Explanation:
A.)
we have two machines M1 and M2
cpi stands for clocks per instruction.
to get cpi for machine 1:
= we multiply frequencies with their corresponding M1 cycles and add everything up
50/100 x 1 = 0.5
20/100 x 2 = 0.4
30/100 x 3 = 0.9
CPI for M1 = 0.5 + 0.4 + 0.9 = 1.8
We find CPI for machine 2
we use the same formula we used for 1 above
50/100 x 2 = 1
20/100 x 3 = 0.6
30/100 x 4 = 1.2
CPI for m2 = 1 + 0.6 + 1.2 = 2.8
B.)
CPU execution time for m1 and m2
this is calculated by using the formula;
I * CPI/clock cycle time
execution time for A:
= I * 1.8/60X10⁶
= I x 30 nsec
execution time b:
I x 2.8/80x10⁶
= I x 35 nsec
D) a type of training that allows...
Answer:
the answer is priming read
Explanation:
hope it helps u
Answer:
import numpy as np
def sample_median(n, P):
return np.median( np.random.choice ( np.arange (1, len(P) + 1 ), n, p = P ) )
print(sample_median(10,[0.1 0.2 0.1 0.3 0.1 0.2]))
print(sample_median(10,[0.1 0.2 0.1 0.3 0.1 0.2]))
print(sample_median(5, [0.3,0.7])
print(sample_median(5, [0.3,0.7])
Explanation:
- Bring in the numpy library to use the median function provided by the numpy library.
- Define the sample_median function that takes in 2 parameters and returns the median with the help of built-in random, choice and arrange functions.
- Call the sample_median function by providing some values to test and then display the results.
Output:
4.5
4.0
2.0
1.0