The answer & explanation for this question is given in the attachment below.
Alternative 1:A small D-cache with a hit rate of 94% and a hit access time of 1 cycle (assume that no additional cycles on top of the baseline CPI are added to the execution on a cache hit in this case).Alternative 2: A larger D-cache with a hit rate of 98% and the hit access time of 2 cycles (assume that every memory instruction that hits into the cache adds one additional cycle on top of the baseline CPI). a)[10%] Estimate the CPI metric for both of these designs and determine which of these two designsprovides better performance. Explain your answers!CPI = # Cycles / # InsnLet X = # InsnCPI = # Cycles / XAlternative 1:# Cycles = 0.50*X*2 + 0.50*X(0.94*2 + 0.06*150)CPI= 0.50*X*2 + 0.50*X(0.94*2 + 0.06*150) / X1= X(0.50*2 + 0.50(0.94*2 + 0.06*150) ) / X= 0.50*2 + 0.50(0.94*2 + 0.06*150)= 6.44Alternative 2:# Cycles = 0.50*X*2 + 0.50*X(0.98*(2+1) + 0.02*150)CPI= 0.50*X*2 + 0.50*X(0.98*(2+1) + 0.02*150) / X2= X(0.50*2 + 0.50(0.98*(2+1) + 0.02*150)) / X= 0.50*2 + 0.50(0.98*(2+1) + 0.02*150)= 3.97Alternative 2 has a lower CPI, therefore Alternative 2 provides better performance.
In 1972 Bill Gates formed Traf-O-Data.
Answer:
new_segment = [ ]
for segment in segments:
new_segment.append({'name': segment, 'average_spend': money})
print( new_segment)
Using list comprehension:
new_segment =[{'name': segment, 'average_spend': money} for segment in segments]
Using map():
def listing(a):
contain = {'name': segment, 'average_spend': money}
return contain
new_segment = [ ]
new_segment.append(map( listing, segment))
print(list(new_segment)
Explanation:
The python codes above create a list of dictionaries in all instances using for loop, for loop in list comprehension and the map function which collect two arguments .
