How java programs interact with virtual machines at the microarchitectural level

Abstract

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 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 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 running on another virtual machine. These conclusions warn researchers working on Java workloads to be careful when using a limited number of Java benchmarks or virtual machines since this might lead to biased conclusions.