Proceedings of the 2006 ACM Symposium on Software Visualization - SoftVis '06 2006
DOI: 10.1145/1148493.1148541
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Visualized adaptive runtime subsystems

Abstract: Virtual execution platforms contain many runtime subsystems that are invoked on-demand as user code executes. We have instrumented an open-source JVM to dump out information on these runtime subsystems, in order to examine how they are used. The monitored components are known as visualized adaptive runtime subsystems. The visualizations in this poster demonstrate five interesting properties of such runtime subsystems.

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Cited by 3 publications
(2 citation statements)
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“…A follow up system focuses on more specific user abstractions [264]. Another system [303] focuses on virtual machine code (IBM's Jikes RVM) and how to optimise it as opposed to user code. DYPER [265,266,267] does controlled performance analysis of Java systems and can obtain a variety of performance metrics including CPU usage, IO, sockets, heap utilization, memory allocations, phase analysis, and reaction analysis.…”
Section: Execution Trace Visualizationmentioning
confidence: 99%
“…A follow up system focuses on more specific user abstractions [264]. Another system [303] focuses on virtual machine code (IBM's Jikes RVM) and how to optimise it as opposed to user code. DYPER [265,266,267] does controlled performance analysis of Java systems and can obtain a variety of performance metrics including CPU usage, IO, sockets, heap utilization, memory allocations, phase analysis, and reaction analysis.…”
Section: Execution Trace Visualizationmentioning
confidence: 99%
“…A follow up system focuses on more specific user abstractions [264]. Another system [303] focuses on virtual machine code (IBM's Jikes RVM) and how to optimise it as opposed to user code. DYPER [265,266,267] does controlled performance analysis of Java systems and can obtain a variety of performance metrics including CPU usage, IO, sockets, heap utilization, memory allocations, phase analysis, and reaction analysis.…”
Section: Execution Trace Visualizationmentioning
confidence: 99%