First 20 Greatest Inventions in Computer Programming

In one of the conversations I’ve had with our senior editor, I was asked the question, "How did the first programmers program?” This led to a discussion about Babbage and Ada Lovelace, at the end of which, I got assigned to researchhow it all began – it being the many firsts in computing history.

I dug into books and websites and was met with many revelations: Babbage isn’t technically the inventor of the computer, FORTRAN most definitely wasn’t the first high level programming language and we used to have styluses for CRT screens (really).

I was also surprised to find out how essential wars were in fueling the rapid progress of computer development, plus the contribution of many women that our textbooks chose to neglect. Hence, to fill the gaps in the history of computers and programs, I’ve put together 20 firsts in the world of computing, from bowling balls to WW2 and all that is in between.

1. First Computer: "Difference engine" (1821)

The "Difference Engine" was a proposed mechanical computer to be used to output mathematical tables. Commissioned by the British government, Charles Babbage (aka Father of Computer) started working on it, but due to its high production cost, the funding was stopped and the machine was never completed.

2. First General Purpose Computer: "Analytical Engine" (1834)

The "Analytical Engine" was also a proposed mechanical computer, the input of which was supposed to be punched cards, with programs and data punched on them. Another brainchild of Charles Babbage, this machine was also not completed.

3. First Computer Program: algorithm to compute Bernoulli numbers (1841 – 1842)

Ada Lovelace (world’s first computer programmer) began translating Italian mathematician Luigi Menabrea’s records on Babbage’s analytical engine in 1841. During the translation she became interested in the machine and left notes with her translation. One of the notes – note G, contained the algorithm to compute Bernoulli numbers by the analytical engine, considered to be the very first computer program.

4. First Working Programmable Computer: Z3 (1941)

Konrad Zuse (the Inventor of Computers) already had a working mechanical computer Z1 but it worked for only few minutes at a time. The use of a different technology – relays, led to Z2 and eventually Z3. Z3 was an electromagnetic computer for which program and data were stored on external punched tapes. It was a secret project of the German government and put to use by The German Aircraft Research Institute. The original machine was destroyed in the bombing of Berlin in 1943.

5. First Electronic Computer: Atanasoff-Berry Computer (ABC) (1942)

Created by John Vincent Atanasoff & Clifford Berry, the hence named Atanasoff-Berry Computer or ABC was used to find the solution for simultaneous linear equations. It was the very first computer that used binary to represent data and electronic switches instead of mechanical. The computer however was not programmable.

6. First Programmable Electronic Computer: Colossus (1943)

The Colossus created by Tommy Flowers, was a machine created to help the British decrypt German messages that were encrypted by the Lorenz cipher, back in World War II. It was programmed by electronic switches and plugs. Colossus brought the time to decipher the encrypted messages down from weeks to mere hours.

7. First General Purpose Programmable Electronic Computer: ENIAC (1946)

Funded by the US Army, ENIAC or Electronic Numerical Integrator And Computerwas developed in the Moore School of Electrical Engineering, University of Pennsylvania by by John Mauchly & J. Presper Eckert. ENIAC was 150 feet wide and could be programmed to perform complex operations like loops; programming was done by altering its electronic switches and cables. It used card readers for input and card punches for output. It helped with computations for feasibility of the world’s first hydrogen bomb.

8. First Trackball: (1946/1952)

Why the two years for the first trackball? Allow me to explain.

The first year was the year given by a Ralph Benjamin, who claimed to have created the world’s first trackball back when he was working on a monitoring system for low-flying aircraft in 1946. The invention he described used a ball to control the X-Y coordinates of a cursor on screen. The design was patented in 1947 but was never released because it’s considered a "military secret". The military opted for the joystick instead.

The second contender for world’s first trackball, used in the Canadian Navy’s DATAR system back in 1952 was invented by Tom Cranston and co. This trackball design had a mock up which utilized a Canadian bowling ball spun on "air bearings" (see image below).

9. First Stored-Program Computer: SSEM (1948)

To overcome the shortcomings of delay-line memory, Frederic C. Williams and Tom Kilburn had developed the first random-access digital storage device based on the standard CRT. The SSEM (Manchester Small-Scale Experimental Machine) was used to implement that storage device for practical usage. The programs were entered in binary form using 32 switches and its output was a CRT.

10. First High-Level Programming Language: Plankalkül (1948)

Although Konrad Zuse started working on Plankalkül since 1943, it was only in 1948 when he published a paper about it. It did not attract much attention unfortunately. It would take close to three decades later for a compiler to be implemented for it, one created by a Joachim Hohmann in a dissertation.

11. First Assembler: "Initial Orders" for EDSAC (1949)

Assembler is a program that converts mnemonics (low-level) into numeric representation (machine code).
The initial orders in EDSAC (Electronic Delay Storage Automatic Calculator) was the first of such a system.
It was used to assemble programs from paper tape input into the memory and running the input.
The programs were in mnemonic codes instead of machine codes, making “initial code” the first ever assembler by processing a symbolic low level program code into machine code.

12. First Personal Computer: "Simon" (1950)

"Simon" by Edmund Berkeley was the first affordable digital computer that could perform four operations: addition, negation, greater than, and selection. The input was punched paper, and the program ran on paper tape. The only output were through five lights.

13. First Compiler: A-0 for UNIVAC 1 (1952)

A compiler is a program that converts high-level language into machine code. The A-0 System was a program created by the legendary Grace Hopper to convert a program specified as a sequence of subroutines and arguments into machine code. The A-0 later evolved into A-2 and was released to customers with its source code making it possibly the very first open source software.

14. First Autocode: Glennie’s Autocode (1952)

An Autocode is a high-level programming language that uses a compiler. The first autocode and its compiler appeared at the University of Manchester to make the programming of the Mark 1 machine more intelligible. It was created by Alick Glennie, hence the name Glennie’s Autocode.

15. First Real-Time Graphics Display Computer: AN/FSQ-7 by IBM (1951)

AN/FSQ-7 was based on one of the first computers that showed real-time output, Whirlwind. It became the lifeline for the US Air Defense system known as Semi-Automatic Ground Environment (SAGE). The computers showed tracks for the targets and automatically showed which defences were within range. AN/FSQ-7 had 100 system consoles; here’s one (image below), the OA-1008 Situation Display (SD), with a light gun used to select targets on screen for further information.

16. First Widely Used High Level Programming Language: FORTRAN (1957)

If you check the textbooks, you will find FORTRAN listed as the first high level programming language. Thought up by John W. Backus who disliked writing programs  and decided to create a programming system to help make the process much easier, the use of FORTRAN greatly reduced the number of programming statements required to get a machine running. By 1963, more than 40 FORTRAN compilers were already available.

17. First Mouse (1964)

It was while sitting in a conference session on computer graphics that the idea of a mouse came to Douglas Engelbart in 1964. He thought up a device with a pair of small wheels (one turns horizontally the other vertically) which can be used to move a cursor on a screen. A prototype (see below) was created by his lead engineer, Bill English but both English and Engelbart never received royalties for the design because technically, it belonged to SRI, Engelbert’s employer.

18. First Commercial Desktop Computer: Programma 101 (1965)

Also known as Perottina, Programma 101 was the world’s first commercial PC. It could perform addition, subtraction, multiplication, division, square root, absolute value, and fraction. For all that it could do, it was priced at $3,200 (it was a very different time) and managed to sell 44,000 units. Perottina was invented by Pier Giorgio Perotto and produced by Olivetti, an Italian manufacturer.

19. First Touchscreen (1965)

It doesn’t look like much but this was the first touchscreen the world has ever known. It’s a capacitative touchscreen panel, with no pressure sensitivity (there’s either contact, or no contact) and it only registers a single point of contact (as opposed to multitouch). The concept was adopted for use by air traffic controllers in the UK up until the 1990s.

20. First Object Oriented Programming Language: Simula (1967)

Based on C. A. R. Hoare’s concept of class constructs, Ole-Johan Dahl & Kristen Nygaard updated their "SIMULA I" programming language with objects, classes and subclasses. This resulted in the creation of SIMULA 67 which became the first object-oriented programming language.

Final thoughts

As much as this post was about what we could learn about the many firsts in computing history it is hard to immerse ourselves in history itself. As a result, at least for me, we become more appreciative about the work done by generations before ours, and we can better understand what drives the many changes that shape the world that we live in today.

I hope this post inspires you as much as it inspired me. Share your thoughts on these firsts, and if I missed any, which I’m sure I did, do add them in the comments.

Data Structures & Algorithm

What is a Data Structure?

Data Structure is a systematic way to organize data in order to use it efficiently. Following terms are foundation terms of a data structure.

• Interface − Each data structure has an interface. Interface represents the set of operations that a data structure supports. An interface only provides the list of supported operations, type of parameters they can accept and return type of these operations.
• Implementation − Implementation provides the internal representation of a data structure. Implementation also provides the definition of the algorithms used in the operations of the data structure.

Characteristics of a Data Structure

• Correctness − Data Structure implementation should implement its interface correctly.
• Time Complexity − Running time or execution time of operations of data structure must be as small as possible.
• Space Complexity − Memory usage of a data structure operation should be as little as possible.

Need for Data Structure

As applications are getting complex and data rich, there are three common problems applications face now-a-days.

• Data Search − Consider an inventory of 1 million(10⁶) items of a store. If application is to search an item. It has to search item in 1 million(10⁶) items every time slowing down the search. As data grows, search will become slower.
• Processor speed − Processor speed although being very high, falls limited if data grows to billion records.
• Multiple requests − As thousands of users can search data simultaneously on a web server,even very fast server fails while searching the data.

To solve above problems, data structures come to rescue. Data can be organized in a data structure in such a way that all items may not be required to be search and required data can be searched almost instantly.

Execution Time Cases

There are three cases which are usual used to compare various data structure's execution time in relative manner.

• Worst Case − This is the scenario where a particular data structure operation takes maximum time it can take. If a operation's worst case time is ƒ(n) then this operation will not take time more than ƒ(n) time where ƒ(n) represents function of n.
• Average Case − This is the scenario depicting the average execution time of an operation of a data structure. If a operation takes ƒ(n) time in execution then m operations will take mƒ(n) time.
• Best Case − This is the scenario depicting the least possible execution time of an operation of a data structure. If a operation takes ƒ(n) time in execution then actual operation may take time as random number which would be maximum as ƒ(n).

Basic Terminology

• Data − Data are values or set of values.
• Data Item − Data item refers to single unit of values.
• Group Items − Data item that are divided into sub items are called as Group Items.
• Elementary Items − Data item that cannot be divided are called as Elementary Items.
• Attribute and Entity − An entity is that which contains certain attributes or properties which may be assigned values.
• Entity Set − Entities of similar attributes form an entity set.
• Field − Field is a single elementary unit of information representing an attribute of an entity.
• Record − Record is a collection of field values of a given entity.
• File − File is a collection of records of the entities in a given entity set.
• Local Environment Setup
• If you are still willing to set up your environment for C programming language, you need the following two softwares available on your computer, (a) Text Editor and (b) The C Compiler.

• Text Editor

  This will be used to type your program. Examples of few editors include Windows Notepad, OS Edit command, Brief, Epsilon, EMACS, and vim or vi.
  Name and version of text editor can vary on different operating systems. For example, Notepad will be used on Windows, and vim or vi can be used on windows as well as Linux or UNIX.
  The files you create with your editor are called source files and contain program source code. The source files for C programs are typically named with the extension ".c".
  Before starting your programming, make sure you have one text editor in place and you have enough experience to write a computer program, save it in a file, compile it and finally execute it.

  The C Compiler

  The source code written in source file is the human readable source for your program. It needs to be "compiled", to turn into machine language so that your CPU can actually execute the program as per instructions given.
  This C programming language compiler will be used to compile your source code into final executable program. I assume you have basic knowledge about a programming language compiler.
  Most frequently used and free available compiler is GNU C/C++ compiler, otherwise you can have compilers either from HP or Solaris if you have respective Operating Systems.
  Following section guides you on how to install GNU C/C++ compiler on various OS. I'm mentioning C/C++ together because GNU gcc compiler works for both C and C++ programming languages.

  Installation on UNIX/Linux

  If you are using Linux or UNIX, then check whether GCC is installed on your system by entering the following command from the command line −

  $ gcc -v
  

  If you have GNU compiler installed on your machine, then it should print a message something as follows −

  Using built-in specs.
  Target: i386-redhat-linux
  Configured with: ../configure --prefix=/usr .......
  Thread model: posix
  gcc version 4.1.2 20080704 (Red Hat 4.1.2-46)
  

  If GCC is not installed, then you will have to install it yourself using the detailed instructions available at http://gcc.gnu.org/install/
  This tutorial has been written based on Linux and all the given examples have been compiled on Cent OS flavor of Linux system.

  Installation on Mac OS

  If you use Mac OS X, the easiest way to obtain GCC is to download the Xcode development environment from Apple's web site and follow the simple installation instructions. Once you have Xcode setup, you will be able to use GNU compiler for C/C++.
  Xcode is currently available at developer.apple.com/technologies/tools/.

  Installation on Windows

  To install GCC at Windows you need to install MinGW. To install MinGW, go to the MinGW homepage, www.mingw.org, and follow the link to the MinGW download page. Download the latest version of the MinGW installation program, which should be named MinGW-<version>.exe.
  While installing MinWG, at a minimum, you must install gcc-core, gcc-g++, binutils, and the MinGW runtime, but you may wish to install more.
  Add the bin subdirectory of your MinGW installation to your PATH environment variable, so that you can specify these tools on the command line by their simple names.
  When the installation is complete, you will be able to run gcc, g++, ar, ranlib, dlltool, and several other GNU tools from the Windows command line

• Data Structures - Algorithms Basics

• Algorithm is a step by step procedure, which defines a set of instructions to be executed in certain order to get the desired output. Algorithms are generally created independent of underlying languages, i.e. an algorithm can be implemented in more than one programming language.

  From data structure point of view, following are some important categories of algorithms −

  • Search − Algorithm to search an item in a datastructure.
  • Sort − Algorithm to sort items in certain order
  • Insert − Algorithm to insert item in a datastructure
  • Update − Algorithm to update an existing item in a data structure
  • Delete − Algorithm to delete an existing item from a data structure

  Characteristics of an Algorithm

  Not all procedures can be called an algorithm. An algorithm should have the below mentioned characteristics −

  • Unambiguous − Algorithm should be clear and unambiguous. Each of its steps (or phases), and their input/outputs should be clear and must lead to only one meaning.
  • Input − An algorithm should have 0 or more well defined inputs.
  • Output − An algorithm should have 1 or more well defined outputs, and should match the desired output.
  • Finiteness − Algorithms must terminate after a finite number of steps.
  • Feasibility − Should be feasible with the available resources.
  • Independent − An algorithm should have step-by-step directions which should be independent of any programming code.

  How to write an algorithm?

  There are no well-defined standards for writing algorithms. Rather, it is problem and resource dependent. Algorithms are never written to support a particular programming code.

  As we know that all programming languages share basic code constructs like loops (do, for, while), flow-control (if-else) etc. These common constructscan be used to write an algorithm.

  We write algorithms in step by step manner, but it is not always the case. Algorithm writing is a process and is executed after the problem domain is well-defined. That is, we should know the problem domain, for which we are designing a solution.

  Example

  Let's try to learn algorithm-writing by using an example.

  Problem − Design an algorithm to add two numbers and display result.

  step 1 − START
  step 2 − declare three integers a, b & c
  step 3 − define values of a & b
  step 4 − add values of a & b
  step 5 − store output of step 4 to c
  step 6 − print c
  step 7 − STOP
  

  Algorithms tell the programmers how to code the program. Alternatively the algorithm can be written as −

  step 1 − START ADD
  step 2 − get values of a & b
  step 3 − c ← a + b
  step 4 − display c
  step 5 − STOP
  

  In design and analysis of algorithms, usually the second method is used to describe an algorithm. It makes it easy of the analyst to analyze the algorithm ignoring all unwanted definitions. He can observe what operations are being used and how the process is flowing.

  Writing step numbers, is optional.

  We design an algorithm to get solution of a given problem. A problem can be solved in more than one ways.

  
  

  Hence, many solution algorithms can be derived for a given problem. Next step is to analyze those proposed solution algorithms and implement the best suitable.

  Algorithm Analysis

  Efficiency of an algorithm can be analyzed at two different stages, before implementation and after implementation, as mentioned below −

  • A priori analysis − This is theoretical analysis of an algorithm. Efficiency of algorithm is measured by assuming that all other factors e.g. processor speed, are constant and have no effect on implementation.
  • A posterior analysis − This is empirical analysis of an algorithm. The selected algorithm is implemented using programming language. This is then executed on target computer machine. In this analysis, actual statistics like running time and space required, are collected.

  We shall learn here a priori algorithm analysis. Algorithm analysis deals with the execution or running time of various operations involved. Running time of an operation can be defined as no. of computer instructions executed per operation.

  Algorithm Complexity

  Suppose X is an algorithm and n is the size of input data, the time and space used by the Algorithm X are the two main factors which decide the efficiency of X.

  • Time Factor − The time is measured by counting the number of key operations such as comparisons in sorting algorithm
  • Space Factor − The space is measured by counting the maximum memory space required by the algorithm.

  The complexity of an algorithm f(n) gives the running time and / or storage space required by the algorithm in terms of n as the size of input data.

  Space Complexity

  Space complexity of an algorithm represents the amount of memory space required by the algorithm in its life cycle. Space required by an algorithm is equal to the sum of the following two components −

  • A fixed part that is a space required to store certain data and variables, that are independent of the size of the problem. For example simple variables & constant used, program size etc.
  • A variable part is a space required by variables, whose size depends on the size of the problem. For example dynamic memory allocation, recursion stack space etc.

  Space complexity S(P) of any algorithm P is S(P) = C + SP(I) Where C is the fixed part and S(I) is the variable part of the algorithm which depends on instance characteristic I. Following is a simple example that tries to explain the concept −

  Algorithm: SUM(A, B)
  Step 1 -  START
  Step 2 -  C ← A + B + 10
  Step 3 -  Stop
  

  Here we have three variables A, B and C and one constant. Hence S(P)=1+3. Now space depends on data types of given variables and constant types and it will be multiplied accordingly.

  Time Complexity

  Time Complexity of an algorithm represents the amount of time required by the algorithm to run to completion. Time requirements can be defined as a numerical function T(n), where T(n) can be measured as the number of steps, provided each step consumes constant time.

  For example, addition of two n-bit integers takes n steps. Consequently, the total computational time is T(n)= c*n, where c is the time taken for addition of two bits. Here, we observe that T(n) grows linearly as input size increases.

• Data Structures - Asymptotic Analysis

• Asymptotic analysis of an algorithm, refers to defining the mathematical boundation/framing of its run-time performance. Using asymptotic analysis, we can very well conclude the best case, average case and worst case scenario of an algorithm.

  Asymptotic analysis are input bound i.e., if there's no input to the algorithm it is concluded to work in a constant time. Other than the "input" all other factors are considered constant.

  Asymptotic analysis refers to computing the running time of any operation in mathematical units of computation. For example, running time of one operation is computed as f(n) and may be for another operation it is computed as g(n²). Which means first operation running time will increase linearly with the increase in n and running time of second operation will increase exponentially when n increases. Similarly the running time of both operations will be nearly same if n is significantly small.

  Usually, time required by an algorithm falls under three types −

  • Best Case − Minimum time required for program execution.
  • Average Case − Average time required for program execution.
  • Worst Case − Maximum time required for program execution.

  Asymptotic Notations

  Following are commonly used asymptotic notations used in calculating running time complexity of an algorithm.

  • Ο Notation
  • Ω Notation
  • θ Notation

  Big Oh Notation, Ο

  The Ο(n) is the formal way to express the upper bound of an algorithm's running time. It measures the worst case time complexity or longest amount of time an algorithm can possibly take to complete. For example, for a functionf(n)

  Ο(f(n)) = { g(n) : there exists c > 0 and n0 such that g(n) ≤ c.f(n) for all n > n0. }

  Omega Notation, Ω

  The Ω(n) is the formal way to express the lower bound of an algorithm's running time. It measures the best case time complexity or best amount of time an algorithm can possibly take to complete.

  For example, for a function f(n)

  Ω(f(n)) ≥ { g(n) : there exists c > 0 and n0 such that g(n) ≤ c.f(n) for all n > n0. }

  Theta Notation, θ

  The θ(n) is the formal way to express both the lower bound and upper bound of an algorithm's running time. It is represented as following.

  θ(f(n)) = { g(n) if and only if g(n) =  Ο(f(n)) and g(n) = Ω(f(n)) for all n > n0. }

  constant	−	Ο(1)
  logarithmic	−	Ο(log n)
  linear	−	Ο(n)
  n log n	−	Ο(n log n)
  quadratic	−	Ο(n2)
  cubic	−	Ο(n3)
  polynomial	−	nΟ(1)
  exponential	−	2Ο(n)