Browse > Article
http://dx.doi.org/10.17662/ksdim.2015.11.1.027

The study on the Efficient methodology to apply the GPU for military information system improvement  

Kauh, Janghyuk (국방과학연구소)
Lee, Dongho (광운대학교 컴퓨터소프트웨어학과)
Publication Information
Journal of Korea Society of Digital Industry and Information Management / v.11, no.1, 2015 , pp. 27-35 More about this Journal
Abstract
Increasing the number of GPU (Graphic Processor Unit) cores, the studies on High Performance Computing Platform using GPU have actively been made in recent. This trend has led to the development of GPGPU (General Purpose GPU) and CUDA (Compute Unified Device Architecture) Framework. In this paper, we explain the many benefits of the GPU based system, and propose the ICIDF(Identify Compute-Intensive Data set and Function) methodology to apply GPU technology to legacy military information system for performance improvement. To demonstrate the efficiency of this methodology, we applied this method to AES CPU based program obtained from the Internet web site. Simply changing the data structure made improved the performance of AES program. As a result, the performance of AES based GPU program is improved gradually up to 10 times. Depending on the developer's ability, additional performance improvement can be expected. The problem to be solved is heat issue, but this problem has been much improved by the development of the cooling technology.
Keywords
Multi-core; Parallel Processing; GPGPU; GPU; CUDA;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 M. Harris, S. Sengupta, Y. Zhang, and A. Davidson, "CUDPP: CUDA data parallel primitives library," 2009. http://gpgpu.org/developer/cudpp/.
2 Jeff A. Stuart, J. D. Owens, "Multi-GPU MapReduce on GPU Clusters," IEEE International Parallel & Distributed Processing Symposium, 2011, pp. 1068-1079.
3 Reza Farivar, Abhishek Verma, Ellick chan, Roy H Campbell, "MITHRA: Multiple data Independent Tasks on a Heterogeneous Resource Architecture," IEEE Cluster Computing and Workshops, 2009.
4 Parth R. Trivedi, "c2cudatranslator: Automatic conversion of source code for C to CUDA C," 2012. http://code.google.com/p/c2cudatranslator
5 Karl malbrain, 786/1280 Byte Table AES C byte-implementation 03 OCT 2006, http://www.geocities.ws/malbrain
6 이승학.김경훈.안치영.최승원, "GPU를 이용한 SDR 시스템용 LTE MIMO 기지국 기능 구현," 디지털산업정보학회 논문지, 제8권, 제4호, 2012, pp. 91-98.
7 이윤혁.김동욱.서영호, "GPGPU기반의 디지털홀로그램 콘텐츠의 고속 생성 기법," 디지털산업정보학회 논문지, 제9권, 제1호, 2013, pp. 151-162.
8 고장혁.이동호, "GPU를 이용한 정보시스템 성능향상에 관한 연구," 한국군사과학기술학회 종합학술대회, 2013.
9 GE Intelligent Platforms, "GPGPU COTS Platforms - High-Performance Computing Solutions," 2011, pp. 2-6, http://defense.ge-ip.com/gpgpu
10 Mark Harris, "Optimizing Parallel Reduction in CUDA, NVIDIA," 2007, pp. 7-37.