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Specialization is for d odos. Keshav Pingali The University of Texas at Austin. Lesson from natural world. Brown bears Adapted to live in temperate forests Polar bears A dapted to live above Arctic Circle Polar bears evolved from brown bears Specialization

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keshav pingali the university of texas at austin

Specialization

is for

dodos

KeshavPingali

The University of Texas at Austin

lesson from natural world
Lesson from natural world
  • Brown bears
    • Adapted to live in temperate forests
  • Polar bears
    • Adapted to live above Arctic Circle
  • Polar bears evolved from brown bears
  • Specialization
    • Driven by struggle for survival

Specialization

lesson from salt lake city
Lesson from Salt Lake City
  • 1830-1978:
    • Mormons were mostly white
    • Very specialized
  • 1978:
    • Church elders have divine revelation
  • Today:
    • Mormons are very diverse
  • Generalization:
    • Driven by struggle for survival

Generalization

main take away
Main take-away
  • Struggle for survival can lead to both specialization and generalization
  • Specialization
    • Useful when there are mutually exclusive alternatives
  • Generalization
    • Useful if you can adapt to diverse environments
    • (eg) human beings of all races now live all over the world
  • What are the lessons for programming languages?
specialized languages dsls
Specialized languages = DSLs
  • DSLs usually evolve into general-purpose languages
    • To deal with diverse data types
    • To grow market share
  • MATLAB
    • Began with dense matrices and vectors to use EISPACK and LINPACK w/o writing FORTRAN code
    • Today: sparse matrices, objects, ADT’s, etc.
    • APL: dodo of array languages
  • SQL
    • Focus on structured data aka relations
    • Today: evolving to deal with unstructured data
  • Erlang
    • Began as a way to write telephony apps
    • Today: has evolved to a general functional language
two claims for dsls
Two claims for DSLs
  • Claim I: productivity
    • Programmers can code with domain abstractions
    • (eg) relations, groups, fields, etc.
  • Claim II: performance
    • Compilers for DSLs can use domain-specific information to optimize programs
    • (eg) optimization in SQL
  • Counter-point:
    • Can be implemented easily in general-purpose languages

ADTs + semantic information about methods

anecdotal evidence
Anecdotal Evidence
  • Graph analytics
  • Many DSLs:
    • GraphLab, PowerGraph, Ligra, SociaLite..
  • Galois:
    • General-purpose programming model implemented in sequential C++
  • SOSP 2014: Nguyen et al
    • Galois programs run 10-10,000 times faster than GraphLab programs
    • Shim for GraphLab on top of Galois in about 200 lines of code
slide8

Generalization

Specialization