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| 55 | <H1><A NAME="SECTION0012400000000000000000"> |
| 56 | 10.4 What Is Deterministic Profiling?</A> |
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| 58 | <A NAME="Deterministic_Profiling"></A> |
| 59 | <P> |
| 60 | <i class="dfn">Deterministic profiling</i> is meant to reflect the fact that all |
| 61 | <em>function call</em>, <em>function return</em>, and <em>exception</em> events |
| 62 | are monitored, and precise timings are made for the intervals between |
| 63 | these events (during which time the user's code is executing). In |
| 64 | contrast, <i class="dfn">statistical profiling</i> (which is not done by this |
| 65 | module) randomly samples the effective instruction pointer, and |
| 66 | deduces where time is being spent. The latter technique traditionally |
| 67 | involves less overhead (as the code does not need to be instrumented), |
| 68 | but provides only relative indications of where time is being spent. |
| 69 | |
| 70 | <P> |
| 71 | In Python, since there is an interpreter active during execution, the |
| 72 | presence of instrumented code is not required to do deterministic |
| 73 | profiling. Python automatically provides a <i class="dfn">hook</i> (optional |
| 74 | callback) for each event. In addition, the interpreted nature of |
| 75 | Python tends to add so much overhead to execution, that deterministic |
| 76 | profiling tends to only add small processing overhead in typical |
| 77 | applications. The result is that deterministic profiling is not that |
| 78 | expensive, yet provides extensive run time statistics about the |
| 79 | execution of a Python program. |
| 80 | |
| 81 | <P> |
| 82 | Call count statistics can be used to identify bugs in code (surprising |
| 83 | counts), and to identify possible inline-expansion points (high call |
| 84 | counts). Internal time statistics can be used to identify ``hot |
| 85 | loops'' that should be carefully optimized. Cumulative time |
| 86 | statistics should be used to identify high level errors in the |
| 87 | selection of algorithms. Note that the unusual handling of cumulative |
| 88 | times in this profiler allows statistics for recursive implementations |
| 89 | of algorithms to be directly compared to iterative implementations. |
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