Master’s thesis: Parallel Computing for Digital Signal Processing on Mobile Device GPUs

May 27th, 2014

This master’s thesis by Markus Konrad analyzes the potentials of GPGPU on mobile devices such as smartphones or tablets. The question was, if and how the GPU on such devices can be used to speed up certain algorithms especially in the fields of image processing. GPU computing technologies such as OpenCL, OpenGL shaders, and Android RenderScript are assessed in the thesis. The abstract reads as follows:

This thesis studies how certain popular algorithms in the field of image and audio processing can be accelerated on mobile devices by means of parallel execution on their graphics processing unit (GPU). Several technologies with which this can be achieved are compared in terms of possible performance improvements, hardware and software support, as well as limitations of their programming model and functionality. The results of this research are applied in a practical project, consisting of performance improvements for marker detection in an Augmented Reality application for mobile devices.

The PDF is available for download and the source-code for some Android application prototypes is published on github.

Libra 3.0 – GPGPU SDK on Mobiles and Tablets

November 13th, 2013

The Libra 3.0 Heterogeneous Cloud Computing SDK has recently been released by GPU Systems. It supports PC, Tablet and Mobile Devices and includes a new virtualizing function for cloud compute services of local and remote CPUs and GPUs. C/C++, Java, C# and Matlab are supported. Read the full press release here.

Workload Analysis and Efficient OpenCL-based Implementation of SIFT Algorithm on a Smartphone

September 22nd, 2013

Abstract

Feature detection and extraction are essential in computer vision applications such as image matching and object recognition. The Scale-Invariant Feature Transform (SIFT) algorithm is one of the most robust approaches to detect and extract distinctive invariant features from images. However, high computational complexity makes it difficult to apply the SIFT algorithm to mobile applications. Recent developments in mobile processors have enabled heterogeneous computing on mobile devices, such as smartphones and tablets. In this paper, we present an OpenCL-based implementation of the SIFT algorithm on a smartphone, taking advantage of the mobile GPU. We carefully analyze the SIFT workloads and identify the parallelism. We implemented major steps of the SIFT algorithm using both serial C++ code and OpenCL kernels targeting mobile processors, to compare the performance of different workflows. Based on the profiling results, we partition the SIFT algorithm between the CPU and GPU in a way that best exploits the parallelism and minimizes the buffer transferring time to achieve better performance. The experimental results show that we are able to achieve 8.5 FPS for keypoints detection and 19 FPS for descriptor generation without reducing the number and the quality of the keypoints. Moreover, the heterogeneous implementation can reduce energy consumption by 41% compared to an optimized CPU-only implementation.

(Guohui Wang, Blaine Rister, and Joseph R. Cavallaro: “Workload Analysis and Efficient OpenCL-based Implementation of SIFT Algorithm on a Smartphone”, 1st IEEE Global Conference on Signal and Information Processing (GlobalSIP), Dec. 2013, [PDF])

Accelerating Computer Vision Algorithms Using OpenCL on The Mobile GPU

March 12th, 2013

Abstract:

Recently, general-purpose computing on graphics processing units (GPGPU) has been enabled on mobile devices thanks to the emerging heterogeneous programming models such as OpenCL. The capability of GPGPU on mobile devices opens a new era for mobile computing and can enable many computationally demanding computer vision algorithms on mobile devices. As a case study, this paper proposes to accelerate an exemplar-based inpainting algorithm for object removal on a mobile GPU using OpenCL. We discuss the methodology of exploring the parallelism in the algorithm as well as several optimization techniques. Experimental results demonstrate that our optimization strategies for mobile GPUs have significantly reduced the processing time and make computationally intensive computer vision algorithms feasible for a mobile device. To the best of the authors’ knowledge, this work is the first published implementation of general-purpose computing using OpenCL on mobile GPUs.

(Guohui Wang, Yingen Xiong, Jay Yun and Joseph R. Cavallaro: “Accelerating Computer Vision Algorithms Using OpenCL on the Mobile GPU – A Case Study”, International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, May 2013, to appear. [PDF])