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Abstract: This paper deals with the analysis of the parallelization of irregular reductions, a frequent operation found in many irregular applications, in the context of a shared-memory model. Locality exploitation is a classical problem in computer architecture and compiler design that presently still plays a fundamental role due to the growing gap between memory and processor speeds. This work contributes a formal description of the design space of locality-based parallel irregular reductions, which is used to achieve a quantitative analysis of them. The model allows to determine behavioural aspects in the methods that may influence their performance. Several parallel compiler techniques for irregular reductions are placed and examined within this model. Experimental evaluation is also presented, under various locality conditions, and the results were compared to those derived from the model. [Copyright &y& Elsevier]

More and more parallel applications are running in a distributed environment to take advantage of easily available and inexpensive commodity resources. For data intensive applications, employing multiple distributed storage resources has many advantages. In this paper, we present a Multi-Storage I/O System (MS-I/O) that cannot only effectively manage various distributed storage resources in the system, but also provide novel high performance storage access schemes. MS-I/O employs many state-of-the-art I/O optimizations such as collective I/O, asynchronous I/O etc. and a number of new techniques such as data location, data replication, subfile, superfile and data access history. In addition, many MS-I/O optimization schemes can work simultaneously within a single data access session, greatly improving the performance.Although I/O optimization techniques can help improve performance, it also complicates I/O system. In addition, most optimization techniques have their limitations. Therefore, selecting accurate optimization policies requires expert knowledge which is not suitable for end users who may have little knowledge of I/O techniques. So the task of I/O optimization decision should be left to the I/O system itself, that is, automatic from user’s point of view. We present a User Access Pattern data structure which is associated with each dataset that can help MS-I/O easily make accurate I/O optimization decisions. [Copyright &y& Elsevier]