lowest resistivity of the AZO thin film is 3.2×10-2 Ω. Optical properties of AZO thin films are tested by UV-visible spectroscopy, while comparing with all the films 0.5 at.% aluminum doped film produces more than 90% of transmittance

Exploiting historical data: pruning autotuning spaces and estimating the number of tuning steps

International audienceAutotuning, the practice of automatic tuning of applications to provide performance portability, has received increased attention in the research community, especially in high performance computing. Ensuring high performance on a variety of hardware usually means modifications to the code, often via different values of a selected set of parameters, such as tiling size, loop unrolling factor or data layout. However, the search space of all possible combinations of these parameters can be large, which can result in cases where the benefits of autotuning are outweighed by its cost, especially with dynamic tuning. Therefore, estimating the tuning time in advance or shortening the tuning time is very important in dynamic tuning applications. We have found that certain properties of tuning spaces do not vary much when hardware is changed. In this paper, we demonstrate that it is possible to use historical data to reliably predict the number of tuning steps that is necessary to find a wellperforming configuration, and to reduce the size of the tuning space. We evaluate our hypotheses on a number of HPC benchmarks written in CUDA and OpenCL, using several different generations of GPUs and CPUs