Dynamic Analysis of Large-Scale Systems

Dynamic Analysis of Mechanical Systems With Intermittent Motion

Journal of Mechanical Design ◽

10.1115/1.3256436 ◽

1982 ◽

Vol 104 (4) ◽

pp. 778-784 ◽

Cited By ~ 36

Author(s):

R. A. Wehage ◽

E. J. Haug

Keyword(s):

Dynamic Analysis ◽

Numerical Integration ◽

Large Scale ◽

Mechanical Systems ◽

Integration Algorithm ◽

Large Scale Systems ◽

Jump Discontinuities ◽

Impulsive Forces ◽

Computer Generation ◽

Intermittent Motion

A method is presented for dynamic analysis of systems with impulsive forces, impact, discontinuous constraints, and discontinuous velocities. A method of computer generation of the equations of planar motion and impulse-momentum relations that define jump discontinuities in system velocity for large scale systems is presented. An event predictor, working in conjunction with a new numerical integration algorithm, efficiently controls the numerical integration and allows for automatic equation reformulation. A weapon mechanism and a trip plow are simulated using the method to illustrate its capabilities.

Download Full-text

Dynamic Analysis and Profiling of Multithreaded Systems

Designing Software-Intensive Systems ◽

10.4018/978-1-59904-699-0.ch010 ◽

2009 ◽

pp. 290-334

Author(s):

Daniel G. Waddington ◽

Nilabja Roy ◽

Douglas C. Schmidt

Keyword(s):

Operating System ◽

Dynamic Analysis ◽

Large Scale ◽

Behavioral Analysis ◽

Software Systems ◽

Large Scale Systems ◽

Advantages And Disadvantages ◽

Model Complex ◽

Basic Approaches ◽

External Stimuli

As software-intensive systems become larger, more parallel, and more unpredictable the ability to analyze their behavior is increasingly important. There are two basic approaches to behavioral analysis: static and dynamic. Although static analysis techniques, such as model checking, provide valuable information to software developers and testers, they cannot capture and predict a complete, precise, image of behavior for large-scale systems due to scalability limitations and the inability to model complex external stimuli. This chapter explores four approaches to analyzing the behavior of software systems via dynamic analysis: compiler-based instrumentation, operating system and middleware profiling, virtual machine profiling, and hardware-based profiling. We highlight the advantages and disadvantages of each approach with respect to measuring the performance of multithreaded systems and demonstrate how these approaches can be applied in practice.

Download Full-text

Dynamic Analysis and Profiling of Multithreaded Systems

Advanced Operating Systems and Kernel Applications ◽

10.4018/978-1-60566-850-5.ch009 ◽

2011 ◽

pp. 156-199 ◽

Cited By ~ 1

Author(s):

Daniel G. Waddington ◽

Nilabja Roy ◽

Douglas C. Schmidt

Keyword(s):

Operating System ◽

Dynamic Analysis ◽

Large Scale ◽

Behavioral Analysis ◽

Software Systems ◽

Large Scale Systems ◽

Advantages And Disadvantages ◽

Model Complex ◽

Basic Approaches ◽

External Stimuli

As software-intensive systems become larger, more parallel, and more unpredictable the ability to analyze their behavior is increasingly important. There are two basic approaches to behavioral analysis: static and dynamic. Although static analysis techniques, such as model checking, provide valuable information to software developers and testers, they cannot capture and predict a complete, precise, image of behavior for large-scale systems due to scalability limitations and the inability to model complex external stimuli. This chapter explores four approaches to analyzing the behavior of software systems via dynamic analysis: compiler-based instrumentation, operating system and middleware profiling, virtual machine profiling, and hardware-based profiling. We highlight the advantages and disadvantages of each approach with respect to measuring the performance of multithreaded systems and demonstrate how these approaches can be applied in practice.

Download Full-text