Considerations on the insularity of performance evaluation
IEEE Transactions on Software Engineering
Analyzing parallel program executions using multiple views
Journal of Parallel and Distributed Computing - Special issue: software tools for parallel programming and visualization
Quartz: a tool for tuning parallel program performance
SIGMETRICS '90 Proceedings of the 1990 ACM SIGMETRICS conference on Measurement and modeling of computer systems
An integrated environment for programming distributed memory multiprocessors
EDMCC2 Proceedings of the 2nd European conference on Distributed memory computing
IPS-2: The Second Generation of a Parallel Program Measurement System
IEEE Transactions on Parallel and Distributed Systems
Software engineering for parallel systems: the TRAPPER approach
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
The monitoring facilities of the graphical parallel programming environment TRAPPER
PDP '95 Proceedings of the 3rd Euromicro Workshop on Parallel and Distributed Processing
Performance-steered design of software architectures for embedded multicore systems
Software—Practice & Experience
A language approach to high performance computing on heterogeneous networks
Progress in computer research
PVMbuilder - A Tool for Parallel Programming
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
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The Trapper software-engineering environment for parallel and distributed systems comprises tools for software design, hardware configuration, mapping, monitoring, visualization, and performance tuning of parallel applications and systems. This article's authors used Trapper as the main software-development tool to realize a complex industrial research application within the Prometheus collision-avoidance project at Daimler-Benz. In this project, they outfitted a Mercedes 500 SEL research vehicle with 18 cameras and approximately 60 computing nodes for various image-processing and control algorithms such as lane following, obstacle detection, and traffic-sign recognition. This article seeks to analyze the behavior and improve the performance of the most time-consuming task of the whole software: traffic-sign recognition. The authors show how Trapper effectively detected and eliminated performance bottlenecks of this parallel application.