Highly Scalable, Parallel and Distributed AdaBoost Algorithm using Light Weight Threads and Web Services on a Network of Multi-Core Machines

TL;DR

This paper introduces a highly scalable, hybrid parallel and distributed AdaBoost algorithm leveraging multi-core CPUs and web services, significantly reducing learning time for object detection tasks.

Contribution

The paper presents a novel hierarchical web services architecture for AdaBoost, achieving near-linear speedup and drastically decreasing training time compared to prior single-level implementations.

Findings

Achieved a speedup of 95.1 on 31 workstations with quad-core processors.

Reduced AdaBoost training time to 4.8 seconds per feature.

Demonstrated near-linear scalability in a distributed environment.

Abstract

AdaBoost is an important algorithm in machine learning and is being widely used in object detection. AdaBoost works by iteratively selecting the best amongst weak classifiers, and then combines several weak classifiers to obtain a strong classifier. Even though AdaBoost has proven to be very effective, its learning execution time can be quite large depending upon the application e.g., in face detection, the learning time can be several days. Due to its increasing use in computer vision applications, the learning time needs to be drastically reduced so that an adaptive near real time object detection system can be incorporated. In this paper, we develop a hybrid parallel and distributed AdaBoost algorithm that exploits the multiple cores in a CPU via light weight threads, and also uses multiple machines via a web service software architecture to achieve high scalability. We present a novel hierarchical web services based distributed architecture and achieve nearly linear speedup up to the number of processors available to us. In comparison with the previously published work, which used a single level master-slave parallel and distributed implementation [1] and only achieved a speedup of 2.66 on four nodes, we achieve a speedup of 95.1 on 31 workstations each having a quad-core processor, resulting in a learning time of only 4.8 seconds per feature.