Exploiting Multi- and Many-core Parallelism for Accelerating Image Compression

Kim, Cheong Ghil; Choi, Yong Soo

doi:10.1109/mue.2011.13

Cited by 2 publications

(2 citation statements)

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“…Some works explore SIMD (Single Instruction Multiple Data) architectures, and more especially GPU (Graphical Processing Unit) (Wakatani, 2012;Khan & Akhtar, 2013). Kim and Choi (2011) combined both GPU and multithreading in their 2D DCT (discrete cosine transform) solution for the FIC problem (Kim & Choi, 2011). The article focused on the OpenCL parallel modeling.…”

Section: Resultsmentioning

confidence: 99%

Exploiting Data-Parallelism on Multicore and SMT Systems for Implementing the Fractal Image Compressing Problem

Righi¹,

Rodrigues²,

Costa³

et al. 2016

CIS

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This paper presents a parallel modeling of a lossy image compression method based on the fractal theory and its evaluation over two versions of dual-core processors: with and without simultaneous multithreading (SMT) support. The idea is to observe the speedup on both configurations when changing application parameters and the number of threads at operating system level. Our target application is particularly relevant in the Big Data era. Huge amounts of data often need to be sent over low/medium bandwidth networks, and/or to be saved on devices with limited store capacity, motivating efficient image compression. Especially, the fractal compression presents a CPU-bound coding method known for offering higher indexes of file reduction through highly time-consuming calculus. The structure of the problem allowed us to explore data-parallelism by implementing an embarrassingly parallel version of the algorithm. Despite its simplicity, our modeling is useful for fully exploiting and evaluating the considered architectures. When comparing performance in both processors, the results demonstrated that the SMT-based one presented gains up to 29%. Moreover, they emphasized that a large number of threads does not always represent a reduction in application time. In average, the results showed a curve in which a strong time reduction is achieved when working with 4 and 8 threads when evaluating pure and SMT dual-core processors, respectively. The trend concerns a slow growing of the execution time when enlarging the number of threads due to both task granularity and threads management.

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Section: Resultsmentioning

confidence: 99%

Exploiting Data-Parallelism on Multicore and SMT Systems for Implementing the Fractal Image Compressing Problem

Righi¹,

Rodrigues²,

Costa³

et al. 2016

CIS

View full text Add to dashboard Cite

show abstract

“…In [16], [17] can be found single elementar tasks than can be done by GPUs and in [18] with other Parallel Architectures like the Intel Xeon Phi. In [19], [20] can be found full algorithms implemented in GPUs.…”

Section: Introductionmentioning

confidence: 99%