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SIGMETRICS 2001 / Performance 2001

Characterizing the Memory Behavior of Java Workloads: A Structured View and Opportunities for Optimizations

Authors
Yefim Shuf <yshuf@cs.princeton.edu> <yefim@us.ibm.com>
Computer Science Department, Princeton University
IBM T. J. Watson Research Center, Yorktown Heights

Mauricio J. Serrano <mauricio.j.serrano@intel.com>
Intel Microprocessor Research Labs, Santa Clara

Manish Gupta <mgupta@us.ibm.com>
IBM T. J. Watson Research Center, Yorktown Heights

Jaswinder Pal Singh <jps@cs.princeton.edu>
Computer Science Department, Princeton University
 

Abstract
This paper studies the memory behavior of important Java workloads used in benchmarking Java Virtual Machines (JVMs), based on instrumentation of both application and library code in a state-of-the-art JVM, and provides structured information about these workloads to help guide systems' design. We begin by characterizing the inherent memory behavior of the benchmarks, such as information on the breakup of heap accesses among different categories and on the hotness of references to fields and methods. We then provide detailed information about misses in the data TLB and caches, including the distribution of misses over different kinds of accesses and over different methods. In the process, we make interesting discoveries about TLB behavior and limitations of data prefetching schemes discussed in the literature in dealing with pointer-intensive Java codes. Throughout this paper, we develop a set of recommendations to computer architects and compiler writers on how to optimize computer systems and system software to run Java programs more efficiently. This paper also makes the first attempt to compare the characteristics of SPECjvm98 to those of a server-oriented benchmark, pBOB, and explain why the current set of SPECjvm98 benchmarks may not be adequate for a comprehensive and objective evaluation of JVMs and just-in-time (JIT) compilers.

We discover that the fraction of accesses to array elements is quite significant, demonstrate that the number of "hot spots" in the benchmarks is small, and show that field reordering cannot yield significant performance gains. We also show that even a fairly large L2 data cache is not effective for many Java benchmarks. We observe that instructions used to prefetch data into the L2 data cache are often squashed because of high TLB miss rates and because the TLB does not usually have the translation information needed to prefetch the data into the L2 data cache. We also find that co-allocation of frequently used method tables can reduce the number of TLB misses and lower the cost of accessing type information block entries in virtual method calls and runtime type checking.


[Last updated Fri Mar 23 2001]