Concurrent ML (CML) is a high-level message-passing language that supports the construction of first-class synchronous abstractions called events. This mechanism has proven quite effective over the years and has been incorporated in a number of other languages. While CML provides a concurrent programming model, its implementation has always been limited to uniprocessors. This limitation is exploited in the implementation of the synchronization protocol that underlies the event mechanism, but with the advent of cheap parallel processing on the desktop (and laptop), it is time for Parallel CML.

Concurrent ML (CML) is a statically-typed higher-order concurrent language that is embedded in Standard ML. Its most notable feature is its support for first-class synchronous operations. This mechanism allows programmers to encapsulate complicated communication and synchronization protocols as first-class abstractions, which encourages a modular style of programming where the underlying channels used to communicate with a given thread are hidden behind data and type abstraction.While CML has been in active use for well over a decade, little attention has been paid to optimizing CML programs. In this paper, we present a new program analysis for statically-typed higher-order concurrent languages that enables the compile-time specialization of communication operations. This specialization is particularly important in a multiprocessor or multicore setting, where the synchronization overhead for general-purpose operations are high. Preliminary results from a prototype that we have built demonstrate that specialized channel operations are much faster than the general-purpose operations.Our analysis technique is modular (i.e.,, it analyzes and optimizes a single unit of abstraction at a time), which plays to the modular style of many CML programs. The analysis consists of three steps: the first is a type-sensitive control-flow analysis that uses the program's type-abstractions to compute more precise results. The second is the construction of an extended control-flow graph using the results of the CFA. The last step is an iterative analysis over the graph that approximates the usage patterns of known channels. Our analysis is designed to detect special patterns of use, such as one-shot channels, fan-in channels, and fan-out channels. We have proven the safety of our analysis and state those results.