In this paper a review is presented on the PSE (Problem Solving Environment) concept in computational engineering and science. In the PSE concept, human concentrates on target problems and works out solutions, and a part of application of solutions, which can be solved mechanically, is performed by computers or machines or software. PSE provides integrated human friendly innovative computational services and facilities for easy incorporation of novel solution methods to solve a target class of problems. PSE is an innovative concept to enrich our e-Science, e-Life, e-Engineering, e-Production, e-Commerce, e-Home, etc. The PSE-relating studies were started in 1970’s to provide a higher-level programming language than Fortran, etc. in scientific computations. The trend at that time was natural to deliver more human-friendly programming environment, and was resulting in PSE, CAE (Computer Assisted Engineering), libraries, etc. At present PSE covers a rather wide area, for example, program generation support PSEs, education support PSEs, CAE software learning support PSEs, Grid/Cloud computing support PSEs, job execution support PSEs, e-Learning support PSEs, etc. This review paper includes the PSE definition, a brief history of PSE, example PSE study activities, uncertainty management PSE and a future research directions in PSE.
In the history of self-learning, a lot of mechanisms have been proposed and used. "e-learning" has been attracted because it has a possibility to support a self-learning by the use of computer systems. Also "e-learning" does not restrict both time and place of the learner. However, it has been revealed that "e-learning" alone does not keep the motivation of learners. (exceptions: those who can maintain their motivation by themselves.) In order to keep the motivation of learners, we have developed the several PSEs by the use of TIES (= Tezukayama Internet Educational Service) in which both e-learning and e-teaching are available.
In this paper, we propose a MPI-based framework and a library for geospatial vector data processing to perform efficient load balancing in heterogeneous distributed systems and hide MPI programming. Our experimental results show that our proposed framework is up to 1.63 times faster than MR4C, which uses Hadoop YARN for the load balancing. Also, the number of code steps of geospatial vector data processing with our library, which hides the MPI programming, smaller than that of the MR4C.
Computer aided engineering (CAE) is an essential in the engineering field. Various CAE softwares have been developed in science and engineering. Recently non-expert users have opportunities to use the CAE tools. In this study, we developed a platform and the related base modules to solve target problems by CAEs. The modules visualize 3D models by WebGL (Web Graphics Library) through general web browsers. Additionally, we have constructed a problem solving environment (PSE) for CAE by integrating the platform and the basic modules. The PSE for CAE was developed based on open-source softwares. Since the PSE is a web-based service, every user can use any devices which have a web browser. Therefore, the PSE is a useful tool for non-expert and educational CAE users.