Requisite Computer Science Efforts

Among the current limitations of Linda, those important to computational chemistry applications are failure to provide information on where or how tuples are stored or accessed lack of structure within tuple space, making it hard to maintain modularity a requirement to match general tuples, leading to inefficiencies in memory usage and communication even for simple data structures (e.g., a distributed array) lack of primitives for efficient global operations (e.g., reduction and broadcast) and requiring the compiler to detect and optimize these constructs. There are many current directions of related research. 

In prior sections of this chapter, we discussed a variety of programming languages, as well as program structuring techniques that have been found useful for writing parallel computational chemistry codes. Some parallel systems are described in the chapter appendix. In this section, we delve more deeply into the more specific topics of  

Message passing, collective communication, and similar alternatives for programming software libraries for large-scale applications on distributed-memory computer systems. 

It is worth noting that these topics are more often discussed in the computer science and applied math communities than in computational chemistry. Some groups, such as ours, find that collaboration between computational chemists and computer scientists is a good way to leverage both groups expertise and experience. 

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