14,239 research outputs found
Recommended from our members
Computer-aided programming for multiprocessing systems
As both the number of processors and the complexity of problems to be solved increase, programming multiprocessing systems becomes more difficult and error-prone. This report discusses parallel models of computation and tools for computer-aided programming (CAP). Program development tools are necessary since programmers are not able to develop complex parallel programs efficiently. In particular, a CAP tool, named Hypertool, is described here. It performs scheduling and handles the communication primitive insertion automatically so that many errors are eliminated. It also generates the performance estimates and other program quality measures to help programmers in improving their algorithms and programs. Experiments have shown that up to a 300% performance improvement can be achieved by computer-aided programming
A methodology for producing reliable software, volume 1
An investigation into the areas having an impact on producing reliable software including automated verification tools, software modeling, testing techniques, structured programming, and management techniques is presented. This final report contains the results of this investigation, analysis of each technique, and the definition of a methodology for producing reliable software
Mira: A Framework for Static Performance Analysis
The performance model of an application can pro- vide understanding about its
runtime behavior on particular hardware. Such information can be analyzed by
developers for performance tuning. However, model building and analyzing is
frequently ignored during software development until perfor- mance problems
arise because they require significant expertise and can involve many
time-consuming application runs. In this paper, we propose a fast, accurate,
flexible and user-friendly tool, Mira, for generating performance models by
applying static program analysis, targeting scientific applications running on
supercomputers. We parse both the source code and binary to estimate
performance attributes with better accuracy than considering just source or
just binary code. Because our analysis is static, the target program does not
need to be executed on the target architecture, which enables users to perform
analysis on available machines instead of conducting expensive exper- iments on
potentially expensive resources. Moreover, statically generated models enable
performance prediction on non-existent or unavailable architectures. In
addition to flexibility, because model generation time is significantly reduced
compared to dynamic analysis approaches, our method is suitable for rapid
application performance analysis and improvement. We present several scientific
application validation results to demonstrate the current capabilities of our
approach on small benchmarks and a mini application
Evaluating testing methods by delivered reliability
There are two main goals in testing software: (1) to achieve adequate quality (debug testing), where the objective is to probe the software for defects so that these can be removed, and (2) to assess existing quality (operational testing), where the objective is to gain confidence that the software is reliable. Debug methods tend to ignore random selection of test data from an operational profile, while for operational methods this selection is all-important. Debug methods are thought to be good at uncovering defects so that these can be repaired, but having done so they do not provide a technically defensible assessment of the reliability that results. On the other hand, operational methods provide accurate assessment, but may not be as useful for achieving reliability. This paper examines the relationship between the two testing goals, using a probabilistic analysis. We define simple models of programs and their testing, and try to answer the question of how to attain program reliability: is it better to test by probing for defects as in debug testing, or to assess reliability directly as in operational testing? Testing methods are compared in a model where program failures are detected and the software changed to eliminate them. The “better” method delivers higher reliability after all test failures have been eliminated. Special cases are exhibited in which each kind of testing is superior. An analysis of the distribution of the delivered reliability indicates that even simple models have unusual statistical properties, suggesting caution in interpreting theoretical comparisons
- …