GPU backed Data Mining on Android Devices
Robert Fritze, Claudia Plant

TL;DR
This paper presents a wrapper library enabling OpenCL-based GPU computing on Android devices, demonstrating its effectiveness for data mining algorithms like DBSCAN and Kmeans in terms of speed and energy efficiency.
Contribution
The authors developed a wrapper library that allows existing OpenCL programs to run on Android devices with minimal modifications, facilitating GPU-accelerated data mining.
Findings
GPU implementation outperforms multithreaded CPU versions in speed
GPU approach offers better energy efficiency
OpenCL can be effectively used on Android for HPC tasks
Abstract
Choosing an appropriate programming paradigm for high-performance computing on low-power devices can be useful to speed up calculations. Many Android devices have an integrated GPU and - although not officially supported - the OpenCL framework can be used on Android devices for addressing these GPUs. OpenCL supports thread and data parallelism. Applications that use the GPU must account for the fact that they can be suspended by the user or the Android operating system at any moment. We have created a wrapper library that allows to use OpenCL on Android devices. Already written OpenCL programs can be executed with almost no modification. We have used this library to compare the performance of the DBSCAN and Kmeans algorithms on an integrated GPU of an Arm-v7 tablet with other single and multithreaded implementations on the same device. We have investigated which programming paradigm and…
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Taxonomy
TopicsDistributed and Parallel Computing Systems · Parallel Computing and Optimization Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
