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Keshav Pingali The University of Texas at Austin

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

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  1. Specialization is for dodos KeshavPingali The University of Texas at Austin

  2. 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

  3. 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

  4. 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?

  5. 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

  6. 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

  7. 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

  8. Generalization Specialization

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