• Corpus ID: 212547910

Parallel Image Processing from Cloud using CUDA and HADOOP Architecture: A Novel Approach

@inproceedings{Gowda2015ParallelIP,
  title={Parallel Image Processing from Cloud using CUDA and HADOOP Architecture: A Novel Approach},
  author={M. T. Thirthe Gowda},
  year={2015}
}
In There is an increased, large quantity if data with the super-resolution quality data, hence there is an increased demand in high quality image data. This requirements causes a challenge in disk space in single PC or computers. A primary solution to employ the storage of large quantity of high quality is provided by use of Cloud computing. The proposed approach uses a Hadoop based remote sensing image processing system (HBRSIPS) which is used in areas of big data analysis, particularly text… 

Figures from this paper

References

SHOWING 1-10 OF 14 REFERENCES
Local acceleration in Distributed Geographic Information Processing with CUDA
TLDR
The paper designs a prototype for distributed remote sensing image processing and achieves local acceleration in each computing node with CUDA (Compute Unified Device Architecture), and overviews the architecture and programming model of CUDA.
Towards a robust, real-time face processing system using CUDA-enabled GPUs
TLDR
This paper designs and develops optimized parallel implementations of face detection and tracking algorithms on graphics processors using the Compute Unified Device Architecture (CUDA), a C-based programming model from NVIDIA.
High-performance cone beam reconstruction using CUDA compatible GPUs
Parallel Computing Experiences with CUDA
TLDR
Experiences gained in applying CUDA to a diverse set of problems are surveyed and the parallel speedups over sequential codes running on traditional CPU architectures attained by executing key computations on the GPU are surveyed.
Scalable parallel programming
TLDR
The challenge is to develop mainstream application software that transparently scales its parallelism to leverage the increasing number of processor cores, much as 3D graphics applications transparently scale their Parallelism to manycore GPUs with widely varying numbers of cores.
A Survey of General‐Purpose Computation on Graphics Hardware
TLDR
This report describes, summarize, and analyzes the latest research in mapping general‐purpose computation to graphics hardware.
A rough set approach to the discovery of classification rules in spatial data
TLDR
It is demonstrated that the proposed approach can effectively discover in remotely sensed data the optimal spectral bands and optimal rule set for a classification task and paves the road for data mining in mixed spatial databases consisting of qualitative and quantitative data.
Land cover classification based on tolerant rough set
TLDR
According to the overall accuracy, the κ coefficient, the total normalized probability of misclassification (TNPM) and McNemar's test, the result of TRSC was better than that of the minimum distance classifier (MDC), and similar to those of the maximum likelihood classifier and the multiplayer perceptron (MLP).
Operating System Concepts
TLDR
This best-selling book provides a solid theoretical foundation for understanding operating systems while giving the teacher and students the flexibility to choose the implementation system.
NVIDIA CUDA Programming Guide
  • 2009
...
...