Sprachen für parallele objektorientierte Programmierung

In letzter Zeit wurden eine ganze Reihe von objektorientierten Sprachen zur parallelen Programmierung entworfen und implementiert. Einige dieser Sprachen werden hier einander gegenübergestellt. Das Hauptaugenmerk der Arbeit liegt einerseits auf den bereitgestellten Konzepten zur Bewältigung der Komplexität, die sich durch die Parallelisierung ergibt, und andererseits auf der Flexibilisierung von Synchronisation und Kommunikation zur Optimierung der Parallelisierbarkeit von Programmausführungen

Directions in parallel programming: HPF, shared virtual memory and object parallelism in pC++

Fortran and C++ are the dominant programming languages used in scientific computation. Consequently, extensions to these languages are the most popular for programming massively parallel computers. We discuss two such approaches to parallel Fortran and one approach to C++. The High Performance Fortran Forum has designed HPF with the intent of supporting data parallelism on Fortran 90 applications. HPF works by asking the user to help the compiler distribute and align the data structures with the distributed memory modules in the system. Fortran-S takes a different approach in which the data distribution is managed by the operating system and the user provides annotations to indicate parallel control regions. In the case of C++, we look at pC++ which is based on a concurrent aggregate parallel model

Applications of agent architectures to decision support in distributed simulation and training systems

This work develops the approach and presents the results of a new model for applying intelligent agents to complex distributed interactive simulation for command and control. In the framework of tactical command, control communications, computers and intelligence (C4I), software agents provide a novel approach for efficient decision support and distributed interactive mission training. An agent-based architecture for decision support is designed, implemented and is applied in a distributed interactive simulation to significantly enhance the command and control training during simulated exercises. The architecture is based on monitoring, evaluation, and advice agents, which cooperate to provide alternatives to the dec ision-maker in a time and resource constrained environment. The architecture is implemented and tested within the context of an AWACS Weapons Director trainer tool. The foundation of the work required a wide range of preliminary research topics to be covered, including real-time systems, resource allocation, agent-based computing, decision support systems, and distributed interactive simulations. The major contribution of our work is the construction of a multi-agent architecture and its application to an operational decision support system for command and control interactive simulation. The architectural design for the multi-agent system was drafted in the first stage of the work. In the next stage rules of engagement, objective and cost functions were determined in the AWACS (Airforce command and control) decision support domain. Finally, the multi-agent architecture was implemented and evaluated inside a distributed interactive simulation test-bed for AWACS Vv\u27Ds. The evaluation process combined individual and team use of the decision support system to improve the performance results of WD trainees. The decision support system is designed and implemented a distributed architecture for performance-oriented management of software agents. The approach provides new agent interaction protocols and utilizes agent performance monitoring and remote synchronization mechanisms. This multi-agent architecture enables direct and indirect agent communication as well as dynamic hierarchical agent coordination. Inter-agent communications use predefined interfaces, protocols, and open channels with specified ontology and semantics. Services can be requested and responses with results received over such communication modes. Both traditional (functional) parameters and nonfunctional (e.g. QoS, deadline, etc.) requirements and captured in service requests

Implementing a Parallel C++ Runtime System for Scalable Parallel Systems

pC++ is a language extension to C++ designed to allow programmers to compose "concurrent aggregate" collection classes which can be aligned and distributed over the memory hierarchy of a parallel machine in a manner modeled on the High Performance Fortran Forum (HPFF) directives for Fortran 90. pC++ allows the user to write portable and efficient code which will run on a wide range of scalable parallel computer systems. The first version of the compiler is a preprocessor which generates Single Program Multiple Data (SPMD) C++ code. Currently, it runs on the Thinking Machines CM-5, the Intel Paragon, the BBN TC2000, the Kendall Square Research KSR-1, and the Sequent Symmetry. In this paper we describe the implementation of the runtime system, which provides the concurrency and communication primitives between objects in a distributed collection. To illustrate the behavior of the runtime system we include a description and performance results on four benchmark programs. 1 Introduction ..

Profiling Concurrent Programs Using Hardware Counters

Concurrency is a programming tool that is widely used in applications. Concurrent user-level threads can be used to structure the execution of a program in a uniprocessor environment and/or speed up its execution in a multiprocessor setting. Unfortunately, threads may interact with each other in unpredictable ways, often leading to performance problems that are nonexistent in the sequential domain. A profiler can be used to help locate performance problems in sequential and concurrent programs. A profiler is a tool that monitors, analyzes, and visualizes the execution performance of a program to help users verify its expected behaviour, and locate its bottlenecks and hotspots. One of the important tools a profiler has at its disposal is a set of hardware counters, which are specialized CPU registers that count the occurrences of hardware events as a program executes. Hardware-event counts provide extremely precise insight into the execution behaviour of a program, and can be used to pinpoint portions of code where performance is suboptimal. This thesis describes the design and implementation of µProfiler, which is a profiler for sequential and concurrent programs written in a concurrent dialect of the C++ programming language called µC++. µC++ offers user-level concurrency in a uniprocessor or multiprocessor shared-memory environment. A new architecture-abstraction layer is developed, which allows µProfiler to access hardware counters on multiple CPU types. As well, two new profiling metrics are presented, which use the architecture-abstraction layer to gather hardware-event counts for µC++ programs. These metrics offer performance information about µC++ programs that is unavailable by any other means