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Method-level phase behavior in java workloads 04

Method-level phase behavior in Java workloads

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19th Conference on Object-Oriented Programming, Systems, Languages, and Applications

作者： Georges, A Buytaert, D Eeckhout, L De Bosschere, K Univ Ghent Dept Elect & Informat Syst ELIS B-9000 Ghent Belgium

ISBN: (纸本)9781581138313

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.

关键词： measurement performance experimentation workload characterization performance analysis java workloads virtual machine technology

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How java programs interact with virtual machines at the microarchitectural level

How Java programs interact with virtual machines at the micr...

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18th Annual Conference on Object-Oriented Programming, Systems, Languages and Applications (OOPSLA 2003)

作者： Eeckhout, L Georges, A De Bosschere, K Univ Ghent Dept Elect & Informat Syst ELIS B-9000 Ghent Belgium

java workloads are becoming increasingly prominent on various platforms ranging from embedded systems, over general-purpose computers to high-end servers. Understanding the implications of all the aspects involved when running java workloads, is thus extremely important during the design of a system that will run such workloads. In other words, understanding the interaction between the java application, its input and the virtual machine it runs on, is key to a successful design. The goal of this paper is to study this complex interaction at the microarchitectural level, e.g., by analyzing the branch behavior, the cache behavior, etc. This is done by measuring a large number of performance characteristics using performance counters on an AMD K7 Duron microprocessor. These performance characteristics are measured for seven virtual machine configurations, and a collection of java benchmarks with corresponding inputs coming from the SPEGjvm98 benchmark suite, the SPECjbb2000 benchmark suite, the java Grande Forum benchmark suite and an open-source raytracer, called Raja with 19 scene descriptions. This large amount of data is further analyzed using statistical data analysis techniques, namely principal components analysis and cluster analysis. These techniques provide useful insights in an understandable way. From our experiments, we conclude that (i) the behavior observed at the microarchitectural level is primarily determined by the virtual machine for small input sets, e.g., the SPEQjvm98 s1 input set;(ii) the behavior can be quite different for various input sets, e.g., short-running versus long-running benchmarks;(iii) for long-running benchmarks with few hot spots, the behavior can be primarily determined by the java program and not the virtual machine, i.e., all the virtual machines optimize the hot spots to similarly behaving native code;(iv) in general, the behavior of a java application running on one virtual machine can be significantly different from running

关键词： measurement performance experimentation workload characterization performance analysis statistical data analysis java workloads virtual machine technology

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How java programs interact with virtual machines at the microarchitectural level 03

How java programs interact with virtual machines at the micr...

引用

Proceedings of the 18th annual ACM SIGPLAN conference on Object-oriented programing, systems, languages, and applications

作者： Lieven Eeckhout Andy Georges Koen De Bosschere Ghent University Gent Belgium

ISBN: (纸本)9781581137125

java workloads are becoming increasingly prominent on various platforms ranging from embedded systems, over general-purpose computers to high-end servers. Understanding the implications of all the aspects involved when running java workloads, is thus extremely important during the design of a system that will run such workloads. In other words, understanding the interaction between the java application, its input and the virtual machine it runs on, is key to a succesful design. The goal of this paper is to study this complex interaction at the microarchitectural level, e.g., by analyzing the branch behavior, the cache behavior, etc. This is done by measuring a large number of performance characteristics using performance counters on an AMD K7 Duron microprocessor. These performance characteristics are measured for seven virtual machine configurations, and a collection of java benchmarks with corresponding inputs coming from the SPECjvm98 benchmark suite, the SPECjbb2000 benchmark suite, the java Grande Forum benchmark suite and an open-source raytracer, called Raja with 19 scene descriptions. This large amount of data is further analyzed using statistical data analysis techniques, namely principal components analysis and cluster analysis. These techniques provide useful insights in an understandable *** our experiments, we conclude that (i) the behavior observed at the microarchitectural level is primarily determined by the virtual machine for small input sets, e.g., the SPECjvm98 s1 input set; (ii) the behavior can be quite different for various input sets, e.g., short-running versus long-running benchmarks; (iii) for long-running benchmarks with few hot spots, the behavior can be primarily determined by the java program and not the virtual machine, i.e., all the virtual machines optimize the hot spots to similarly behaving native code; (iv) in general, the behavior of a java application running on one virtual machine can be significantly different from runnin

关键词： virtual machine technology workload characterization performance analysis java workloads statistical data analysis

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