Massively parallel desktop computing capabilities now well within the reach of individual academics modify the environment for posterior simulation in fundamental and potentially quite advantageous ways. But to fully exploit these benefits algorithms that conform to parallel computing...

In this paper, I have researched and developed solutions for optimizing the stream compaction algorithmic function using the Compute Unified Device Architecture (CUDA). The stream compaction is a common parallel primitive, an essential building block for many data processing algorithms, whose...

In this paper, we have researched economic considerations regarding the opportunity of optimizing data processing using graphics processing units (GPU) that implement the Compute Unified Device Architecture (CUDA). After analyzing the hardware’s costs and a series of experimental tests, the...

In this paper, we have researched and developed solutions for optimizing the radix sort algorithmic function using the Compute Unified Device Architecture (CUDA). The radix sort is a common parallel primitive, an essential building block for many data processing algorithms, whose optimization...

In this paper, the authors propose a new parallel implemented approach on Graphics Processing Units (GPU) for training logistic regression model. Logistic regression has been applied in many machine learning applications to build building predictive models. However, logistic training regularly...