OpenMP has been very successful in exploiting structured parallelism in applications. With increasing application complexity, there is a growing need for addressing irregular parallelism in the presence of complicated control structures. This is evident in various efforts by the industry and research communities to provide a solution to this challenging problem. One of the primary goals of OpenMP 3.0 is to define a standard dialect to express and efficiently exploit unstructured parallelism. This paper presents the design of the OpenMP tasking model by members of the OpenMP 3.0 tasking sub-committee which was formed for this purpose. The paper summarizes the efforts of the sub-committee (spanning over two years) in designing, evaluating and seamlessly integrating the tasking model into the OpenMP specification. In this paper, we present the design goals and key features of the tasking model, including a rich set of examples and an in-depth discussion of the rationale behind various design choices. We compare a prototype implementation of the tasking model with existing models, and evaluate it on a wide range of applications. The comparison shows that the OpenMP tasking model provides expressiveness, flexibility, and huge potential for performance and scalability.

Packet classification plays an important role in next-generation Internet routers in providing various services such as packet filtering, policy routing, traffic policing, and load balancing. In this paper, we propose an original field-level trie classification (FLTC) scheme that accommodates classifier (rule database) with multiple fields specified in different forms (prefix and range). The FLTC achieves a high classification speed with reasonable storage. It is also highly scalable regarding the size and the number of fields of classifiers.

In order to provide Java with the ability for supporting scientific parallel computing, we introduce a data parallel extension to Java language with runtime system support. We provide the distributed array extension to Java, and discuss the related operation and control over the new distributed array. Communication involving distributed arrays are handles through a standard of a collective communication library. We consider the programming in a Single Program Multiple Data (SPMD) model.

The NPAC kernel runtime, developed in the PCRC (Parallel Compiler Runtime Consortium) project, is a runtime library with special support for the High Performance Fortran data model. It provides array descriptors for a generalized class of HPF like distributed arrays, support for parallel access to their elements, and a rich library of collective communication and arithmetic operations for manipulating these arrays. The library has been successfully used as a component in experimental HPF translation systems. With prospects for early appearance of fully featured, efficient HPF compilers looking questionable, we discuss a class of more easily implementable data parallel language extensions that preserve many of the attractive features of HPF, while providing the programmer with direct access to runtime libraries such as the NPAC PCRC kernel.

We outline an extension of Java for programming with distributed arrays. The basic programming style is Single Program Multiple Data (SPMD), but parallel arrays are provided as new language primitives. Further extensions include three distributed control constructs, the most important being a data-parallel loop construct. Communications involving distributed arrays are handled through a standard library of collective operations. Because the underlying programming model is SPMD programming, direct calls to MPI or other communication packages are also allowed in an HPJava program. © 1998 John Wiley & Sons, Ltd.