1 """ 2 Some helper functions to analyze the output of sys.getdxp() (which is 3 only available if Python was built with -DDYNAMIC_EXECUTION_PROFILE). 4 These will tell you which opcodes have been executed most frequently 5 in the current process, and, if Python was also built with -DDXPAIRS, 6 will tell you which instruction _pairs_ were executed most frequently, 7 which may help in choosing new instructions. 8 9 If Python was built without -DDYNAMIC_EXECUTION_PROFILE, importing 10 this module will raise a RuntimeError. 11 12 If you're running a script you want to profile, a simple way to get 13 the common pairs is: 14 15 $ PYTHONPATH=$PYTHONPATH:<python_srcdir>/Tools/scripts \ 16 ./python -i -O the_script.py --args 17 ... 18 > from analyze_dxp import * 19 > s = render_common_pairs() 20 > open('/tmp/some_file', 'w').write(s) 21 """ 22 23 import copy 24 import opcode 25 import operator 26 import sys 27 import threading 28 29 if not hasattr(sys, "getdxp"): 30 raise RuntimeError("Can't import analyze_dxp: Python built without" 31 " -DDYNAMIC_EXECUTION_PROFILE.") 32 33 34 _profile_lock = threading.RLock() 35 _cumulative_profile = sys.getdxp() 36 37 # If Python was built with -DDXPAIRS, sys.getdxp() returns a list of 38 # lists of ints. Otherwise it returns just a list of ints. 39 def has_pairs(profile): 40 """Returns True if the Python that produced the argument profile 41 was built with -DDXPAIRS.""" 42 43 return len(profile) > 0 and isinstance(profile[0], list) 44 45 46 def reset_profile(): 47 """Forgets any execution profile that has been gathered so far.""" 48 with _profile_lock: 49 sys.getdxp() # Resets the internal profile 50 global _cumulative_profile 51 _cumulative_profile = sys.getdxp() # 0s out our copy. 52 53 54 def merge_profile(): 55 """Reads sys.getdxp() and merges it into this module's cached copy. 56 57 We need this because sys.getdxp() 0s itself every time it's called.""" 58 59 with _profile_lock: 60 new_profile = sys.getdxp() 61 if has_pairs(new_profile): 62 for first_inst in range(len(_cumulative_profile)): 63 for second_inst in range(len(_cumulative_profile[first_inst])): 64 _cumulative_profile[first_inst][second_inst] += ( 65 new_profile[first_inst][second_inst]) 66 else: 67 for inst in range(len(_cumulative_profile)): 68 _cumulative_profile[inst] += new_profile[inst] 69 70 71 def snapshot_profile(): 72 """Returns the cumulative execution profile until this call.""" 73 with _profile_lock: 74 merge_profile() 75 return copy.deepcopy(_cumulative_profile) 76 77 78 def common_instructions(profile): 79 """Returns the most common opcodes in order of descending frequency. 80 81 The result is a list of tuples of the form 82 (opcode, opname, # of occurrences) 83 84 """ 85 if has_pairs(profile) and profile: 86 inst_list = profile[-1] 87 else: 88 inst_list = profile 89 result = [(op, opcode.opname[op], count) 90 for op, count in enumerate(inst_list) 91 if count > 0] 92 result.sort(key=operator.itemgetter(2), reverse=True) 93 return result 94 95 96 def common_pairs(profile): 97 """Returns the most common opcode pairs in order of descending frequency. 98 99 The result is a list of tuples of the form 100 ((1st opcode, 2nd opcode), 101 (1st opname, 2nd opname), 102 # of occurrences of the pair) 103 104 """ 105 if not has_pairs(profile): 106 return [] 107 result = [((op1, op2), (opcode.opname[op1], opcode.opname[op2]), count) 108 # Drop the row of single-op profiles with [:-1] 109 for op1, op1profile in enumerate(profile[:-1]) 110 for op2, count in enumerate(op1profile) 111 if count > 0] 112 result.sort(key=operator.itemgetter(2), reverse=True) 113 return result 114 115 116 def render_common_pairs(profile=None): 117 """Renders the most common opcode pairs to a string in order of 118 descending frequency. 119 120 The result is a series of lines of the form: 121 # of occurrences: ('1st opname', '2nd opname') 122 123 """ 124 if profile is None: 125 profile = snapshot_profile() 126 def seq(): 127 for _, ops, count in common_pairs(profile): 128 yield "%s: %s\n" % (count, ops) 129 return ''.join(seq()) 130
