OpenCL Partial Offloading Technique on Mobile Device
Date Issued
2015
Date
2015
Author(s)
Chou, Ju-Cheng
Abstract
Mobile devices have become increasingly prevalent in recent years, leading the public to reassess their expectations in terms of user experience. As a result, industry has progressively turned to utilizing hardware components, such as GPUs (graphics processing units), which are traditionally present on computers. Despite their sophistication, mobile devices lack the capacity to execute resource-intensive tasks quickly and efficiently, and the state of the art is to leverage heterogeneous cloud resources to augment mobile devices. However, network transfer costs drastically limit the advantage of this approach. While performing computing tasks on a heterogeneous system is a well-studied area, how to offload workload onto a heterogeneous cloud in the presence of an unstable network remains an outstanding problem. This thesis presents the design and implementation of a workload offloading framework that transparently mitigates the network transfer cost and takes advantage of a heterogeneous resourcerich cloud for speeding up mobile devices’ OpenCL computations. Our approach uses machine learning mechanism to decide the workload partition and maps the processing elements to heterogeneous local and remote resources. By partitioning the tasks, our system increases the utilization of mobile and server resources while reducing the amount of data to transfer over the network. Our results show that adaptive partitioning can have a significant impact on the performance of benchmarks, even in a dynamic environment. The experiments result shows that the difference in performance between the optimal partition and the partition suggested by our model is less than 8%.
Subjects
Mobile Cloud Computing
Pervasive Computing
Computation Offloading
Heterogeneous System
Resource Mapping
Type
thesis
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