Abstract
Heterogeneous systems have gained popularity due to the rapid growth in data and the need for processing this big data to extract useful information. In recent years, many healthcare applications have been developed which use machine learning algorithms to perform tasks such as image classification, object detection, image segmentation, and instance segmentation. The increasing amount of big visual data requires images to be processed efficiently. It is common that we use heterogeneous systems for such type of applications, as processing a huge number of images on a single PC may take months of computation. In heterogeneous systems, data are distributed on different nodes in the system. However, heterogeneous systems do not distribute images based on the computing capabilities of different types of processors in the node; therefore, a slow processor may take much longer to process an image compared to a faster processor. This imbalanced workload distribution observed in heterogeneous systems for image processing applications is the main cause of inefficient execution. In this paper, an efficient workload distribution mechanism for image processing applications is introduced. The proposed approach consists of two phases. In the first phase, image data are divided into an ideal split size and distributed amongst nodes, and in the second phase, image data are further distributed between CPU and GPU according to their computation speeds. Java bindings for OpenCL are used to configure both the CPU and GPU to execute the program. The results have demonstrated that the proposed workload distribution policy efficiently distributes the images in a heterogeneous system for image processing applications and achieves 50% improvements compared to the current state-of-the-art programming frameworks.
Highlights
GPUs are becoming popular to exploit data-level parallelism [1] in embarrassingly parallel applications [2] because of the SIMD [3, 4] architecture
Images inherently have data-level parallelism [1, 10], because all pixels can be processed independently and GPUs are commonly used for image processing applications
For image processing applications or applications involving the process of big data in different domains such as healthcare, heterogeneous systems are commonly used and have shown improvement over single processor and distributed systems
Summary
GPUs (graphical processing units) are becoming popular to exploit data-level parallelism [1] in embarrassingly parallel applications [2] because of the SIMD (single instruction multiple data) [3, 4] architecture. A heterogeneous cluster has many CPUs and GPUs and can exploit both task-level and datalevel parallelism in applications. A heterogeneous cluster (i.e., a cluster containing CPUs and GPUs) can exploit both task-level (across multiple images) and data-level parallelism in images. It is very challenging to provide a mechanism that can efficiently utilize the computing resources in a heterogeneous cluster [12]. We provide a programming framework that ensures efficient workload distribution amongst the nodes by dividing the data into equal size splits and distribute the split data between CPU and GPU cores based on their computational capabilities [20].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
