Construction and Rendering of Trimmed Blending Surfaces with Sharp Features on a GPU

Ko, Daehyun; Lee, Jieun; Lim, Seong-Jae; Yoon, Seung‐Hyun

doi:10.4218/etrij.11.1510.0091

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…The many-core architecture of a GPU makes it possible to execute an operation in parallel and manipulate massive data rapidly. In the past, GPUs were mainly used in graphics applications [11], but more recently, their usage has been extended to the area of accelerated computing [12]. In particular, lately, GPUs have been used in many applications for genome analysis requiring high-performance computing capability.…”

Section: Introductionmentioning

confidence: 99%

Toward High Utilization of Heterogeneous Computing Resources in SNP Detection

Lim¹,

Kim²,

Jung³

et al. 2015

ETRI J

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As the amount of re‐sequencing genome data grows, minimizing the execution time of an analysis is required. For this purpose, recent computing systems have been adopting both high‐performance coprocessors and host processors. However, there are few applications that efficiently utilize these heterogeneous computing resources. This problem equally refers to the work of single nucleotide polymorphism (SNP) detection, which is one of the bottlenecks in genome data processing. In this paper, we propose a method for speeding up an SNP detection by enhancing the utilization of heterogeneous computing resources often used in recent high‐performance computing systems. Through the measurement of workload in the detection procedure, we divide the SNP detection into several task groups suitable for each computing resource. These task groups are scheduled using a window overlapping method. As a result, we improved upon the speedup achieved by previous open source applications by a magnitude of 10.

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Section: Introductionmentioning

confidence: 99%

Toward High Utilization of Heterogeneous Computing Resources in SNP Detection

Lim¹,

Kim²,

Jung³

et al. 2015

ETRI J

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show abstract

“…In the past, GPUs were only used for accelerating the production of a rendered image [4]. As GPUs are rapidly developing, however, they are being steadily used in various fields.…”

Section: Introductionmentioning

confidence: 99%

CUDA-based JPEG2000 encoding scheme

Lee

Kim

Yoon

2014

16th International Conference on Advanced Communication Technology

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Abstract-JPEG2000 is the international standard for image compression. The rich feature set and the state of the art image compression performance make JPEG2000 an attractive alternative for many applications. Especially JPEG2000 is used in the area for digital cinema and medical image. Although the JPEG2000 provides high compression rates and error tolerance, it is burden for both encoding and decoding. To improve the performance, a parallel computing architecture called CUDA has been receiving a lot of attention recently. In this paper, we attempt to realize a real-time JPEG2000 encoding scheme by using GPUs. We present CUDA algorithms that perform DCDM decomposition, multi-component transform, 2D discrete wavelet transform, and quantization completely on a CUDA device, which brings us significant performance gain on a general CPU without extra cost. In addition, we present CUDA algorithm for performing the color conversion from RGB to XYZ.

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“…In the past, GPUs were only used for accelerating the production of a rendered image [5]. As GPUs are rapidly developing, however, they are being steadily used in various fields [6].…”

Section: Introductionmentioning

confidence: 99%

GPU-based JPEG2000 decoding scheme for digital cinema

Lee

Kim

Lee

et al. 2014

16th International Conference on Advanced Communication Technology

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Abstract-For the digital cinema system specification released by Digital Cinema Initiatives, it was decided to use 2K or 4K images encoded by the JPEG2000 standard. JPEG2000 provides high compression rates and error tolerance, but it is a burden for both encoding and decoding. To improve the decoding performance, a parallel computing architecture called CUDA has been receiving a lot of attention recently. In this paper, we attempt to realize a real-time JPEG2000 decoding scheme for digital cinema using multiple CPU cores and GPUs. We present CUDA algorithms that perform inverse quantization, inverse 2D discrete wavelet transform and inverse irreversible color transform on a CUDA device, which brings us significant performance gain on a general CPU without extra cost.

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Construction and Rendering of Trimmed Blending Surfaces with Sharp Features on a GPU

Cited by 6 publications

References 10 publications

Toward High Utilization of Heterogeneous Computing Resources in SNP Detection

Toward High Utilization of Heterogeneous Computing Resources in SNP Detection

CUDA-based JPEG2000 encoding scheme

GPU-based JPEG2000 decoding scheme for digital cinema

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