A Review of CUDA, MapReduce, and Pthreads Parallel Computing Models

TL;DR

This paper reviews CUDA, MapReduce, and Pthreads models, highlighting their features and challenges to aid users in implementing parallel computing for big data processing.

Contribution

It provides a high-level overview of three major parallel programming models to facilitate better understanding and application in high performance computing.

Findings

Comparative analysis of CUDA, MapReduce, and Pthreads features

Identification of challenges in utilizing parallel programming models

Guidance for efficient implementation of big data projects

Abstract

The advent of high performance computing (HPC) and graphics processing units (GPU), present an enormous computation resource for Large data transactions (big data) that require parallel processing for robust and prompt data analysis. While a number of HPC frameworks have been proposed, parallel programming models present a number of challenges, for instance, how to fully utilize features in the different programming models to implement and manage parallelism via multi-threading in both CPUs and GPUs. In this paper, we take an overview of three parallel programming models, CUDA, MapReduce, and Pthreads. The goal is to explore literature on the subject and provide a high level view of the features presented in the programming models to assist high performance users with a concise understanding of parallel programming concepts and thus faster implementation of big data projects using high performance computing.