Making Memory Measurements in .NET framework

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Making Memory Measurements
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The sys module has a function getsizeof() that can be used to investigate the memory footprint (in bytes) of individual Python objects For example:
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>>> >>> 14 >>> 52 >>> 52 >>> 56 import sys sysgetsizeof(1) sysgetsizeof("Hello World") sysgetsizeof([1,2,3,4]) sum(sysgetsizeof(x) for x in [1,2,3,4])
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For containers such as lists, tuples, and dictionaries, the size that gets reported is just for the container object itself, not the cumulative size of all objects contained inside of it For instance, in the previous example, the reported size of the list [1,2,3,4] is actually smaller than the space required for four integers (which are 14 bytes each)This is because the contents of the list are not included in the totalYou can use sum() as shown here to calculate the total size of the list contents Be aware that the getsizeof() function is only going to give you a rough idea of overall memory use for various objects Internally, the interpreter aggressively shares objects via reference counting so the actual memory consumed by an object might be far less than you first imagine Also, given that C extensions to Python can allocate memory outside of the interpreter, it may be difficult to precisely get a measurement of overall memory useThus, a secondary technique for measuring the actual memory footprint is to inspect your running program from an operating system process viewer or task manager Frankly, a better way to get a handle on memory use may be to sit down and be analytical about it If you know your program is going to allocate various kinds of data structures and you know what kinds of data will be stored in those structures (that is, ints, floats, strings, and so on), you can use the results of the getsizeof() function to
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Tuning and Optimization
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obtain figures for calculating an upper bound on your program s memory footprint or at the very least, you can get enough information to carry out a back of the envelope estimate
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Disassembly
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The dis module can be used to disassemble Python functions, methods, and classes into low-level interpreter instructionsThe module defines a function dis() that can be used like this:
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>>> from dis import dis >>> dis(split) 2 0 LOAD_FAST 3 LOAD_ATTR 6 LOAD_FAST 9 CALL_FUNCTION 12 STORE_FAST 3 15 LOAD_GLOBAL 18 JUMP_IF_FALSE 21 POP_TOP 22 25 26 29 32 35 38 41 42 45 48 51 54 57 60 63 66 67 70 73 76 79 BUILD_LIST DUP_TOP STORE_FAST LOAD_GLOBAL LOAD_GLOBAL LOAD_FAST CALL_FUNCTION GET_ITER FOR_ITER UNPACK_SEQUENCE STORE_FAST STORE_FAST LOAD_FAST LOAD_FAST LOAD_FAST CALL_FUNCTION LIST_APPEND JUMP_ABSOLUTE DELETE_FAST STORE_FAST JUMP_FORWARD POP_TOP
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Expert programmers can use this information in two ways First, a disassembly will show you exactly what operations are involved in executing a functionWith careful study, you might spot opportunities for making speedups Second, if you are programming with threads, each line printed in the disassembly represents a single interpreter operation each of which has atomic executionThus, if you are trying to track down a tricky race condition, this information might be useful
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11 Testing, Debugging, Profiling, and Tuning
Tuning Strategies
The following sections outline a few optimization strategies that, in the opinion of the author, have proven to be useful with Python code
Understand Your Program
Before you optimize anything, know that speedup obtained by optimizing part of a program is directly related to that part s total contribution to the execution time For example, if you optimize a function by making it run 10 times as fast but that function only contributes to 10 percent of the program s total execution time, you re only going to get an overall speedup of about 9% 10% Depending on the effort involved in making the optimization, this may or may not be worth it It is always a good idea to first use the profiling module on code you intend to optimizeYou really only want to focus on functions and methods where your program spends most of its time, not obscure operations that are called only occasionally