Simulation can be an important step in the development of software for wireless sensor networks and has been the subject of intense research in the past decade. While most previous efforts in simulating wireless sensor networks have focused on protocol-level issues utilizing models of the software implementation, a significant challenge remains in precisely measuring time-dependent properties such as radio channel utilization. One promising approach, first demonstrated by ATEMU, is to simulate the behavior of sensor network programs at the machine code level with cycle-accuracy, but poor performance has so far limited its scalability. In this paper we present Avrora, a cycle-accurate instructionlevel sensor network simulator which scales to networks of up to 10,000 nodes and performs as much as 20 times faster than previous simulators with equivalent accuracy, handling as many as 25 nodes in real-time. We show how an event queue can enable efficient instruction-level simulation of microcontroller programs and allow the hidden parallelism in finegrained sensor network simulations to be extracted, once two core synchronization problems are identified and solved. Avrora's ability to measure detailed time-critical phenomena can shed new light on design issues for large-scale sensor networks.

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We present a new approach to inferring types in untyped object-oriented programs with inheritance, assignments, and late binding. It guarantees that all messages are understood, annotates the program with type information, allows polymorphic methods, and can be used as the basis of an optimizing compiler.Types are finite sets of classes and subtyping is set inclusion. Using a trace graph, our algorithm constructs a set of conditional type constraints and computes the least solution by least fixed-point derivation.

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We present a new approach to inferring types in untyped object-oriented programs with inheritance, assignments, and late binding. It guarantees that all messages are understood, annotates the program with type information, allows polymorphic methods, and can be used as the basis of an optimizing compiler.Types are finite sets of classes and subtyping is set inclusion. Using a trace graph, our algorithm constructs a set of conditional type constraints and computes the least solution by least fixed-point derivation.

We introduce the Concurrent Collections (CnC) programming model. CnC supports flexible combinations of task and data parallelism while retaining determinism. CnC is implicitly parallel, with the user providing high-level operations along with semantic ordering constraints that together form a CnC graph. We formally describe the execution semantics of CnC and prove that the model guarantees deterministic computation. We evaluate the performance of CnC implementations on several applications and show that CnC offers performance and scalability equivalent to or better than that offered by lower-level parallel programming models.

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Flow analyses of untyped higher-order functional programs have in the past decade been presented by Ayers, Bondorf, Consel, Jones, Heintze, Sestoft, Shivers, Steckler, Wand, and others. The analyses are usually defined as abstract interpretations and are used for rather different tasks such as type recovery, globalization, and binding-time analysis. The analyses all contain a global closure analysis that computes information about higher-order control-flow. Sestoft proved in 1989 and 1991 that closure analysis is correct with respect to call-by-name and call-by-value semantics, but it remained open if correctness holds for arbitrary beta-reduction. This article answers the question; both closure analysis and others are correct with respect to arbitrary beta-reduction. We also prove a subject-reduction result: closure information is still valid after beta-reduction. The core of our proof technique is to define closure analysis using a constraint system. The constraint system is equivalent to the closure analysis of Bondorf, which in turn is based on Sestoft's.