Performance analysis of parallel algorithms. In Journal of Applied Quantitative Methods, volume 2, pages 129-134. JAQM, Romania, 2007.Felician Alecu, 2007. Performance Analysis of Parallel Algorithms, Journal of Applied Quantitative methods, 2(1): 129-134....
Over the past decade, there has been a continued interest in the design of schemes for the implementation of particle filtering algorithms using parallel or distributed hardware of various types, including general purpose devices such as multi-core CPUs or graphical processing units (GPUs) [1] and...
even for large values of N. We also tested with another parallel algorithms implementation, HPX, and got similar results. That doesn’t mean it was wrong for the standards committee to add those to the STL; it just means the hardware our implementation targets didn’t see improvements...
This paper explores the application of parallel algorithms and high-performance computing (HPC) in the processing and forecasting of large-scale water demand data. Building upon prior work, which identified the need for more robust and scalable forecasting models, this study integrates parall...
There are many studies that address performance measures and optimization design of parallel robots, but there has been no review of the performance indices, optimization algorithms, and optimization methods of PMs. This work studies these three aspects to provide a reference for researchers in related...
For data parallel computing frameworks, a precise yet useful cost model often measures the job execution time (i.e., beyond simple cost like I/O) [,9,12]. The main challenge in building an execution time based cost model for a DAG workflow is the inherent complexity of system resource al...
Though performance measures are key for evaluating and improving GPU-based parallel algorithms, good performance criteria are yet to be developed. In this chapter, we reviewed the conventional metrics for parallel implementation, and pointed out that these metrics are problematic in the scenario of GPU...
The performance of a randomized metaheuristic algorithm can be divided into efficiency and effectiveness measures. The efficiency relates to the algorithm’s speed of finding accurate solutions, convergence, and computation. On the other hand, effectiveness relates to the algorithm’s capability of ...
One of the most popular measures of robustness of a statistical procedure is the breakdown point, which represents the proportion of outlying data points an estimator can resist before giving a biased result. The maximum breakdown point is 50%, since, if more than half of the observations are ...
LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: Faster training speed and higher efficiency. Lower memory usage. Better accuracy. Support of parallel, distributed, and GPU learning. Capable...