Computer's future is declarative

1. COMPUTER’S
FUTURE IS
DECLARATIVE
by Jose Carpio
University of Huelva, Spain

2. INFORMATION TECHNOLOGY
NEW CHALLENGES
• Analize Big Data
• Improve computer’s performace
• More Data Scientistic needed

3. HOW BIG IS DATA?
https://www.sisense.com/blog/infographic-big-big-data/

4. HOW TO SEARCH IN BIG DATA?
• Organize information
• Distribute search
• Present results

5. HADOOP & SPARK
• Distributed processing of large data sets
• Scale up from single servers to thousands of machines
• Spark – Similar to Hadoop x100 times faster
https://hadoop.apache.org/ https://spark.apache.org/

6. DECLARATIVE VS IMPERATIVE
PROGRAMMING
Imperative
• Focus on “How to do things”
• Describe Step by Step what to do
• Positive: Usualy faster
• Negative: More knowledge needed

7. DECLARATIVE VS IMPERATIVE
PROGRAMMING
Declarative
• Focus on “What do you want”
• Declarative Systems provides extra help to
achieve your goals
• Positive: Good for complex and distributed systems
• Negative: Usually slower

8. WHY DECLARATIVE PROGRAMMING?
• Focusing on describe what do you want instead of describe
step by step how to resolv the problema
• Suitable for complex search and systems
• Higher level programming language
• Suitable for Artificial Intelligence (Prolog)

9. PROLOG: LOGIC PROGRAMMING
Based on Logic
Rules
if “there are good waves” then “let’s go to surf!”
Facts
“Today we have good waves”.

10. WHY IS DIFFICULT TO LEARN
LOGIC PROGRAMMING?
• Difficult to learn new paradigms.
• Programmer do not resolv the problem,
only describe it.
• Difficult to visualize program flow.

11. LEARN NEW PARADIGM
It is like LEARN TO RIDE A BIKE ... at 25
• https://www.youtube.com/watch?v=4rbHANdJcSk

12. HOW LOGIC PROGRAMMING
WORKS?
•Mathematic induction
•Resolution algorithm
https://en.wikipedia.org/wiki/Mathematical_induction

13. HOW INDUCTION WORKS?
•Ask to your ancestor
• 1. The concept is true for first element
• 2. If my ancestor is true I am true
“If my parents are humans” then “I am a human”

14. EXAMPLE
Think about natural numbers
1, 2 ,3 ,4 ,5, ….
Let’s go to define the concept of natural number
for any posible number!

15. EXAMPLE
Number one is a natural number.
If n-1 is natural, then n is natural .

16. EXAMPLE: PROLOG VERSION
natural(1).
natural(N2):- N2 is N-1, natural(N).

17. PROBLEMS WITH PROLOG
•Error: Out of local stack
•Not too fast
•Difficult to learn

18. VON NEUMAN ARCHITECTURE
Not suitable for induction
https://es.wikipedia.org/wiki/Arquitectura_de_Von_Neumann
https://www.hackster.io/savva-osipov/arduino-powered-telegraph-bb8777

19. POSIBLE SOLUTIONS
• New computer architecture
• Millions of micro compute units interconected
• More similar to Graphic Cards CUDA architecture

20. STRUCTURE WITH COMPUTE CAPABILITIES
• v
https://itnext.io/recursion-for-nested-tree-structure-components-in-vue-1cb600005475
https://www.youtube.com/watch?v=1VDw3G8TA68

21. THANKS!

22. REFERENCES
• Nvidia super computer
https://www.datacenterknowledge.com/supercomputers/ibm-nvidia-build-world-s-
fastest-supercomputer-us-government
• Swi Prolog
http://www.swi-prolog.org/
• How big is data
https://www.sisense.com/blog/infographic-big-big-data/
• Mathematical induction
https://en.wikipedia.org/wiki/Mathematical_induction