A methodology for benchmarking Java Grande applications

Bull, J. Mark; Smith, Lorna; Westhead, Martin; Henty, David; Davey, R. A.

doi:10.1145/304065.304103

Cited by 64 publications

(55 citation statements)

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“…The authors analyze the Java Grande benchmark suite [7] using JVM-independent metrics. The authors consider static and dynamic instruction mixes and identify differences in optimizations performed by five different Java-to-bytecode compilers.…”

Section: Related Workmentioning

confidence: 99%

Characteristics of dynamic JVM languages

Sarimbekov

Podzimek

Bulej

et al. 2013

Proceedings of the 7th ACM Workshop on Virtual Machines and Intermediate Languages

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The Java Virtual Machine (JVM) has become an execution platform targeted by many programming languages. However, unlike with Java, a statically-typed language, the performance of the JVM and its Just-In-Time (JIT) compiler with dynamically-typed languages lags behind purpose-built language-specific JIT compilers. In this paper, we aim to contribute to the understanding of the workloads imposed on the JVM by dynamic languages. We use various metrics to characterize the dynamic behavior of a variety of programs written in three dynamic languages (Clojure, Python, and Ruby) executing on the JVM. We identify the differences with respect to Java, and briefly discuss their implications.

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Section: Related Workmentioning

confidence: 99%

Characteristics of dynamic JVM languages

Sarimbekov

Podzimek

Bulej

et al. 2013

Proceedings of the 7th ACM Workshop on Virtual Machines and Intermediate Languages

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“…The SPEC suite focuses on the performance of the hardware processor and memory subsystem when executing common general purpose application computations 4 . Finally, we chose two memory-intensive benchmarks from the Java Grande 2.0 suite [12] to further evaluate the performance of k-BLPP.…”

Section: Methodsmentioning

confidence: 99%

Ball-Larus path profiling across multiple loop iterations

D’Elia

Demetrescu

2013

Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages &Amp; Application

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Identifying the hottest paths in the control flow graph of a routine can direct optimizations to portions of the code where most resources are consumed. This powerful methodology, called path profiling, was introduced by Ball and Larus in the mid 90's [4] and has received considerable attention in the last 15 years for its practical relevance. A shortcoming of the Ball-Larus technique was the inability to profile cyclic paths, making it difficult to mine execution patterns that span multiple loop iterations. Previous results, based on rather complex algorithms, have attempted to circumvent this limitation at the price of significant performance losses even for a small number of iterations. In this paper, we present a new approach to multi-iteration path profiling, based on data structures built on top of the original Ball-Larus numbering technique. Our approach allows the profiling of all executed paths obtained as a concatenation of up to k Ball-Larus acyclic paths, where k is a user-defined parameter. We provide examples showing that this method can reveal optimization opportunities that acyclic-path profiling would miss. An extensive experimental investigation on a large variety of Java benchmarks on the Jikes RVM shows that our approach can be even faster than Ball-Larus due to fewer operations on smaller hash tables, producing compact representations of cyclic paths even for large values of k.

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“…For the purposes of our study, we have used programs from two Java benchmark suites: the SPEC JVM98 benchmark suite [26] and the Java Grande Forum Benchmark Suite [8]. The first set of programs is taken from section 3 of the Java Grande suite version 2.0, and contains five large-scale applications, designed as examples of real-world applications.…”

Section: The Benchmark Suitesmentioning

confidence: 99%

“…Studies of the Grande and SPEC suites have typically concentrated on performance issues for various JVMs. Studies of the Grande suite include performance-related measures [8,7] as well as dynamic byte-code level views [11]. The SPEC JVM98 suite is perhaps more commonly used to measure the speed and effectiveness of Java compilers and virtual machines [5,6,23,27,31].…”

Section: The Benchmark Suitesmentioning

confidence: 99%

A method‐level comparison of the Java Grande and SPEC JVM98 benchmark suites

Gregg

Power

Waldron

2005

Concurrency and Computation

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SUMMARYIn this paper we seek to provide a foundation for the study of the level of use of objectoriented techniques in Java programs in general, and scientific applications in particular. In particular we investigate the profiles of Java programs from a number of perspectives, including the use of class library methods, the size of methods called, the mode of invoke instruction used and the polymorphicity of call sites. We also present a categorisation of the nature of small-sized methods used in Java programs. We compare the Java Grande and SPEC JVM98 benchmark suites, and note a significant difference in the nature and composition of these suites, with the programs from the Java Grande suite demonstrating a less object-oriented approach.

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A methodology for benchmarking Java Grande applications

Cited by 64 publications

References 1 publication

Characteristics of dynamic JVM languages

Characteristics of dynamic JVM languages

Ball-Larus path profiling across multiple loop iterations

A method‐level comparison of the Java Grande and SPEC JVM98 benchmark suites

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