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국방정보시스템 성능향상을 위한 효율적인 GPU적용방안 연구

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

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초록

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.

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.

참고문헌 (10)

  1. 이승학, "GPU를 이용한 SDR 시스템 용 LTE MIMO 기지국 기능 구현", (사)디지털산업정보학회 논문지, 8(4), pp.91~98 (2012), (사)디지털산업정보학회
  2. 이윤혁, "GPGPU기반의 디지털 홀로그램 콘텐츠의 고속 생성 기법", (사)디지털산업정보학회 논문지, 9(1), pp.151~163 (2013), (사)디지털산업정보학회
  3. 고장혁, "GPU를 이용한 정보시스템 성능향상에 관한 연구", 한국군사과학기술학회 종합학술대회 (2013)
  4. "GPGPU COTS Platforms - High-Performance Computing Solutions"[URL]
  5. Optimizing Parallel Reduction in CUDA, "Mark Harris", NVIDIA, 7~37 (2007)
  6. "CUDPP: CUDA data parallel primitives library"[URL]
  7. Jeff A. Stuart, "Multi-GPU MapReduce on GPU Clusters", IEEE International Parallel & Distributed Processing Symposium, pp.1068~1079 (2011)
  8. Reza Farivar, "MITHRA: Multiple data Independent Tasks on a Heterogeneous Resource Architecture", IEEE Cluster Computing and Workshops (2009)
  9. "c2cudatranslator: Automatic conversion of source code for C to CUDA C"[URL]
  10. "786/1280 Byte Table AES C byte-implementation 03 OCT"[URL]

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