In this paper, we describe the Eureka Programming Model (EuPM) that simplifies the expression of speculative parallel tasks, and is especially well suited for parallel search and optimization applications. The focus of this work is to provide a clean semantics for, and efficiently support, such "eureka-style" computations (EuSCs) in general structured task parallel programming models. In EuSCs, a eureka event is a point in a program that announces that a result has been found. A eureka triggered by a speculative task can cause a group of related speculative tasks to become redundant, and enable them to be terminated at well-defined program points. Our approach provides a bound on the additional work done in redundant speculative tasks after such a eureka event occurs. We identify various patterns that are supported by our eureka construct, which include search, optimization, convergence, and soft real-time deadlines. These different patterns of computations can also be safely combined or nested in the EuPM, along with regular task-parallel constructs, thereby enabling high degrees of composability and reusability. As demonstrated by our implementation, the EuPM can also be implemented efficiently. We use a cooperative runtime that uses delimited continuations to manage the termination of redundant tasks and their synchronization at join points. In contrast to current approaches, EuPM obviates the need for cumbersome manual refactoring by the programmer that may (for example) require the insertion of if checks and early return statements in every method in the call chain. Experimental results show that solutions using the EuPM simplify programmability, achieve performance comparable to hand-coded speculative task-based solutions and out-perform non-speculative task-based solutions.

This paper introduces a unified concurrent programming model combining the previously developed Actor Model (AM) and the task-parallel Async-Finish Model (AFM). With the advent of multi-core computers, there is a renewed interest in programming models that can support a wide range of parallel programming patterns. The proposed unified model shows how the divide-and-conquer approach of the AFM and the no-shared mutable state and event-driven philosophy of the AM can be combined to solve certain classes of problems more efficiently and productively than either of the aforementioned models individually. The unified model adds actor creation and coordination to the AFM, while also enabling parallelization within actors. This paper describes two implementations of the unified model as extensions of Habanero-Java and Habanero-Scala. The unified model adds to the foundations of parallel programs, and to the tools available for the programmer to aid in productivity and performance while developing parallel software.