Skeleton-based automatic parallelization of image processing algorithms for GPUs

Nugteren, Cedric; Corporaal, Henk; Mesman, Bart

doi:10.1109/samos.2011.6045441

Cited by 13 publications

(9 citation statements)

References 16 publications

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“…Other work on algorithmic skeletons introduces algorithm classifications highly related to this work. Examples are [3], [8], [10] and [15]. In comparison to these works, we provide more detail in our classes (key-enabler: parameters) and cover a large number of applications while maintaining understandability (key-enabler: modularity).…”

Section: Classifications Used With Algorithmic Skeletonsmentioning

confidence: 99%

Classification Algorithms

2020

Sharing Economy and Big Data Analytics

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Multi-core and many-core were already major trends for the past six years, and are expected to continue for the next decades. With this trend of parallel computing, it becomes increasingly difficult to decide on which architecture to run a certain application or algorithm. Additionally, it brings forth the problem of parallel programming, leading to the so-called software engineering crisis.In this work we present a new algorithm classification. This algorithm classification is designed for programmers and tools to capture and reason about parallel algorithms. The classification is initially intended to be used to address two challenges: 1) the challenge of parallel programming, and 2), performance prediction for parallel and heterogeneous systems. In order to address these two challenges, we introduce a new algorithm classification in this work. The classification uses a limited vocabulary and a well-defined grammar, creating a modular classification. Additionally, the classification is parameterisable. Both the modularity and the parameterisability of the algorithm classification make it possible to enable a very fine-grained and widely applicable classification.We furthermore illustrate the new algorithm classification by classifying a number of example algorithms from the field of image processing. We also show a number of code snippets to give an intuitive feel for the classification.

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Section: Classifications Used With Algorithmic Skeletonsmentioning

confidence: 99%

Classification Algorithms

2020

Sharing Economy and Big Data Analytics

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“…Image processing applications exhibit structures for different execution and memory access patterns [4,22,23], some of which are classified in Table 2. These are mainly algorithmic characteristics, and hence are platform independent and equally valid for different computing platforms CPU, GPU, FPGA etc..…”

Section: Inter Processor Communication Networkmentioning

confidence: 99%

FPGA-Based Soft-Core Processors for Image Processing Applications

Amiri

Siddiqui

Kelly

et al. 2016

J Sign Process Syst

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With security and surveillance, there is an increasing need to process image data efficiently and effectively either at source or in a large data network. Whilst a Field-Programmable Gate Array has been seen as a key technology for enabling this, the design process has been viewed as problematic in terms of the time and effort needed for implementation and verification. The work here proposes a different approach of using optimized FPGA-based soft-core processors which allows the user to exploit the task and data level parallelism to achieve the quality of dedicated FPGA implementations whilst reducing design time. The paper also reports some preliminary progress on the design Roger Woods flow to program the structure. An implementation for a Histogram of Gradients algorithm is also reported which shows that a performance of 328 fps can be achieved with this design approach, whilst avoiding the long design time, verification and debugging steps associated with conventional FPGA implementations.

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“…Many researchers have already been applied GPUs to implement many algorith ms in various areas such as image processing, computational geometry, and scientific co mputation, as well as co mputer graphics [15][16][17][18]. Parallel imp lementations on GPUs have been applied to various numerical problems [19][20][21] to reduce the computation time without sacrificing the degree of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Parallel Implementation of Texture Based Image Retrieval on The GPU

Heidari

Chalechale

Mohammadabadi

2013

IJIGSP

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Most image processing algorithms are inherently parallel, so multithreading processors are suitable in such applications. In huge image databases, image processing takes very long time for run on a single core processor because of single thread execution of algorithms. Graphical Processors Units (GPU) is more common in most image processing applications due to multithread execution of algorithms, programmability and low cost. In this paper we implement texture based image retrieval system in parallel using Compute Unified Device Architecture (CUDA) programming model to run on GPU. The main goal of this research work is to parallelize the process of texture based image retrieval through entropy, standard deviation, and local range, also whole process is much faster than normal. Our work uses extensive usage of highly multithreaded architecture of multi-cored GPU. We evaluated the retrieval of the proposed technique using Recall, Precision, and Average Precision measures. Experimental results showed that parallel implementation led to an average speed up of 140.046×over the serial implementation. The average Precision and the average Recall of presented method are 39.67% and 55.00% respectively

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Skeleton-based automatic parallelization of image processing algorithms for GPUs

Cited by 13 publications

References 16 publications

Classification Algorithms

Classification Algorithms

FPGA-Based Soft-Core Processors for Image Processing Applications

Parallel Implementation of Texture Based Image Retrieval on The GPU

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