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. Parallel implementations of CML-like primitives for Java and Haskell exist, but build on high-level synchronization constructs that are unlikely to perform well. This paper presents a novel, parallel implementation of CML that exploits a purpose-built optimistic concurrency protocol designed for both correctness and performance on shared-memory multiprocessors. This work extends and completes an earlier protocol that supported just a strict subset of CML with synchronization on input, but not output events. Our main contributions are a model-checked reference implementation of the protocol and two concrete implementations. This paper focuses on Manticore’s functional, continuation-based implementation but briefly discusses an independent, thread-based implementation written in C # and running on Microsoft’s stock, parallel runtime. Although very different in detail, both derive from the same design. Experimental evaluation of the Manticore implementation reveals good performance, dispite the extra overhead of multiprocessor synchronization.

The trend in microprocessor design toward multicore and manycore processors means that future performance gains in software will largely come from harnessing parallelism. To realize such gains, we need languages and implementations that can enable parallelism at many different levels. For example, an application might use both explicit threads to implement course-grain parallelism for independent tasks and implicit threads for fine-grain data-parallel computation over a large array. An important aspect of this requirement is supporting a wide range of different scheduling mechanisms for parallel computation. In this paper, we describe the scheduling framework that we have designed and implemented for Manticore, a strict parallel functional language. We take a micro-kernel approach in our design: the compiler and runtime support a small collection of scheduling primitives upon which complex scheduling policies can be implemented. This framework is extremely flexible and can support a wide range of different scheduling policies. It also supports the nesting of schedulers, which is key to both supporting multiple scheduling policies in the same application and to hierarchies of speculative parallel computations. In addition to describing our framework, we also illustrate its expressiveness with several popular scheduling techniques. We present a (mostly) modular approach to extending our schedulers to support cancellation. This mechanism is essential for implementing eager and speculative parallelism. We finally evaluate our framework with a series of benchmarks and an analysis.

Abstract. 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. We are pursuing such an implementation as part of the Manticore project. In this paper, we describe a parallel implementation of Asymmetric CML (ACML), which is a subset of CML that does not support output guards. We describe an optimistic concurrency protocol for implementing CML synchronization. This protocol has been implemented as part of the Manticore system. 1

Abstract. Clocks are a mechanism for providing synchronization barriers in concurrent programming languages. They are usually implemented using primitive communication mechanisms and thus spare the programmer from reasoning about low-level implementation details such as remote procedure calls and error conditions. Clocks provide flexibility, but programs often use them in specific ways that do not require their full implementation. In this paper, we describe a tool that mitigates the overhead of general-purpose clocks by statically analyzing how programs use them and choosing optimized implementations when available. We tackle the clock implementation in the standard library of the X10 programming language—a parallel, distributed object-oriented language. We report our findings for a small set of analyses and benchmarks. Our tool only adds a few seconds to analysis time, making it practical to use as part of a compilation chain.

Recent research on Distributed Event-based Systems (DEBS) has focussed on event correlation, which is the task of processing events to identify meaningful patterns of events in the event cloud. In DEBS, software components communicate by generating, disseminating and receiving event notifications, which reify and describe the event. Several parts of an event notification are context-sensitive, depending on where the software component producing the event is deployed, the communication infrastructure available for event dissemination etc. Event contexts may be added during the production of an event (e.g. by the runtime system executing the component) or during dissemination (by a middleware) and play an integral part in event correlation. Examples of contextual information include physical time, logical time, geographical coordinates, information about the source of events, digital signatures, etc. EventJava [7], an extension of Java with advanced support for event correlation, explicitly integrates the notion of event context, thereby allowing a programmer to customize the way in which events are ordered, propagated and correlated with other events. In this paper, we explain why contexts are indispensable to DEBS, present an overview of EventJava and illustrate the use of contexts through programming examples.

Parallelism is going mainstream. Many chip manufactures are turning to multicore processor designs rather than scalar-oriented frequency increases as a way to get performance in their desktop, enterprise, and mobile processors. This endeavor is not likely to succeed long term if mainstream applications cannot be parallelized to take advantage of tens and eventually hundreds of hardware threads. Multicore architectures will differ in significant ways from their multisocket predecessors. For example, the communication to compute bandwidth ratio is likely to be higher, which will positively impact performance. More generally, multicore architectures introduce several new dimensions of variability in both performance guarantees and architectural contracts, such as the memory model, that may not stabilize for several generations of product. Programs written in functional or (constraint-)logic programming languages, or even in other languages with a controlled use of side effects, can greatly simplify parallel programming. Such declarative programming allows for a deterministic semantics even when the underlying implementation might be highly