Abstract. In this paper, we present a communication library which extends an MPI application on a single parallel machine to a cluster of parallel machines. Stampi provides some functionality which are required for constructing distributed applications and environments based on the MPI2 standard with a focus on dynamic process management. Since the mechanism of communication bridge is transparent for users, it is very useful to assemble and link MPI applications on meta-computer systems. Furthermore Stampi supports novel functions; one is the communication between a Java applet to the backend parallel computer. Another is supporting remote file-IO. Both give us a framework of distributed resource management based on an MPI communication infrastructure. This paper covers the architecture of Stampi.

When carrying out scientific research, the first step is to acquire relevant papers. It is easy to grab vast numbers of papers by inputting a keyword into a digital library or an online search engine. However, reading all the retrieved papers to find the most relevant ones is agonizingly time-consuming. Previous works have tried to improve paper search by clustering papers with their mutual similarity based on reference relations, including limited use of the type of citation (e.g. providing background vs. using specific method or data). However, previously proposed methods only classify or organize the papers from one point of view, and hence not flexible enough for user or context-specific demands. Moreover, none of the previous works has built a practical system based on a paper database. In this paper, we first establish a paper database from an open-access paper source, then use machine learning to automatically predict the reason for each citation between papers, and finally visualize the resulting information in an application system to help users more efficiently find the papers relevant to their personal uses. User studies employing the system show the effectiveness of our approach.

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