A Consideration of the Achievement of Productive Level Parallel Programming Skills
This paper gives a consideration of the achievement of productive level parallel programming skills, based on the data of the graduation studies in the Polytechnic University of Japan. The data show that most students can achieve only parallel programming skills during the graduation study (about 600 to 700 hours), if the programming environment is limited to GPGPUs. However, the data also show that it is a very high level task that a student achieves productive level parallel programming skills during only the graduation study. In addition, it shows that the parallel programming environments for GPGPU, such as CUDA and OpenCL, may be more suitable for parallel computing education than other environments such as MPI on a cluster system and Cell.B.E. These results must be useful for the areas of not only software developments, but also hardware product developments using computer technologies.
Digital Object Identifier (DOI):
 C. Ivica, J.T. Riley, and C. Shubert. StarHPC – Teaching parallel
programming within elastic compute cloud. Proc. Int’l Conf ITI,
 W.B. Gardner. Third-year parallel programming for CS undergraduates.
Proc. Int’l Conf FECS, pp.8–13, CSREA, 2011.
 (2013) SYOKUGYO DAI homepage. (Online). Available:
 (2012) Open MPI website. (Online). Available:
 (2012) KURO-BOX/HG (in Japanese) homepage. (Online). Available:
 (2012) PLAYSTATION3 Linux Information Site. (Online). Available:
 (2012) CUDA Downloads homepage. (Online). Available:
 (2012) OpenCL homepage. (Online). Available: http://www.khronos.org/
 J.E. Dayhoff. “Neural Network Architectures: An Introduction”. Van
Nostrand Reinhold, 1989.