Here is a Python program:
tmp = input().split(' ')
c = tmp[0]; s = tmp[1]
ans=0
for i in range(len(s)):
if s[i] == c: ans+=1
# the ans variable stores the number of occurrences
print(ans)
Answer:
Explanation:
/*# represents ID selector
*/
#feature{
font-family: 'Arial';
font-size: 10px;
color: red;
background: white;
width: 80%;
filter: drop-shadow(30px 10px 4px #4444dd);
}
Answer:
It would be correct to say that out-of-order makes a machine's performance more sensitive to branch prediction accuracy.
Explanation:
This can be explained as when a machine is out-of-order, in that state the execution holds importance in prediction accuracy, any increase in these results in rate of prediction near about 25% for the single-issue operating in-order. This is due to the reason that some of the predictions are required for the global pattern history. Most recent outcomes are recorded in the register and for a 4-way machine which is out-of-order, accuracy is very poor as a result of the delay of the branch history for next prediction.
