Masami Matsumoto

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In this paper, we introduce a new framework called PSE Park for constructing a Problem Solving Environment (PSE); it enables us to construct PSEs easily. PSE Park outputs PSEs depending on user’s demand/input. In this sense, PSE Park is a kind of PSE for PSE, and helps users to construct PSEs. PSE Park consists of four engines: PIPE server, core,(More)
A problem-solving environment (PSE) for a distributed high-performance computing (HPC) is proposed to help users to work on distributed computer environment. When users access and use distributed computers for scientific computations, the PSE tells users which computers are available and appropriate for their specific application software by using hardware(More)
In this paper, we introduce a new framework called PSE Park for constructing a Problem Solving Environment (PSE); it enables us to construct PSEs easily. PSE Park outputs PSEs depending on user's demand/input. In this sense, PSE Park is a kind of PSE for PSE, and helps users to construct PSEs. PSE Park consists of four engines: PIPE server, core,(More)
Uncertainty in scientific computing is discussed in this paper. Uncertainty comes from various sources, for example, from physical model uncertainty, mathematical model errors, unknown input data, numerical model errors, insufficient numerical precision, round-off error, floating point precision, programming errors, data processing errors or uncertainty,(More)
In this paper, we describe implementations of PSE Park engines especially for batch function. PSE Park is a meta-PSE on Cloud to perform a PSE construction support for the scientific and technological simulation by using distributed machines. PSE Park is a framework that consists of five engines of Console, Core, PIPE Server, Manager and x4u. Meta-PSE is(More)
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