# Chapter 4. Operations on Data

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1 Chapter 4 Operations on Data 1

2 OBJECTIVES After reading this chapter, the reader should be able to: List the three categories of operations performed on data. Perform unary and binary logic operations on bit patterns. Distinguish between logic shift operations and arithmetic shift operations. Perform addition and subtraction on integers when they are stored in two s complement format. Perform addition and subtraction on integers when stored in sign-and-magnitude format. Perform addition and subtraction operations on reals stored in floating-point format. 2

3 Operations on Data Three Categories: Logic Operations Shift Operations Arithmetic Operations 3

4 4.1 LOGICAL OPERATIONS 4

5 Logic Operations Boolean algebra belongs to a special field of mathematics called logic. A bit can take one of the two values: 0 or 1. A truth table defines the values of the output for each possible input or inputs. The output of each operator is always one bit. Operation: A + B Operator : + Operands : A, B 5

6 1 -True 0 -False Truth Tables 6

7 Unary and Binary Operations 7

8 Logical Operations 8

9 Logic Operations at Bit Level NOT Operator X 9

10 AND Operator x y For x = 0 or 1 x AND AND x 0 10

11 OR Operator x y For x = 0 or 1 x OR OR x 1 11

12 XOR Operator x y The result is 0 iff both bits are equal; otherwise, it is 1. For x = 0 or 1 1 XOR x NOT x x XOR 1 NOT x 12

13 The Application of XOR Operator x y x XOR y = 0 if x = y 1 if x y EQuality x y x EQ y 13

14 Example 4.2 The XOR operator is not actually a new operator. We can always simulate it using the other three operators. The following two expressions are equivalent x XOR y [x AND (NOT y)] OR [(NOT x) AND y] The equivalence can be proved if we make the truth table for both. 14

15 Logic Operations at Pattern Level The same four operators (NOT, AND, OR, and XOR) can be applied to an n-bit pattern. Figure 4.2 shows these four operators with input and output patterns. Figure 4.2 Logic operators applied to bit patterns 15

16 Example 4.3 Use the NOT operator on the bit pattern Solution The solution is shown below. Note that the NOT operator changes every 0 to 1 and every 1 to 0. 16

17 Example 4.4 Use the AND operator on the bit patterns and Solution Note that only one bit in the output is 1, where both corresponding inputs are 1s. 17

18 Example 4.5 Use the OR operator on the bit patterns and Solution Note that only one bit in the output is 0, where both corresponding inputs are 0s. 18

19 Example 4.6 Use the XOR operator on the bit patterns and Solution Compare the output in this example with the one in Example 4.5. The only difference is that when the two inputs are 1s, the result is 0 (the effect of exclusion). 19

20 Applications The four logic operations can be used to modify a bit pattern. Complementing (NOT) Unsetting (AND) Setting (OR) Flipping (XOR) 20

21 Mask The Target can be modified (unset/set/reverse specific bits) by the Binary Operator (AND/OR/XOR) with a Mask. 21

22 Example of Unsetting Specific Bits 22

23 Example 4.7 Use a mask to unset (clear) the 5 leftmost bits of a pattern. Test the mask with the pattern Solution The mask is Target AND Mask Result

24 Example of Setting Specific Bits 24

25 Example 4.8 Use a mask to set the 5 leftmost bits of a pattern. Test the mask with the pattern Solution The mask is Target OR Mask Result

26 Example of Flipping Specific Bits 26

27 Example 4.9 Use a mask to flip the 5 leftmost bits of a pattern. Test the mask with the pattern Solution Target XOR Mask Result

28 4.2 SHIFT OPERATIONS 28

29 Overview Shift operations move the bits in a pattern, changing the positions of the bits. They can move bits to the left or to the right. We can divide shift operations into two categories: logical shift operations and arithmetic shift operations. 29

30 Logical Shift Operations (1) A logical shift operation is applied to a pattern that does not represent a signed number. We distinguish two types of logical shift operations, as described below: Logical Shift Logical Circular Shift (Rotate) 30

31 Logical Shift Operations (2) Figure 4.3 Logical Shift Operations 31

32 Example 4.10 Use a logical left shift operation on the bit pattern Solution The leftmost bit is lost and a 0 is inserted as the rightmost bit. Discarded Added 32

33 Circular Shift Operations Figure 4.4 Circular Shift Operations 33

34 Example 4.11 Use a circular left shift operation on the bit pattern Solution The leftmost bit is circulated and becomes the rightmost bit. 34

35 Arithmetic Shift Operations Arithmetic shift operations assume that the bit pattern is a signed integer in two s complement format. Arithmetic right shift is used to divide an integer by two, while arithmetic left shift is used to multiply an integer by two. Figure 4.5 Arithmetic Shift Operations 35

36 Example 4.12 Use an arithmetic right shift operation on the bit pattern The pattern is an integer in two s s complement format. Solution The leftmost bit is retained and also copied to its right neighbor bit. The original number was 103 and the new number is 52, which is the result of dividing 103 by 2 truncated to the smaller integer. 36

37 Example 4.13 Use an arithmetic left shift operation on the bit pattern The pattern is an integer in two s s complement format. Solution The leftmost bit is lost and a 0 is inserted as the rightmost bit. The original number was 39 and the new number is 78. The original number is multiplied by two. The operation is valid because no underflow occurred. 37

38 Example 4.14 Use an arithmetic left shift operation on the bit pattern The pattern is an integer in two s s complement format. Solution The leftmost bit is lost and a 0 is inserted as the rightmost bit. The original number was 127 and the new number is 2. Here the result is not valid because an overflow has occurred. The expected answer = 254 cannot be represented by an 8-bit 8 pattern. 38

39 Example 4.15 Combining logic operations and logical shift operations give us some tools for manipulating bit patterns. Assume that we have a pattern and we need to use the third bit (from the right) of this pattern in a decision-making process. We want to know if this particular bit is 0 or 1. The following shows how we can find out. We can then test the result: if it is an unsigned integer 1, the target bit was 1, whereas if the result is an unsigned integer 0, the target bit was 0. 39

40 4.3 ARITHMETIC OPERATIONS 40

41 Arithmetic Operations on Integers We focus only on addition and subtraction because Multiplication is just repeated addition and Division is just repeated subtraction. 41

42 Two s Complement Integers When the subtraction operation is encountered, the computer simply changes it to an addition operation, but makes two s complement of the second number. In other words: A B A + (B + 1) Where B is the one s complement of B and (B + 1) means the two s complement of B 42

43 We should remember that we add integers column by column. The following table shows the sum and carry (C). 43

44 Figure 4.6 Addition and Subtraction of Integers in Two s Complement Format 44

45 Example 4.16 Two integers A and B are stored in two s s complement format. Show how B is added to A. A = ( ) 2 B = ( ) 2 Solution The operation is adding. A is added to B and the result is stored in R. (+17) + (+22) = (+39). 45

46 Example 4.17 Two integers A and B are stored in two s s complement format. Show how B is added to A. A = ( ) 2 B = ( ) 2 Solution The operation is adding. A is added to B and the result is stored in R. (+24) + ( 17)( = (+7). 46

47 Example 4.18 Two integers A and B are stored in two s s complement format. Show how B is subtracted from A. A = ( ) 2 B = ( ) 2 Solution The operation is subtracting. A is added to (B + 1) and the result is stored in R. (+24) ( 17) = (+41). 47

48 Example 4.19 Two integers A and B are stored in two s s complement format. Show how B is subtracted from A. A = ( ) 2 B = ( ) 2 Solution The operation is subtracting. A is added to (B + 1) and the result is stored in R. ( 35)( (+20) = ( 55).( 48

49 Example 4.20 Two integers A and B are stored in two s s complement format. Show how B is added to A. A = ( ) 2 B = ( ) 2 Solution The operation is adding. A is added to B and the result is stored in R. We expect the result to be = 130, but the answer is 126. The error is due to overflow, because the expected answer (+130) is not in the range 128 to

50 i When we do arithmetic operations on numbers in a computer, we should remember that each number and the result should be in the range defined by the bit allocation. 50

51 Sign-and-Magnitude Integers Addition and subtraction for integers in sign-andmagnitude representation looks very complex. We have four different combination of signs (two signs, each of two values) for addition and four different conditions for subtraction. This means that we need to consider eight different situations. However, if we first check the signs, we can reduce these cases, as shown in Figure

52 Figure 4.7 Addition and Subtraction of Integers in Sign-and- Magnitude Format 52

53 Example 4.22 Two integers A and B are stored in sign-and and-magnitude format. Show how B is added to A. A = ( ) 2 B = ( ) 2 Solution The operation is adding: the sign of B is not changed. S = A S XOR B S = 1; R M = A M + (B M +1). Since there is no overflow, we need to take the two s s complement of R M. The sign of R is the sign of B. (+17) + ( 22) = ( 5).( 53

54 Example 4.23 Two integers A and B are stored in sign-and and-magnitude format. Show how B is subtracted from A. A = ( ) 2 B = ( ) 2 Solution The operation is subtracting: S B = S B. S = A S XOR B S = 1, R M = A M + (B M +1). Since there is an overflow, the value of R M is final. The sign of R is the sign of A. ( 81)( ( 22) = ( 59).( 54

55 Arithmetic Operations on Reals All arithmetic operations such as addition, subtraction, multiplication and division can be applied to reals stored in floating-point format. Multiplication of two reals involves multiplication of two integers in sign-and-magnitude representation. Division of two reals involves division of two integers in sign-and-magnitude representations. We only show addition and subtractions for reals. 55

56 Addition and Subtraction of Reals Addition and subtraction of real numbers stored in floating-point numbers is reduced to addition and subtraction of two integers stored in sign-andmagnitude (combination of sign and mantissa) after the alignment of decimal points. Figure 4.8 shows a simplified version of the procedure (there are some special cases that we have ignored). 56

57 Figure 4.8 Addition and Subtraction of Reals in Floating-Point Format 57

58 Example 4.24 Show how the computer finds the result of (+5.75) + ( ) = ( ). Solution As we saw in Chapter 3, these two numbers are stored in floating- point format, as shown below, but we need to remember that each number has a hidden 1 (which is not stored, but assumed). 58

59 Example 4.24 (Continued) The first few steps in the UML diagram (Figure 4.8) are not needed. We de-normalize the numbers by adding the hidden 1s to the mantissa and incrementing the exponent. Now both de- normalized mantissas are 24 bits and include the hidden 1s. They should be stored in a location that can hold all 24 bits. Each exponent is incremented. 59

60 Example 4.24 (Continued) Now we do sign-and and-magnitude addition, treating the sign and the mantissa of each number as one integer stored in sign-and and- magnitude representation. There is no overflow in the mantissa, so we normalize. The mantissa is only 23 bits, no rounding is needed. E = ( ) 2 = 134 M = In other words, the result is ( ) = ( ) 2 =

61 Example 4.25 Show how the computer finds the result of (+5.75) + ( ) = Solution These two numbers can be stored in floating-point format, as shown below: De-normalization results in: 61

62 Example 4.25 (Continued) Alignment is not needed (both exponents are the same), so we apply addition operation on the combinations of sign and mantissa. The result is shown below, in which the sign of the result is negative: Now we need to normalize. We decrement the exponent three times and shift the de-normalized mantissa to the left three positions: 62

63 Example 4.25 (Continued) The mantissa is now 24 bits, so we round it to 23 bits. The result is R = expected = ,, as 63

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