Matches in UGent Biblio for { <https://biblio.ugent.be/publication/361372#aggregation> ?p ?o. }
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- aggregation classification "A1".
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation creator person.
- aggregation date "2004".
- aggregation format "application/pdf".
- aggregation hasFormat 361372.bibtex.
- aggregation hasFormat 361372.csv.
- aggregation hasFormat 361372.dc.
- aggregation hasFormat 361372.didl.
- aggregation hasFormat 361372.doc.
- aggregation hasFormat 361372.json.
- aggregation hasFormat 361372.mets.
- aggregation hasFormat 361372.mods.
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- aggregation hasFormat 361372.txt.
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- aggregation hasFormat 361372.yaml.
- aggregation isPartOf urn:isbn:1-58113-831-8.
- aggregation isPartOf urn:issn:0362-1340.
- aggregation language "eng".
- aggregation rights "I have transferred the copyright for this publication to the publisher".
- aggregation subject "Technology and Engineering".
- aggregation title "Method-level phase behavior in Java workloads".
- aggregation abstract "Java workloads are becoming more and more prominent on various computing devices. Understanding the behaviour of a Java workload which includes the interaction between the application and the virtual machine (VM), is thus of primary importance during performance analysis and optimization. Moreover, as contemporary software projects are increasing in complexity, automatic performance analysis techniques are indispensable. This paper proposes an off-line method-level phase analysis approach for Java workloads that consists of three steps. In the first step, the execution time is computed for each method invocation. Using an off-line tool, we subsequently analyze the dynamic call graph (that is annotated with the method invocations' execution times) to identify method-level phases. Finally, we measure performance characteristics for each of the selected phases. This is done using hardware performance monitors. As such, our approach allows for linking microprocessor-level information at the individual methods in the Java application's source code. This is extremely interesting information during performance analysis and optimization as programmers can use this information to optimize their code. We evaluate our approach in the Jikes RVM on an IA-32 platform using the SPECjvm98 and SPECjbb2000 benchmarks. This is done according to a number of important criteria: the overhead during profiling, the variability within and between the phases, its applicability in Java workload characterization (measuring performance characteristics of the various VM components) and application bottleneck identification.".
- aggregation authorList BK854046.
- aggregation endPage "287".
- aggregation issue "10".
- aggregation startPage "270".
- aggregation volume "39".
- aggregation aggregates 1000278.
- aggregation isDescribedBy 361372.
- aggregation similarTo 1028976.1028999.
- aggregation similarTo LU-361372.