Method-level phase behavior in java workloads

Java workloads are becoming more and more prominent on various computing devices. Understanding the behavior 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.

[1]  Narayanan Vijaykrishnan,et al.  Java Runtime Systems: Characterization and Architectural Implications , 2001, IEEE Trans. Computers.

[2]  R. Balasubramonian,et al.  Memory hierarchy reconfiguration for energy and performance in general-purpose processor architectures , 2000, Proceedings 33rd Annual IEEE/ACM International Symposium on Microarchitecture. MICRO-33 2000.

[3]  Sandhya Dwarkadas,et al.  Characterizing and predicting program behavior and its variability , 2003, 2003 12th International Conference on Parallel Architectures and Compilation Techniques.

[4]  Vivek Sarkar,et al.  The Jalape ~ no Dynamic Optimizing Compiler for Java TM , 1999 .

[5]  Mikhail Dmitriev.  Selective profiling of Java applications using dynamic bytecode instrumentation , 2004, IEEE International Symposium on - ISPASS Performance Analysis of Systems and Software, 2004.

[6]  Alfred V. Aho,et al.  Compilers: Principles, Techniques, and Tools , 1986, Addison-Wesley series in computer science / World student series edition.

[7]  Stephen J. Fink,et al.  The Jalapeño virtual machine , 2000, IBM Syst. J..

[8]  Laurie Hendren,et al.  Dynamic metrics for java , 2003, OOPSLA 2003.

[9]  Jack J. Dongarra,et al.  A Portable Programming Interface for Performance Evaluation on Modern Processors , 2000, Int. J. High Perform. Comput. Appl..

[10]  Erik Hagersten,et al.  Memory system behavior of Java-based middleware , 2003, The Ninth International Symposium on High-Performance Computer Architecture, 2003. HPCA-9 2003. Proceedings..

[11]  Lizy Kurian John,et al.  Benchmarking Internet Servers on Superscalar Machines , 2001 .

[12]  Mauricio J. Serrano,et al.  Characterizing the memory behavior of Java workloads: a structured view and opportunities for optimizations , 2001, SIGMETRICS '01.

[13]  Matthew Arnold,et al.  Online feedback-directed optimization of Java , 2002, OOPSLA '02.

[14]  Stephen J. Fink,et al.  Design, implementation and evaluation of adaptive recompilation with on-stack replacement , 2003, International Symposium on Code Generation and Optimization, 2003. CGO 2003..

[15]  James E. Smith,et al.  Comparing program phase detection techniques , 2003, Proceedings. 36th Annual IEEE/ACM International Symposium on Microarchitecture, 2003. MICRO-36..

[16]  Brad Calder,et al.  Automatically characterizing large scale program behavior , 2002, ASPLOS X.

[17]  Matthew Arnold,et al.  Adaptive optimization in the Jalapeño JVM , 2000, OOPSLA '00.

[18]  Brad Calder,et al.  Structures for phase classification , 2004, IEEE International Symposium on - ISPASS Performance Analysis of Systems and Software, 2004.

[19]  Brad Calder,et al.  Phase tracking and prediction , 2003, ISCA '03.

[20]  Matthias Hauswirth,et al.  Using Hardware Performance Monitors to Understand the Behavior of Java Applications , 2004, Virtual Machine Research and Technology Symposium.

[21]  Richard A. Johnson,et al.  Applied Multivariate Statistical Analysis , 1983 .

[22]  John C. Gyllenhaal,et al.  An Architectural Framework for Runtime Optimization , 2001, IEEE Trans. Computers.

[23]  Lieven Eeckhout,et al.  How java programs interact with virtual machines at the microarchitectural level , 2003, OOPSLA 2003.

[24]  Michael C. Huang,et al.  Positional adaptation of processors: application to energy reduction , 2003, ISCA '03.

[25]  R. D. Barnes,et al.  An Architectural Framework for Run-Time Optimization , 2001 .

[26]  Mikko H. Lipasti,et al.  An architectural evaluation of Java TPC-W , 2001, Proceedings HPCA Seventh International Symposium on High-Performance Computer Architecture.