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What happens in the assembly output when we add "cc" to clobber list


By : Ranjan Thomas
Date : September 15 2020, 03:00 PM
it helps some times For x86, absolutely nothing. For x86 and x86-64, a cc clobber is implicit in every asm() statement. This design decision makes some sense because most x86 instructions wrote FLAGS. And because it's easy to miss and could be hard to catch with testing. (Although there's no shortage of things that are easy to get wrong with GNU C inline asm. There's usually no need to use it.)
(It does make it impossible to tell the compiler when your asm statement doesn't modify flags, but the cost of that is probably low, usually just one more instruction to redo a compare or something, or to save a variable so it can be compared later.)
code :


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GCC inline assembly error: asm-specifier for variable '%al' conflicts with asm clobber list


By : ryanhevans77
Date : March 29 2020, 07:55 AM
hope this fix your issue This has to be some bug in gcc (maybe __asm blocks have implicit clobbering). Anyway there are many workarounds:
code :
__asm
{
    // ...
    mov ah, testData
    mov al, ah
    shl eax, 16
    // ...
};
__asm
{
    // ...
    mov al, testData
    mov ah, testData
    shl eax, 16
    // ...
};
__asm
{
    // ...
    movzx eax, testData
    imul eax, 0x0101
    shl eax, 16
    // ...
};

Is FLAGS/EFLAGS part of "CC" (condition control) for clobber list?


By : keef
Date : March 29 2020, 07:55 AM
help you fix your problem According to the manual, yes - cc is clobbered. RDRAND also sets OF, SF, ZF, AF, PF <- 0.
In practice, gcc assumes that an __asm__ block always clobbers the [E|R]FLAGS condition code register for x86. I don't have the reference, but you can see this assumption in places like the longlong.h header used in various GNU packages.

How to get "valid"/"invalid" output from a list of letters that will constitute output?


By : Charran Sinanan
Date : March 29 2020, 07:55 AM
it helps some times The efficient way to test if a character is valid is to use a set of valid letters. And we can use the built-in enumerate function to get the characters in a word and their positions at the same time.
code :
allowed = set('fruit')

words = ('fitur', 'iursfgt', 'ffrruuiitt', 'FRUIT')
for word in words:
    print(word)
    valid = True
    for i, c in enumerate(word):
        if c not in allowed:
            print('Bad char', c, 'at position', i)
            valid = False
    if valid:
        print(word, 'is valid\n') 
    else:
        print(word, 'is invalid\n') 
fitur
fitur is valid

iursfgt
Bad char s at position 3
Bad char g at position 5
iursfgt is invalid

ffrruuiitt
ffrruuiitt is valid

FRUIT
Bad char F at position 0
Bad char R at position 1
Bad char U at position 2
Bad char I at position 3
Bad char T at position 4
FRUIT is invalid
allowed = 'fruit'
words = ('fitur', 'iursfgt', 'ffrruuiitt', 'FRUIT')
for word in words:
    print(word)
    valid = True
    for i in range(len(word)):
        c = word[i]
        if c not in allowed:
            print('Bad char', c, 'at position', i)
            valid = False
    if valid:
        print(word, 'is valid\n') 
    else:
        print(word, 'is invalid\n') 
allowed = set('fruit')
words = ('fitur', 'iursfgt', 'ffrruuiitt', 'FRUIT')
for word in words:
    a = [word]
    a.extend(f'Bad char {c} at position {i}' for i, c in enumerate(word)
        if c not in allowed)
    a.append(f"{word} is {('in', '')[not a]}valid\n")
    print('\n'.join(a))

How to remove List special characters ("()", "'",",") from the output of a bi / tri-gram i


By : somazero
Date : March 29 2020, 07:55 AM
I wish this help you A better question would have been what are those ("()", "'",",") in the ngrams output?
code :
>>> from nltk import ngrams
>>> from nltk import word_tokenize

# Split a sentence into a list of "words"
>>> word_tokenize("This is a foo bar sentence")
['This', 'is', 'a', 'foo', 'bar', 'sentence']
>>> type(word_tokenize("This is a foo bar sentence"))
<class 'list'>

# Extract bigrams.
>>> list(ngrams(word_tokenize("This is a foo bar sentence"), 2))
[('This', 'is'), ('is', 'a'), ('a', 'foo'), ('foo', 'bar'), ('bar', 'sentence')]

# Okay, so the output is a list, no surprise.
>>> type(list(ngrams(word_tokenize("This is a foo bar sentence"), 2)))
<class 'list'>
>>> list(ngrams(word_tokenize("This is a foo bar sentence"), 2))[0]
('This', 'is')
>>> first_thing_in_output = list(ngrams(word_tokenize("This is a foo bar sentence"), 2))[0]
>>> type(first_thing_in_output)
<class 'tuple'>
>>> print(first_thing_in_output)
('This', 'is')
>>> print(str(first_thing_in_output))
('This', 'is')
>>> print(' '.join((first_thing_in_output)))
This is
from nltk import word_tokenize
from nltk.util import ngrams
from nltk.corpus import stopwords

text = '''The pure amnesia of her face,
newborn. I looked so far into her that, for a while, looked so far into her that, for a while  looked so far into her that, for a while looked so far into her that, for a while the visual 
held no memory. Little by little, I returned to myself, waking to nurse the visual held no  memory. Little by little, I returned to myself, waking to nurse
'''

stoplist = set(stopwords.words('english'))

tokens = [token for token in nltk.word_tokenize(text) if token not in stoplist]

list(ngrams(tokens, 2))
[('The', 'pure'),
 ('pure', 'amnesia'),
 ('amnesia', 'face'),
 ('face', ','),
 (',', 'newborn'),
 ('newborn', '.'),
 ('.', 'I'),
 ('I', 'looked'),
 ('looked', 'far'),
 ('far', ','),
 (',', ','), ...]
from string import punctuation
from nltk import word_tokenize
from nltk.util import ngrams
from nltk.corpus import stopwords

text = '''The pure amnesia of her face,
newborn. I looked so far into her that, for a while, looked so far into her that, for a while  looked so far into her that, for a while looked so far into her that, for a while the visual 
held no memory. Little by little, I returned to myself, waking to nurse the visual held no  memory. Little by little, I returned to myself, waking to nurse
'''

stoplist = set(stopwords.words('english') + list(punctuation))

tokens = [token for token in nltk.word_tokenize(text) if token not in stoplist]

list(ngrams(tokens, 2))
[('The', 'pure'),
 ('pure', 'amnesia'),
 ('amnesia', 'face'),
 ('face', 'newborn'),
 ('newborn', 'I'),
 ('I', 'looked'),
 ('looked', 'far'),
 ('far', 'looked'),
 ('looked', 'far'), ...]
>>> stopwords.words('english')
['i',
 'me',
 'my',
 'myself',
 'we',
 'our',
 'ours',
 'ourselves',
 'you',
 "you're", ...]
from string import punctuation
from nltk import word_tokenize
from nltk.util import ngrams
from nltk.corpus import stopwords

text = '''The pure amnesia of her face,
newborn. I looked so far into her that, for a while, looked so far into her that, for a while  looked so far into her that, for a while looked so far into her that, for a while the visual 
held no memory. Little by little, I returned to myself, waking to nurse the visual held no  memory. Little by little, I returned to myself, waking to nurse
'''

stoplist = set(stopwords.words('english') + list(punctuation))

tokens = [token for token in nltk.word_tokenize(text) if token.lower() not in stoplist]

list(ngrams(tokens, 2))
[('pure', 'amnesia'),
 ('amnesia', 'face'),
 ('face', 'newborn'),
 ('newborn', 'looked'),
 ('looked', 'far'),
 ('far', 'looked'),
 ('looked', 'far'),
 ('far', 'looked'),
 ('looked', 'far'),
 ('far', 'looked'), ...]
from string import punctuation
from nltk import word_tokenize
from nltk.util import ngrams
from nltk.corpus import stopwords
from nltk import FreqDist

text = '''The pure amnesia of her face,
newborn. I looked so far into her that, for a while, looked so far into her that, for a while  looked so far into her that, for a while looked so far into her that, for a while the visual 
held no memory. Little by little, I returned to myself, waking to nurse the visual held no  memory. Little by little, I returned to myself, waking to nurse
'''

stoplist = set(stopwords.words('english') + list(punctuation))

tokens = [token for token in nltk.word_tokenize(text) if token.lower() not in stoplist]

FreqDist(ngrams(tokens, 2)).most_common()
[(('looked', 'far'), 4),
 (('far', 'looked'), 3),
 (('visual', 'held'), 2),
 (('held', 'memory'), 2),
 (('memory', 'Little'), 2),
 (('Little', 'little'), 2),
 (('little', 'returned'), 2),
 (('returned', 'waking'), 2),
 (('waking', 'nurse'), 2),
 (('pure', 'amnesia'), 1),
 (('amnesia', 'face'), 1),
 (('face', 'newborn'), 1),
 (('newborn', 'looked'), 1),
 (('far', 'visual'), 1),
 (('nurse', 'visual'), 1)]
with open('bigrams-list.tsv', 'w') as fout:
    for bg, count in FreqDist(ngrams(tokens, 2)).most_common():
        print('\t'.join([' '.join(bg), str(count)]), end='\n', file=fout)
looked far  4
far looked  3
visual held 2
held memory 2
memory Little   2
Little little   2
little returned 2
returned waking 2
waking nurse    2
pure amnesia    1
amnesia face    1
face newborn    1
newborn looked  1
far visual  1
nurse visual    1

"Escape" and "Clobber" equivalent in MSVC


By : Lakshminarayanan
Date : March 29 2020, 07:55 AM
this will help While I don't know of an equivalent assembly trick for MSVC, Facebook uses the following in their Folly benchmark library:
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