Answer:
import numpy as np
a = int(input ("Enter a"))
b = int(input ("Enter b"))
c = int(input ("Enter c"))
d = int(input ("Enter d"))
c1 = int(input ("Enter c1"))
c2 = int(input ("Enter c2"))
array1 =[[a, b],[c, d]]
A = np.array (array1)
B = np.array ([c1, c2])
X = np.linalg.inv (A).dot (B)
print (X)
Explanation:
let ax + by =c1
cx + dy =c2
We have used the above NumPy library that has the methods for matrix calculation, and here we have used matrix multiplication, and the inverse of a matrix to find the value of x and y.
We know AX=B
X = inv A. B
And this we have used above. We can calculate inv A and do matrix multiplication using NumPy. And thus we get the above solution.
Microsoft Excel or another spreadsheet program.
Answer:
1. 2588672 bits
2. 4308992 bits
3. The larger the data size of the cache, the larger the area of memory you will need to "search" making the access time and performance slower than the a cache with a smaller data size.
Explanation:
1. Number of bits in the first cache
Using the formula: (2^index bits) * (valid bits + tag bits + (data bits * 2^offset bits))
total bits = 2^15 (1+14+(32*2^1)) = 2588672 bits
2. Number of bits in the Cache with 16 word blocks
Using the formula: (2^index bits) * (valid bits + tag bits + (data bits * 2^offset bits))
total bits = 2^13(1 +13+(32*2^4)) = 4308992 bits
3. Caches are used to help achieve good performance with slow main memories. However, due to architectural limitations of cache, larger data size of cache are not as effective than the smaller data size. A larger cache will have a lower miss rate and a higher delay. The larger the data size of the cache, the larger the area of memory you will need to "search" making the access time and performance slower than the a cache with a smaller data size.
The answer is transcript. When you apply for colleges and they want to see your high school records, they simply ask for your transcript. Hope I helped :)
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