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Pro ling LifeTime by total execution time of the methods.
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Pro ling LifeTime by time spent in the methods.
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expensive method is java.util.Hashtable.containsKey(). The method itself is fairly quick (unfortunately the pro ler does not show the average time spent in each method invocation); however, we called it nearly 600 000 times because we are constantly checking to see if a cell is alive or empty. As we saw in Figure 7.8, some 13 % of the time was spent in LifeCanvas.paintCanvas(). However, from the calling graph in Figure 7.10,
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Figure 7.10 Pro ling LifeTime by calling tree.
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we can see that most of that time was spent in nextElement() from the Hashtable Enumerator. 53 % of the time was spent in HashGM.getNeighbourCount(). The main culprits are Hashtable.containsKey() and the Cell constructor.
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7.14.3.2 Heap Analysis
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Heap analysis is the other side of pro ling. Pro ling is used to identify performance issues; heap analysis to identify memory issues. Sun s Wireless Toolkit heap analyzer displays running data, though with a serious impact on performance, by a factor of about 50. The tool provides two displays. The rst is a graph of overall memory usage (see Figure 7.11). This shows memory gradually increasing, then dropping as the garbage collector kicks in. Remember that this is the KVM garbage collector. It would be quite fascinating to see a similar graph for CLDC HI behavior. The graph view reports that at the point the emulator was shut down, which was soon after the garbage collector ran, there were 1790 objects, occupying around 52 KB of heap.
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Figure 7.11 Graph of LifeTime memory usage.
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The objects view (see Figure 7.12) provides a more detailed break down of the heap utilization. Top of the list are the Cell objects: just over 1500, at 23 bytes each. Again this points to the inef ciency of the algorithm, given that there are typically a few hundred live cells in each generation. Character arrays and Strings are next on the list: these are good targets for obfuscators. The hash tables do not take up as much memory as might be expected.
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7.14.3.3 Debugging Flags
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boolean debug = false; if(debug){ debugStream.println("Debug information"); // other statements debugStream.println("Status: " + myClass); }
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The compiler will not compile this obviously dead code. You should not be afraid of putting in debug statements in this manner as, provided the debug ag is false, the code will not add to the size of your class les. You do have to be careful of one thing: if the debug ag is in a separate le, ensure that you recompile both les when you change the state of the debug ag.
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Figure 7.12 Heap Analysis of LifeTime.
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7.14.3.4 What We Should Look Forward To
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The tools for wireless development are still fairly immature. Despite the prospect of more mobile phones running Java than the total number of desktop computers, Wireless IDEs (such as those from IBM, Sun, Borland, Metrowerks and others) are heavyweight J2SE environments modi ed for wireless development. We also need real-time tools that work with any emulator and on target devices. To assist this, it is likely that Java VMs on Symbian OS will be at least debug-enabled in the near future, with support for on-target pro ling and heap analysis to follow. Better pro ling is needed, for instance to see how much time a method spends servicing each of the methods that call it and how much time is spent on each line of code. Heap analysis that gives a more detailed snapshot of the heap is required. For instance, the J2SE pro ling tools provide a complete dump of the heap so that it is possible to trace and examine the contents of each heap variable.
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7.14.4 Implementing the GenerationMap Class
The most successful container in LifeTime used a sorted binary tree. Under the Wireless Toolkit emulator (running on a 500 MHz Windows 2000 laptop), LifeTime took about 33 s to calculate and render the rst