Visual Programming Languages ICS 519 Paradigms

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Visual Programming LanguagesICS 519Paradigms

• ICS Department
• KFUPM
• Feb. 1, 2005

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Paradigms

• Imperative Languages
• The features (Von Neumann architecture) are
• Variables
• Assignment
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
• Control Structures

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Textual Example

• main()
• int i
• for ( i0 ilt10 i)
• printf(" d\n " , i)

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Visual Example

• Pascal-BLOX Language

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• 2) Functional (Applicative) Languages
• Function
• Atom
• List
• Recursion

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Textual Example

• (DEFINE (equal list1 list2)
• (COND
• ((ATOM? list1) (EQ? list1 list2))
• ((ATOM? List2) NIL)
• ((equal (CAR list1) (CAR list2))
• (equal (CDR list1) (CDR list2)))
• (ELSE NIL)
• ))

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Visual Example

• Clarity Language

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• 3) Object Oriented
• Class
• Object
• Message passing

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Textual Example

• 5 - 3
• 2
• (3lt5) ifTrue 17
• 17
• i ? 2
• ilt9whileTruei print. i ? i 3
• 2
• 5
• 8

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Visual Example

• Prograph is an example of a visual OOLs.
• It has a visual representation for the following
  OOP concepts
• Classes
• Methods
• Parallelism
• Sequencing
• Iteration
• Conditional
• Look at the next figure.

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• Prograph Language

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• 4) Logic languages
• list
• predicate
• rule
• goal

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Example

• parent(X, Y) - mother(X, Y).
• parent(X, Y) - father(X, Y).
• grandparent(X, Z) - parent(X, Y),parent(Y, Z).
• parent(ali, ahmd).
• parent(mohmd, ali).
• grandparent(ahmd)

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Visual Example

• Logic Languages
• Variable
• Constant

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• Predicate
• q()
• q(x) q(a,b)

q
q
q
a
b
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• Rule
• person(x)-mother(x,y),father(x,y)

person
mother
father
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• program

person
person
ali
person
Mother
Father
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• 5) Dataflow Languages
• Programs are represented by a directed graph.
• Nodes
• Arcs
• Johnston, Hanna, and Millar, Advances in
  Dataflow Programming Language, ACM Computing
  Survey, Vol. 36, No. 1, March 2004, pp.1-34.

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Pure Dataflow Model

• Programs are represented by a directed graph.
• Nodes are primitive instructions such as
  arithmetic and comparison operations.
• Arcs represent the data dependencies between the
  instructions.
• Conceptually, data flows as tokens along the arcs
  which behave like unbounded first-in, first-out
  (FIFO) queues.
• Arcs that flow toward a node are said to be input
  arcs to that node, while those that flow away are
  said to be output arcs from that node.

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Pure Dataflow Model (Continue)

• When the program begins, special activation nodes
  place data onto certain key input arcs,
  triggering the rest of the program.
• Whenever a specific set of input arcs of a node
  (called a firing set) has data on it, the node is
  said to be fireable.
• A fireable node is executed at some undefined
  time after it becomes fireable. The result is
  that it removes a data token from each node in
  the firing set, performs its operation, and
  places a new data token on some or all of its
  output arcs.
• It then ceases execution and waits to become
  fireable again.

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Pure Dataflow Model (Continue)

• By this method, instructions are scheduled for
  execution as soon as their operands become
  available.
• This stands in contrast to the von Neumann
  execution model, in which an instruction is only
  executed when the program counter reaches it,
  regardless of whether or not it can be executed
  earlier than this.
• The key advantage is that, in dataflow, more than
  one instruction can be executed in parallel.

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Example
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• Under the von Neumann execution model, the
  program in Figure 1(a) would execute sequentially
  in three time units.
• Under the dataflow execution model, the program
  in Figure 1(b) would execute sequentially in two
  time units.
• It is clear that dataflow provides the potential
  to provide a substantial speed improvement by
  utilizing data dependencies to locate
  parallelism.