• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38B no.3
• /
• pp.163-171
• /
• 2013

Recently, the requirement of processing High Definition (HD) video or 3D application on low, mobile devices has been expanded and content data has been increased as well. It is becoming a major issue in Cloud computing where a Virtual Desktop Infrastructure (VDI) Service needs efficient data processing ability to provide Quality of Experience (QoE) in Cloud computing. In this paper, we propose three kind of Thick-Thin VDI Service which can share and delegate VDI service based on Thick Client using CPU and GPU. Furthermore, we propose and discuss the VDI Service Optimization Method in mixed CPU and GPU Heterogeneous Environment using CPU Parallel Processing OpenMP and GPU Parallel Processing CUDA.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.3
• /
• pp.423-431
• /
• 2021

SPECK and SIMON are lightweight block ciphers developed by NSA(National Security Agency), and SIMECK is a new lightweight block cipher that combines the advantages of SPECK and SIMON. In this paper, a large-capacity encryption using SPECK, SIMON, and SIMECK is implemented using a GPU with efficient parallel processing. CUDA library provided by NVIDIA was used, and performance was maximized by using CUDA assembly language PTX to eliminate unnecessary operations. When comparing the results of the simple CPU implementation and the implementation using the GPU, it was possible to perform large-scale encryption at a faster speed. In addition, when comparing the implementation using the C language and the implementation using the PTX when implementing the GPU, it was confirmed that the performance increased further when using the PTX.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.11a
• /
• pp.1458-1461
• /
• 2010

작년 7월 7일에 있었던 DDoS 공격과 같이 악성 코드로 인한 피해의 규모가 해마다 증가하고 있다. 특히 변형 웜(Polymorphic Worm)은 기존의 방법으로 1차 공격에서의 탐지가 어렵기 때문에 그 위험성이 더 크다. 이에 본 연구에서는 바이오 인포매틱스(Bioinformatics) 분야에서 유전자들의 유사성과 특징을 찾기 위한 방법 중 하나인 Local Alignment를 소개하고 이를 변형 웜 탐지에 적용한다. 또한 수행의 병렬화 및 알고리즘 변형을 통하여 기존 알고리즘의 $O(n^4)$수행시간이라는 단점을 극복한다. 병렬화는 NVIDIA사의 GPU를 이용한 CUDA 프로그래밍과 AMD사의 GPU를 사용한 OpenCL 프로그래밍을 통하여 수행되었다. 이로써 각 GPGPU 플랫폼에서의 Local Alignment를 이용한 변형 웜 탐지 알고리즘의 성능을 비교하였다.

• The Journal of the Korea Contents Association
• /
• v.15 no.7
• /
• pp.1-11
• /
• 2015

In recent high performance computing system, GPGPU has been widely used to process general-purpose applications as well as graphics applications, since GPU can provide optimized computational resources for massive parallel processing. Unfortunately, GPGPU doesn't exploit computational resources on GPU in executing general-purpose applications fully, because the applications cannot be optimized to GPU architecture. Therefore, we provide GPU research guideline to improve the performance of computing systems using GPGPU. To accomplish this, we analyze the negative factors on GPU performance. In this paper, in order to clearly classify the cause of the negative factors on GPU performance, GPU core status are defined into 5 status: fully active status, partial active status, idle status, memory stall status and GPU core stall status. All status except fully active status cause performance degradation. We evaluate the ratio of each GPU core status depending on the characteristics of benchmarks to find specific reasons which degrade the performance of GPU. According to our simulation results, partial active status, idle status, memory stall status and GPU core stall status are induced by computational resource underutilization problem, low parallelism, high memory requests, and structural hazard, respectively.

• Journal of Digital Contents Society
• /
• v.13 no.2
• /
• pp.169-176
• /
• 2012

This paper presents a parallel object-oriented programming (OOP) platform for efficient visualization of three-dimensional content in CUDA environments. For this purpose, this paper discusses the features and limitations in implementing C++ object-oriented codes using CUDA and proposes the solutions. Also, it presents how to implement a 3D parallel visualization platform based on the MVC (Model/View/Controller) design pattern. Also, it provides sample implementations for integral MLS (iMLS) and signed distance fields (SDFs) based on the Marching Cubes and Raytracing. The proposed approach enables GPU parallel processing only by implementing simple interfaces. Based on this, developers can expect general benefits that are common in general OOP techniques including abstractization and inheritance. Though I implemented only two specific samples in this paper, I expect my approach can be widely applied to general computer graphics problems.

• 한국HCI학회:학술대회논문집
• /
• 2008.02a
• /
• pp.451-455
• /
• 2008

In this paper, we present how we implement the low-level, triangle intersection routine in a massively parallel fashion using n VIDIA's new GPGPU language, CUDA. Triangle intersection often becomes a computational bottleneck in the collision detection problem. Due to the relatively low bandwidth between CPU and GPU, it has been challenging to implement efficient, object-space collision detection between triangle sets. However, thanks to the improved data transmission rates in CUDA architecture, in this paper, we improved the performance of triangle intersection substantially better than the optimized CPU counterpart.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.25 no.2
• /
• pp.251-254
• /
• 2014

In this paper, we studied a technique of extracting a Frequency Hopping(FH) signal for analysis using high-speed parallel processing structure. Unlike fixed frequency signal, FH signal is difficult to detect and analyze because FH systems use many random frequencies instead of a single carrier frequency. To solve this problem we designed a method that analyze FH signal using high-speed parallel processing. In order to apply parallel processing, we use CUDA using GPU and compare single processing with prarallel processing. As a result, using CUDA on a GPU is about 8.53 times faster than single processing.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.10a
• /
• pp.238-241
• /
• 2015

특징점 추출 알고리즘은 물체인식, 로보틱스, 비디오트래킹 등 많은 컴퓨터 비전 분야에 사용된다. 그 중 SIFT 알고리즘은 많은 계산량이 필요한 알고리즘으로 구성되어 있으므로 높은 화소의 이미지를 처리하기 위해서는 많은 시간이 소요되므로 GPU를 통한 가속이 필요하다. 본 논문에서는 NVIDIA GPU 장비를 사용하는 CUDA를 이용하여 SIFT 알고리즘을 병렬적으로 처리하여 4배 이상의 수행시간 감소 및 특징점이 많고 고해상도인 영상에서 효율이 더 높은 것을 확인하였다.

• Journal of Digital Contents Society
• /
• v.18 no.4
• /
• pp.739-745
• /
• 2017

This paper presents and discusses an implementation of the GPU shifted sorting method to find approximate k nearest neighbors which executes within "warp", the minimum execution unit in GPU parallel architecture. Also, this paper presents the comparison results with other two common nearest neighbor searching methods, GPU-based kd-tree and ANN (Approximate Nearest Neighbor) library. The proposed implementation focuses on the cases when k is small, i.e. 2, 4, 8, and 16, which are handled efficiently within warp to consider it is very common for applications to handle small k's. Also, this paper discusses optimization ways to implementation by improving memory management in a loop for the CUB open library and adopting CUDA commands which are supported by GPU hardware. The proposed implementation shows more than 16-fold speed-up against GPU-based other methods in the tests, implying that the improvement would become higher for more larger input data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2009.10a
• /
• pp.902-903
• /
• 2009

Matrix multiplication is widely used in scientific and engineering field. Locality can improve the execution performance of matrix multiplication. A method for accelerating matrix multiplication is presented. This method uses both CPU and GPU computing power in PC. The presented method improved execution time about %15~30% than the method which uses only GPU.