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CS 115 Lecture 2 Fundamentals of computer science, computers, and programming Neil Moore Department of Computer Science University of Kentucky Lexington, Kentucky 40506 neil@cs.uky.edu 1 September 2015 What is programming? CS 115 is titled

  1. Algorithms “Step-by-step procedure” is a mouthful. We have a name for that: an algorithm . A “well-ordered collection of unambiguous and effectively computable operations that when executed produces a result and halts in a finite amount of time.” [Schneider and Gersting]. Named after the 9th-century Persian mathematician Muhammad ibn Musa al-Khwarizmi. Muhammad Al-Khwarizmi, Persian computer scientist. Sculpture: S. Ch. Babajan / Image: Michael Zaretski, Flickr. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 4 / 27

  2. Algorithms “Step-by-step procedure” is a mouthful. We have a name for that: an algorithm . A “well-ordered collection of unambiguous and effectively computable operations that when executed produces a result and halts in a finite amount of time.” [Schneider and Gersting]. Named after the 9th-century Persian mathematician Muhammad ibn Musa al-Khwarizmi. ◮ “The Compendious Book on Calculation by Restoring and Balancing” ◮ Described how to solve linear and quadratic equations. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 4 / 27

  3. Algorithms “Step-by-step procedure” is a mouthful. We have a name for that: an algorithm . A “well-ordered collection of unambiguous and effectively computable operations that when executed produces a result and halts in a finite amount of time.” [Schneider and Gersting]. Named after the 9th-century Persian mathematician Muhammad ibn Musa al-Khwarizmi. ◮ “The Compendious Book on Calculation by Restoring and Balancing” ◮ Described how to solve linear and quadratic equations. ◮ Arabic: Al-kitab al-mukhtasar fi hisab al-jabr wa’l-muqabala Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 4 / 27

  4. Algorithms “Step-by-step procedure” is a mouthful. We have a name for that: an algorithm . A “well-ordered collection of unambiguous and effectively computable operations that when executed produces a result and halts in a finite amount of time.” [Schneider and Gersting]. Named after the 9th-century Persian mathematician Muhammad ibn Musa al-Khwarizmi. ◮ “The Compendious Book on Calculation by Restoring and Balancing” ◮ Described how to solve linear and quadratic equations. ◮ Arabic: Al-kitab al-mukhtasar fi hisab al-jabr wa’l-muqabala ⋆ “Algebra” Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 4 / 27

  5. Algorithms “Step-by-step procedure” is a mouthful. We have a name for that: an algorithm . A “well-ordered collection of unambiguous and effectively computable operations that when executed produces a result and halts in a finite amount of time.” [Schneider and Gersting]. Named after the 9th-century Persian mathematician Muhammad ibn Musa al-Khwarizmi. ◮ “The Compendious Book on Calculation by Restoring and Balancing” ◮ Described how to solve linear and quadratic equations. ◮ Arabic: Al-kitab al-mukhtasar fi hisab al-jabr wa’l-muqabala ⋆ “Algebra” Let’s look at one algorithm that is even older than that: ◮ Euclid’s greatest common divisor algorithm. ◮ One of the oldest algorithms that is still in use. ⋆ In Euclid’s Elements , written around 300 BCE. ⋆ Older than long division! Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 4 / 27

  6. Algorithms “Step-by-step procedure” is a mouthful. We have a name for that: an algorithm . A “well-ordered collection of unambiguous and effectively computable operations that when executed produces a result and halts in a finite amount of time.” [Schneider and Gersting]. Named after the 9th-century Persian mathematician Muhammad ibn Musa al-Khwarizmi. ◮ “The Compendious Book on Calculation by Restoring and Balancing” ◮ Described how to solve linear and quadratic equations. ◮ Arabic: Al-kitab al-mukhtasar fi hisab al-jabr wa’l-muqabala ⋆ “Algebra” Let’s look at one algorithm that is even older than that: ◮ Euclid’s greatest common divisor algorithm. ◮ One of the oldest algorithms that is still in use. ⋆ In Euclid’s Elements , written around 300 BCE. ⋆ Older than long division! Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 4 / 27

  7. Euclid’s algorithm Given two numbers a and b , their greatest common divisor (GCD) is the largest number that both are divisible by. The GCD of 10 and 25 is 5; of 13 and 3 is 1. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 5 / 27

  8. Euclid’s algorithm Given two numbers a and b , their greatest common divisor (GCD) is the largest number that both are divisible by. The GCD of 10 and 25 is 5; of 13 and 3 is 1. Euclid designed an algorithm to compute the GCD: Inputs: two positive integers (whole numbers) a and b . Outputs: the GCD of a and b Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 5 / 27

  9. Euclid’s algorithm Given two numbers a and b , their greatest common divisor (GCD) is the largest number that both are divisible by. The GCD of 10 and 25 is 5; of 13 and 3 is 1. Euclid designed an algorithm to compute the GCD: Inputs: two positive integers (whole numbers) a and b . Outputs: the GCD of a and b 1 Repeat as long as b is not zero: 1.1 If a > b , then set a ← ( a − b ). 1.2 Otherwise, set b ← ( b − a ). Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 5 / 27

  10. Euclid’s algorithm Given two numbers a and b , their greatest common divisor (GCD) is the largest number that both are divisible by. The GCD of 10 and 25 is 5; of 13 and 3 is 1. Euclid designed an algorithm to compute the GCD: Inputs: two positive integers (whole numbers) a and b . Outputs: the GCD of a and b 1 Repeat as long as b is not zero: 1.1 If a > b , then set a ← ( a − b ). 1.2 Otherwise, set b ← ( b − a ). 2 Output a as the answer. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 5 / 27

  11. Euclid’s algorithm Given two numbers a and b , their greatest common divisor (GCD) is the largest number that both are divisible by. The GCD of 10 and 25 is 5; of 13 and 3 is 1. Euclid designed an algorithm to compute the GCD: Inputs: two positive integers (whole numbers) a and b . Outputs: the GCD of a and b 1 Repeat as long as b is not zero: 1.1 If a > b , then set a ← ( a − b ). 1.2 Otherwise, set b ← ( b − a ). 2 Output a as the answer. Euclid proved that this algorithm is correct (it gives the right answer) and effective (it always gives an answer). Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 5 / 27

  12. Euclid’s algorithm Given two numbers a and b , their greatest common divisor (GCD) is the largest number that both are divisible by. The GCD of 10 and 25 is 5; of 13 and 3 is 1. Euclid designed an algorithm to compute the GCD: Inputs: two positive integers (whole numbers) a and b . Outputs: the GCD of a and b 1 Repeat as long as b is not zero: 1.1 If a > b , then set a ← ( a − b ). 1.2 Otherwise, set b ← ( b − a ). 2 Output a as the answer. Euclid proved that this algorithm is correct (it gives the right answer) and Euclid, Greek computer scientist. effective (it always gives an answer). Sculpture: J. Durham / Image: Mark A. Wilson, Wikipedia, 2005. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 5 / 27

  13. Euclid’s algorithm Given two numbers a and b , their greatest common divisor (GCD) is the largest number that both are divisible by. The GCD of 10 and 25 is 5; of 13 and 3 is 1. Euclid designed an algorithm to compute the GCD: Inputs: two positive integers (whole numbers) a and b . Outputs: the GCD of a and b 1 Repeat as long as b is not zero: 1.1 If a > b , then set a ← ( a − b ). 1.2 Otherwise, set b ← ( b − a ). 2 Output a as the answer. Euclid proved that this algorithm is correct (it gives the right answer) and effective (it always gives an answer). Let’s try a few examples. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 5 / 27

  14. Euclid’s algorithm Given two numbers a and b , their greatest common divisor (GCD) is the largest number that both are divisible by. The GCD of 10 and 25 is 5; of 13 and 3 is 1. Euclid designed an algorithm to compute the GCD: Inputs: two positive integers (whole numbers) a and b . Outputs: the GCD of a and b 1 Repeat as long as b is not zero: 1.1 If a > b , then set a ← ( a − b ). 1.2 Otherwise, set b ← ( b − a ). 2 Output a as the answer. Euclid proved that this algorithm is correct (it gives the right answer) and effective (it always gives an answer). Let’s try a few examples. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 5 / 27

  15. Designing an algorithm In this class we will use the words “design”, “pseudocode”, and “algorithm” interchangeably. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 6 / 27

  16. Designing an algorithm In this class we will use the words “design”, “pseudocode”, and “algorithm” interchangeably. These are the steps to solve a problem. A design is like an outline or rough draft for your program. Figure out what you’re going to do before you start doing it! We’ll start with a non-computer example. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 6 / 27

  17. Designing an algorithm In this class we will use the words “design”, “pseudocode”, and “algorithm” interchangeably. These are the steps to solve a problem. A design is like an outline or rough draft for your program. Figure out what you’re going to do before you start doing it! We’ll start with a non-computer example. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 6 / 27

  18. Design: building a dog house Let’s say we want to build a dog house. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 7 / 27

  19. Design: building a dog house Let’s say we want to build a dog house. What steps do we need to take? Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 7 / 27

  20. Design: building a dog house Let’s say we want to build a dog house. What steps do we need to take? 1 Decide on a location and size for the doghouse. 2 Get materials for the house. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 7 / 27

  21. Design: building a dog house Let’s say we want to build a dog house. What steps do we need to take? 1 Decide on a location and size for the doghouse. 2 Get materials for the house. 3 Cut a piece of wood for the floor. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 7 / 27

  22. Design: building a dog house Let’s say we want to build a dog house. What steps do we need to take? 1 Decide on a location and size for the doghouse. 2 Get materials for the house. 3 Cut a piece of wood for the floor. 4 Cut wood for the four walls. 5 Cut a door into one wall. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 7 / 27

  23. Design: building a dog house Let’s say we want to build a dog house. What steps do we need to take? 1 Decide on a location and size for the doghouse. 2 Get materials for the house. 3 Cut a piece of wood for the floor. 4 Cut wood for the four walls. 5 Cut a door into one wall. 6 Assemble walls. 7 Attach walls to the floor. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 7 / 27

  24. Design: building a dog house Let’s say we want to build a dog house. What steps do we need to take? 1 Decide on a location and size for the doghouse. 2 Get materials for the house. 3 Cut a piece of wood for the floor. 4 Cut wood for the four walls. 5 Cut a door into one wall. 6 Assemble walls. 7 Attach walls to the floor. 8 Make roof. 9 Attach roof to walls. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 7 / 27

  25. Design: building a dog house Let’s say we want to build a dog house. What steps do we need to take? 1 Decide on a location and size for the doghouse. 2 Get materials for the house. 3 Cut a piece of wood for the floor. 4 Cut wood for the four walls. 5 Cut a door into one wall. 6 Assemble walls. 7 Attach walls to the floor. 8 Make roof. 9 Attach roof to walls. 10 Paint the outside. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 7 / 27

  26. Design: building a dog house Let’s say we want to build a dog house. What steps do we need to take? 1 Decide on a location and size for the doghouse. 2 Get materials for the house. 3 Cut a piece of wood for the floor. 4 Cut wood for the four walls. 5 Cut a door into one wall. 6 Assemble walls. 7 Attach walls to the floor. 8 Make roof. 9 Attach roof to walls. 10 Paint the outside. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 7 / 27

  27. Notes on the design Steps are numbered in the order they should be performed. ◮ If I try cutting the door after attaching the walls to the floor, it will be harder. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 8 / 27

  28. Notes on the design Steps are numbered in the order they should be performed. ◮ If I try cutting the door after attaching the walls to the floor, it will be harder. ◮ You’ll number your steps for the first few designs in class. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 8 / 27

  29. Notes on the design Steps are numbered in the order they should be performed. ◮ If I try cutting the door after attaching the walls to the floor, it will be harder. ◮ You’ll number your steps for the first few designs in class. Some steps could be further divided: ◮ “Get materials”: what materials? Where? Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 8 / 27

  30. Notes on the design Steps are numbered in the order they should be performed. ◮ If I try cutting the door after attaching the walls to the floor, it will be harder. ◮ You’ll number your steps for the first few designs in class. Some steps could be further divided: ◮ “Get materials”: what materials? Where? ◮ “Make roof”: cut at an angle, nail together, . . . Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 8 / 27

  31. Notes on the design Steps are numbered in the order they should be performed. ◮ If I try cutting the door after attaching the walls to the floor, it will be harder. ◮ You’ll number your steps for the first few designs in class. Some steps could be further divided: ◮ “Get materials”: what materials? Where? ◮ “Make roof”: cut at an angle, nail together, . . . “Cut wood for the four walls”: a repeated step. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 8 / 27

  32. Notes on the design Steps are numbered in the order they should be performed. ◮ If I try cutting the door after attaching the walls to the floor, it will be harder. ◮ You’ll number your steps for the first few designs in class. Some steps could be further divided: ◮ “Get materials”: what materials? Where? ◮ “Make roof”: cut at an angle, nail together, . . . “Cut wood for the four walls”: a repeated step. Could go into more detail: how big is a wall, the floor, etc.? Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 8 / 27

  33. Notes on the design Steps are numbered in the order they should be performed. ◮ If I try cutting the door after attaching the walls to the floor, it will be harder. ◮ You’ll number your steps for the first few designs in class. Some steps could be further divided: ◮ “Get materials”: what materials? Where? ◮ “Make roof”: cut at an angle, nail together, . . . “Cut wood for the four walls”: a repeated step. Could go into more detail: how big is a wall, the floor, etc.? What’s the budget? Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 8 / 27

  34. Dog house, refined 1 Decide on a location and size for the doghouse. 2 Get materials for the house. Get lumber. 1 Get paint. 2 Get nails. 3 3 Cut a piece of wood for the floor. 4 Repeat four times: Cut a piece of wood for a wall. 1 5 Cut a door into one wall. 6 Attach walls to the floor. 7 Make roof. Cut two pieces of wood. 1 Join the pieces at a 90 degree angle. 2 Nail the pieces together. 3 8 Attach roof to walls. 9 Paint the outside. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 9 / 27

  35. Dog house, refined 1 Decide on a location and size for the doghouse. 2 Get materials for the house. Get lumber. 1 Get paint. 2 Get nails. 3 3 Cut a piece of wood for the floor. 4 Repeat four times: Cut a piece of wood for a wall. 1 5 Cut a door into one wall. 6 Attach walls to the floor. 7 Make roof. Cut two pieces of wood. 1 Join the pieces at a 90 degree angle. 2 Nail the pieces together. 3 8 Attach roof to walls. 9 Paint the outside. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 9 / 27

  36. The first computers The first automatic computers were designed to solve one specific problem. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 10 / 27

  37. The first computers The first automatic computers were designed to solve one specific problem. The Antikythera mechanism was built around 100 BCE for calendar and astronomical calculations. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 10 / 27

  38. The first computers The first automatic computers were designed to solve one specific problem. The Antikythera mechanism was built around 100 BCE for calendar and astronomical calculations. The Antikythera mechanism Image: user “Marsyas”, Wikimedia Commons, 20 December 2005. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 10 / 27

  39. The first computers The first automatic computers were designed to solve one specific problem. The Antikythera mechanism was built around 100 BCE for calendar and astronomical calculations. Charles Babbage designed the difference engine, 1823–1842, to compute values of polynomials. ◮ Never finished in his lifetime. ◮ Finally built in 1991. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 10 / 27

  40. The first computers The first automatic computers were designed to solve one specific problem. The Antikythera mechanism was built around 100 BCE for calendar and astronomical calculations. Charles Babbage designed the difference engine, 1823–1842, to compute values of polynomials. ◮ Never finished in his lifetime. Babbage’s difference engine, built 1991 ◮ Finally built in 1991. Image: Allan J. Cronin, Wikipedia, March 2009. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 10 / 27

  41. The first computers The first automatic computers were designed to solve one specific problem. The Antikythera mechanism was built around 100 BCE for calendar and astronomical calculations. Charles Babbage designed the difference engine, 1823–1842, to compute values of polynomials. ◮ Never finished in his lifetime. ◮ Finally built in 1991. ◮ And it worked! Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 10 / 27

  42. The first computers The first automatic computers were designed to solve one specific problem. The Antikythera mechanism was built around 100 BCE for calendar and astronomical calculations. Charles Babbage designed the difference engine, 1823–1842, to compute values of polynomials. ◮ Never finished in his lifetime. ◮ Finally built in 1991. ◮ And it worked! Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 10 / 27

  43. Programming early computers Early computers were designed to solve one specific problem. Some could be reprogrammed by flipping switches or plugging in cables. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 11 / 27

  44. Programming early computers Early computers were designed to solve one specific problem. Some could be reprogrammed by flipping switches or plugging in cables. ◮ Flip switches to enter a number into the “store”. ◮ Connect cables from the store to the adder, multiplier, etc. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 11 / 27

  45. Programming early computers Early computers were designed to solve one specific problem. Some could be reprogrammed by flipping switches or plugging in cables. ◮ Flip switches to enter a number into the “store”. ◮ Connect cables from the store to the adder, multiplier, etc. ◮ Setting up the machine to solve a problem could take days. ⋆ Even if you already know which cables should go where. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 11 / 27

  46. Programming early computers Early computers were designed to solve one specific problem. Some could be reprogrammed by flipping switches or plugging in cables. ◮ Flip switches to enter a number into the “store”. ◮ Connect cables from the store to the adder, multiplier, etc. ◮ Setting up the machine to solve a problem could take days. ⋆ Even if you already know which cables should go where. But still, that was faster and more accurate than humans. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 11 / 27

  47. Programming early computers Early computers were designed to solve one specific problem. Some could be reprogrammed by flipping switches or plugging in cables. ◮ Flip switches to enter a number into the “store”. ◮ Connect cables from the store to the adder, multiplier, etc. ◮ Setting up the machine to solve a problem could take days. ⋆ Even if you already know which cables should go where. But still, that was faster and more accurate than humans. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 11 / 27

  48. Stored programs British mathematician Alan Turing described in 1936 a mathematical model of how machines can compute. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 12 / 27

  49. Stored programs British mathematician Alan Turing described in 1936 a mathematical model of how machines can compute. Alan Turing, British computer scientist. Image: National Portrait Gallery, London, 1951. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 12 / 27

  50. Stored programs British mathematician Alan Turing described in 1936 a mathematical model of how machines can compute. ◮ A founder of modern computer science. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 12 / 27

  51. Stored programs British mathematician Alan Turing described in 1936 a mathematical model of how machines can compute. ◮ A founder of modern computer science. He realized that you could make a universal machine ◮ It would take as part of its input a description of the program to run. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 12 / 27

  52. Stored programs British mathematician Alan Turing described in 1936 a mathematical model of how machines can compute. ◮ A founder of modern computer science. He realized that you could make a universal machine ◮ It would take as part of its input a description of the program to run. ◮ Programs become just another kind of data! ◮ John von Neumann developed these ideas further in 1944. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 12 / 27

  53. Stored programs British mathematician Alan Turing described in 1936 a mathematical model of how machines can compute. ◮ A founder of modern computer science. He realized that you could make a universal machine ◮ It would take as part of its input a description of the program to run. ◮ Programs become just another kind of data! ◮ John von Neumann developed these ideas further in 1944. John von Neumann, Hungarian-American computer scientist. Image: Los Alamos National Lab. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 12 / 27

  54. Stored programs British mathematician Alan Turing described in 1936 a mathematical model of how machines can compute. ◮ A founder of modern computer science. He realized that you could make a universal machine ◮ It would take as part of its input a description of the program to run. ◮ Programs become just another kind of data! ◮ John von Neumann developed these ideas further in 1944. Turing later went on to develop the bombe to break WWII encryption. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 12 / 27

  55. Stored programs British mathematician Alan Turing described in 1936 a mathematical model of how machines can compute. ◮ A founder of modern computer science. He realized that you could make a universal machine ◮ It would take as part of its input a description of the program to run. ◮ Programs become just another kind of data! ◮ John von Neumann developed these ideas further in 1944. Turing later went on to develop the bombe to break WWII encryption. Somebody set up us the bombe (reproduction). Image: Antoine Taveneaux, Wikipedia, 18 June 2012. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 12 / 27

  56. Stored programs British mathematician Alan Turing described in 1936 a mathematical model of how machines can compute. ◮ A founder of modern computer science. He realized that you could make a universal machine ◮ It would take as part of its input a description of the program to run. ◮ Programs become just another kind of data! ◮ John von Neumann developed these ideas further in 1944. Turing later went on to develop the bombe to break WWII encryption. ◮ Germany used the Enigma machine to encrypt wartime messages. ◮ The bombe figured out which settings the Enigma used each day. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 12 / 27

  57. Stored programs British mathematician Alan Turing described in 1936 a mathematical model of how machines can compute. ◮ A founder of modern computer science. He realized that you could make a universal machine ◮ It would take as part of its input a description of the program to run. ◮ Programs become just another kind of data! ◮ John von Neumann developed these ideas further in 1944. Turing later went on to develop the bombe to break WWII encryption. ◮ Germany used the Enigma machine to encrypt wartime messages. ◮ The bombe figured out which settings the Enigma used each day. ◮ 2014 film: The Imitation Game ◮ “The Imitation Game” was his name for what we now call the “Turing test”: how can we tell whether a computer is intelligent? Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 12 / 27

  58. Stored programs British mathematician Alan Turing described in 1936 a mathematical model of how machines can compute. ◮ A founder of modern computer science. He realized that you could make a universal machine ◮ It would take as part of its input a description of the program to run. ◮ Programs become just another kind of data! ◮ John von Neumann developed these ideas further in 1944. Turing later went on to develop the bombe to break WWII encryption. ◮ Germany used the Enigma machine to encrypt wartime messages. ◮ The bombe figured out which settings the Enigma used each day. ◮ 2014 film: The Imitation Game ◮ “The Imitation Game” was his name for what we now call the “Turing test”: how can we tell whether a computer is intelligent? A sad end. ◮ In 1952, it came out that Turing was gay: illegal at the time. ◮ He was convicted and sentenced to chemical castration (hormones). ◮ Committed suicide in 1954. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 12 / 27

  59. Stored programs British mathematician Alan Turing described in 1936 a mathematical model of how machines can compute. ◮ A founder of modern computer science. He realized that you could make a universal machine ◮ It would take as part of its input a description of the program to run. ◮ Programs become just another kind of data! ◮ John von Neumann developed these ideas further in 1944. Turing later went on to develop the bombe to break WWII encryption. ◮ Germany used the Enigma machine to encrypt wartime messages. ◮ The bombe figured out which settings the Enigma used each day. ◮ 2014 film: The Imitation Game ◮ “The Imitation Game” was his name for what we now call the “Turing test”: how can we tell whether a computer is intelligent? A sad end. ◮ In 1952, it came out that Turing was gay: illegal at the time. ◮ He was convicted and sentenced to chemical castration (hormones). ◮ Committed suicide in 1954. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 12 / 27

  60. Parts of a modern computer RAM: the computer’s “working memory”. ◮ “Random Access Memory” Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 13 / 27

  61. Parts of a modern computer RAM: the computer’s “working memory”. ◮ “Random Access Memory” ◮ Made up of cells (words), each holding a number. ⋆ Represented in binary. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 13 / 27

  62. Parts of a modern computer RAM: the computer’s “working memory”. ◮ “Random Access Memory” ◮ Made up of cells (words), each holding a number. ⋆ Represented in binary. ◮ Volatile: information is lost when the power goes out. ◮ Fast (nanoseconds) ◮ Relatively expensive. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 13 / 27

  63. Parts of a modern computer RAM: the computer’s “working memory”. ◮ “Random Access Memory” ◮ Made up of cells (words), each holding a number. ⋆ Represented in binary. ◮ Volatile: information is lost when the power goes out. ◮ Fast (nanoseconds) ◮ Relatively expensive. ◮ Von Neumann architecture: CPU reads instructions from RAM. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 13 / 27

  64. Parts of a modern computer RAM: the computer’s “working memory”. ◮ “Random Access Memory” ◮ Made up of cells (words), each holding a number. ⋆ Represented in binary. ◮ Volatile: information is lost when the power goes out. ◮ Fast (nanoseconds) ◮ Relatively expensive. ◮ Von Neumann architecture: CPU reads instructions from RAM. Secondary storage: hard drives, flash, DVD, . . . ◮ Persistent: data can be stored for years or decades. ◮ Slow (microseconds to milliseconds: < 1 / 1000 the speed of RAM) ◮ Relatively cheap. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 13 / 27

  65. Parts of a modern computer RAM: the computer’s “working memory”. ◮ “Random Access Memory” ◮ Made up of cells (words), each holding a number. ⋆ Represented in binary. ◮ Volatile: information is lost when the power goes out. ◮ Fast (nanoseconds) ◮ Relatively expensive. ◮ Von Neumann architecture: CPU reads instructions from RAM. Secondary storage: hard drives, flash, DVD, . . . ◮ Persistent: data can be stored for years or decades. ◮ Slow (microseconds to milliseconds: < 1 / 1000 the speed of RAM) ◮ Relatively cheap. ◮ Data must be transferred to RAM before the CPU can use it. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 13 / 27

  66. Parts of a modern computer RAM: the computer’s “working memory”. ◮ “Random Access Memory” ◮ Made up of cells (words), each holding a number. ⋆ Represented in binary. ◮ Volatile: information is lost when the power goes out. ◮ Fast (nanoseconds) ◮ Relatively expensive. ◮ Von Neumann architecture: CPU reads instructions from RAM. Secondary storage: hard drives, flash, DVD, . . . ◮ Persistent: data can be stored for years or decades. ◮ Slow (microseconds to milliseconds: < 1 / 1000 the speed of RAM) ◮ Relatively cheap. ◮ Data must be transferred to RAM before the CPU can use it. CPU: Central Processing Unit. ◮ Reads instructions from RAM. ◮ Executes (carries them out) in order. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 13 / 27

  67. Parts of a modern computer RAM: the computer’s “working memory”. ◮ “Random Access Memory” ◮ Made up of cells (words), each holding a number. ⋆ Represented in binary. ◮ Volatile: information is lost when the power goes out. ◮ Fast (nanoseconds) ◮ Relatively expensive. ◮ Von Neumann architecture: CPU reads instructions from RAM. Secondary storage: hard drives, flash, DVD, . . . ◮ Persistent: data can be stored for years or decades. ◮ Slow (microseconds to milliseconds: < 1 / 1000 the speed of RAM) ◮ Relatively cheap. ◮ Data must be transferred to RAM before the CPU can use it. CPU: Central Processing Unit. ◮ Reads instructions from RAM. ◮ Executes (carries them out) in order. ◮ Simple instructions: add numbers, is-equal, skip to another instruction. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 13 / 27

  68. Parts of a modern computer RAM: the computer’s “working memory”. ◮ “Random Access Memory” ◮ Made up of cells (words), each holding a number. ⋆ Represented in binary. ◮ Volatile: information is lost when the power goes out. ◮ Fast (nanoseconds) ◮ Relatively expensive. ◮ Von Neumann architecture: CPU reads instructions from RAM. Secondary storage: hard drives, flash, DVD, . . . ◮ Persistent: data can be stored for years or decades. ◮ Slow (microseconds to milliseconds: < 1 / 1000 the speed of RAM) ◮ Relatively cheap. ◮ Data must be transferred to RAM before the CPU can use it. CPU: Central Processing Unit. ◮ Reads instructions from RAM. ◮ Executes (carries them out) in order. ◮ Simple instructions: add numbers, is-equal, skip to another instruction. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 13 / 27

  69. Computer units RAM consists of bits: a circuit that stores a 1 or a 0. Bits are combined into bytes , usually 8 bits. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 14 / 27

  70. Computer units RAM consists of bits: a circuit that stores a 1 or a 0. Bits are combined into bytes , usually 8 bits. ◮ Binary numbers : places are powers of two ⋆ 1, 2, 4, 8, 16, 32, 64, 128 Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 14 / 27

  71. Computer units RAM consists of bits: a circuit that stores a 1 or a 0. Bits are combined into bytes , usually 8 bits. ◮ Binary numbers : places are powers of two ⋆ 1, 2, 4, 8, 16, 32, 64, 128 ◮ So 01001011 Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 14 / 27

  72. Computer units RAM consists of bits: a circuit that stores a 1 or a 0. Bits are combined into bytes , usually 8 bits. ◮ Binary numbers : places are powers of two ⋆ 1, 2, 4, 8, 16, 32, 64, 128 ◮ So 0100101 1 = 1 Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 14 / 27

  73. Computer units RAM consists of bits: a circuit that stores a 1 or a 0. Bits are combined into bytes , usually 8 bits. ◮ Binary numbers : places are powers of two ⋆ 1, 2, 4, 8, 16, 32, 64, 128 ◮ So 010010 1 1 = 1 + 2 Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 14 / 27

  74. Computer units RAM consists of bits: a circuit that stores a 1 or a 0. Bits are combined into bytes , usually 8 bits. ◮ Binary numbers : places are powers of two ⋆ 1, 2, 4, 8, 16, 32, 64, 128 ◮ So 0100 1 011 = 1 + 2 + 8 Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 14 / 27

  75. Computer units RAM consists of bits: a circuit that stores a 1 or a 0. Bits are combined into bytes , usually 8 bits. ◮ Binary numbers : places are powers of two ⋆ 1, 2, 4, 8, 16, 32, 64, 128 ◮ So 0 1 001011 = 1 + 2 + 8 + 64 Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 14 / 27

  76. Computer units RAM consists of bits: a circuit that stores a 1 or a 0. Bits are combined into bytes , usually 8 bits. ◮ Binary numbers : places are powers of two ⋆ 1, 2, 4, 8, 16, 32, 64, 128 ◮ So 01001011 = 1 + 2 + 8 + 64 = 75 Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 14 / 27

  77. Computer units RAM consists of bits: a circuit that stores a 1 or a 0. Bits are combined into bytes , usually 8 bits. ◮ Binary numbers : places are powers of two ⋆ 1, 2, 4, 8, 16, 32, 64, 128 ◮ So 01001011 = 1 + 2 + 8 + 64 = 75 ◮ (More about this in chapter 3). Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 14 / 27

  78. Computer units RAM consists of bits: a circuit that stores a 1 or a 0. Bits are combined into bytes , usually 8 bits. ◮ Binary numbers : places are powers of two ⋆ 1, 2, 4, 8, 16, 32, 64, 128 ◮ So 01001011 = 1 + 2 + 8 + 64 = 75 ◮ (More about this in chapter 3). ◮ One byte can represent a number from 0 to 255. ⋆ Or a single character in ASCII code. Neil Moore (UK CS) CS 115 Lecture 2 Fall 2015 14 / 27

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