Concurrent object-oriented programming (COOP) languages focus the abstraction and encapsulation power of abstract data types on the problem of concurrency control. In particular, pure fine-grained concurrent object-oriented languages (as opposed to hybrid or data parallel) provides the programmer with a simple, uniform, and flexible model while exposing maximum concurrency. While such languages promise to greatly reduce the complexity of large-scale concurrent programming, the popularity of these languages has been hampered by efficiency which is often many orders of magnitude less than that of comparable sequential code. We present a sufficiency set of techniques which enables the efficiency of fine-grained concurrent object-oriented languages to equal that of traditional sequential languages (like C) when the required data is available. These techniques are empirically validated by the application to a COOP implementation of the Livermore Loops.

While fine-grained concurrent languages can naturally capture concurrency in many irregular and dynamic problems, their flexibility has generally resulted in poor execution effciency. In such languages the computation consists of many small threads which are created dynamically and synchronized implicitly. In order to minimize the overhead of these operations, we propose a hybrid execution model which dynamically adapts to runtime data layout, providing both sequential efficiency and low overhead parallel execution. This model uses separately optimized sequential and parallel versions of code. Sequential efficiency is obtained by dynamically coalescing threads via stack-based execution and parallel efficiency through latency hiding and cheap synchronization using heap-allocated activation frames. Novel aspects of the stack mechanism include handling return values for futures and executing forwarded messages (the responsibility to reply is passed along, like call/cc in Scheme) on the stack. In addition, the hybrid execution model is expressed entirely in C, and therefore is easily portable to many systems. Experiments with function-call intensive programs show that this model achieves sequential efficiency comparable to C programs. Experiments with regular and irregular application kernels on the CM5 and T3D demonstrate that it can yield 1.5to 3 times better performance than code optimized for parallel execution alone.

Concrete type information is invaluable for program optimization. The determination of concrete types in object-oriented languages is a flow sensitive global data flow problem. It is made difficult by dynamic dispatch (virtual function invocation) and first class functions (and selectors)—the very program structures for whose optimization its results are most critical. Previous work has shown that constraint-based type inference systems can be used to safely approximate concrete types [15], but their use can be expensive and their results imprecise.