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Interpreting Java Program Runtimes

Interpreting Java Program Runtimes. Stuart Hansen UW - Parkside. The Problem:. The JVM influences program runtimes in complex and mysterious ways. The Real Problem:. It is VERY frustrating to obtain runtimes that you can’t explain. Algorithm Analysis.

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Interpreting Java Program Runtimes

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  1. Interpreting Java Program Runtimes Stuart Hansen UW - Parkside

  2. The Problem: The JVM influences program runtimes in complex and mysterious ways The Real Problem: It is VERY frustrating to obtain runtimes that you can’t explain

  3. Algorithm Analysis • One goal of algorithm analysis is to predict resources (e.g. time) that an implementation will require • There are many confounding influences • Speed of CPU - Implementation Language • Load on the system - Compiler • Size of physical memory • Operating system

  4. Algorithm Analysis con’t. • We use the confounding influences as pedagogic tools • CPU time vs. elapsed time • Physical memory vs. virtual memory • Compiler optimizations

  5. Java VM Presents New Issues • What are they? • Can we control them? • Can we use them at teaching aids?

  6. Lack of Documentation • Lots of material on Java performance with lots of practical advice • None tells why my BubbleSort code doesn’t graph to be a parabola

  7. Three Examples • Calls to Arrays.sort • Modified Mergesort • Rehashing All experiments performed using Sun’s Java SDK 1.4.2 under Debian Linux

  8. Arrays.sort( ) • Compare runtimes of CS1 sorts to Arrays.sort()

  9. Dynamic Class Loading • Java loads a class only when it is first referenced • Disk IO is slow • Can force pre-loading using Class.forName()

  10. Side Issue – Java Startup • Simplest Java application public class Simple { public static void main (String [] args) { } } • Loads 280 classes

  11. Mergesort • Requires an auxiliary array for merging • Should only be as big as subarrays to be merged • Too large of an array degrades performance to O(n2) • Let students work with and improve bad code

  12. Results

  13. Finding the Parabola • Limit the graph to the smaller array sizes

  14. Heap Management • Generational Garbage Collector • Major and minor garbage collections • Alternative is incremental garbage collection • Controlling Heap size • Grows as needed by default -Xms and –Xmx options

  15. Incremental GC

  16. Things to Note • Still not perfectly smooth • Incremental GC creates a performance hit of about 10%

  17. Rehashing • Suggested by Mike Clancy during panel discussion at SIGCSE 2002 • Create a Hashtable of a fixed size • Load with different amounts of data • Explicitly invoke rehash()

  18. Rehashing Results

  19. Optimizations in Java • Dynamic Compilation • Byte code to native code • Dynamic Optimization • E.g. Method inlining • Both are hard to control

  20. Rehashing with Large Initial Heap • Lets more time be dedicated to rehashing

  21. Remaining Issues • Each data set is still analyzed on an ad hoc basis • No consistent set of instructions to give to students to get meaningful runtimes • No handle on Java 5

  22. Conclusion • Highly recommend experimentation in classes • Need tenacity to explain the results Questions?

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