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25. Code-Tuning Strategies
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Avoid going to the system. Work with the system vendor to make the call faster. Most vendors want to improve their products and are glad to learn about parts of their systems with weak performance. (They may seem a little grouchy about it at first, but they really are interested.)
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In the code tuning initiative I describe in the When to Tune Section, the program used an AppTime class that was derived from a commercially available BaseTime class. (These names have been changed to protect the guilty.) The AppTime object was the most common object in this application, and we instantiated tens of thousands of AppTime objects. After several months, we discovered that BaseTime was initializing itself to the system time in its constructor. For our purposes, the system time was irrelevant, which meant we were needlessly generating thousands of system-level calls. Simply overriding BaseTime s constructor and initializing the time field to 0 instead of to the system time gave us about as much performance improvement as all the other changes we made put together.
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Interpreted languages Interpreted languages tend to exact significant performance penalties because they must process each programming-language instruction before creating and executing machine code. In the performance benchmarking I performed for this chapter and 26, I observed the following approximate relationships in performance among different languages:
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Table 25-1. Relative execution time of programming languages Language C++ Visual Basic C# Java PHP Python Type of Language Compiled Compiled Compiled Byte code Interpreted Interpreted Execution time relative to C++ 1:1 1:1 1:1 1.5:1 >100:1 >100:1
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As you can see from the table, C++, Visual Basic, and C# are all comparable. Java is close, but tends to be slower than the other languages. PHP and Python are interpreted languages, and code in those languages tended to run a factor of 100 or more slower than code in C++, VB, C#, and Java. The general numbers presented in this table must be viewed cautiously. For any particular piece of code, C++, VB, C#, or Java might be twice as fast or half as fast as the other languages. (You can see this for yourself in the detailed examples in 26.)
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Errors A final source of performance problems is errors in the code. Errors can include leaving debugging code turned on (such as logging trace information to a file), forgetting to deallocate memory, improperly designing database tables, and so on.
A version 1.0 application I worked on had a particular operation that was much slower than other similar operations. A great deal of project mythology grew up to explain the slowness of this operation. We released version 1.0 without ever fully understanding why this particular operation was so slow. While working on the version 1.1 release, however, I discovered that the database table used by the operation wasn t indexed! Simply indexing the table improved performance by a factor of 30 for some operations. Defining an index on a commonly-used table is not optimization; it s just good programming practice.
Relative Performance Costs of Common Operations
Although you can t count on some operations being more expensive than others without measuring them, certain operations tend to be more expensive. When you look for the molasses in your program, use Table 25-2 to help make some initial guesses about the sticky parts of your program.
Table 25-2. Costs of Common Operations Relative Time Consumed Operation Baseline (integer assignment) Routine Calls Call routine with no parameters Call private routine with no parameters Call private routine with 1 parameter Call private routine with 2 parameters Object routine call Derived routine call Polymorphic routine call Object References Level 1 object dereference i = obj.num 1 1 foo() this.foo() this.foo( i ) this.foo( i, j ) bar.foo() derivedBar.foo() abstractBar.foo() 1 1 1.5 1.7 2 2 2.5 n/a 0.5 0.5 0.5 1 1 2 Example i=j C++ 1 Java 1
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