Specialized systems are  Inevitable  Already the norm  Practical.

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Presentation transcript:

Specialized systems are  Inevitable  Already the norm  Practical

Heterogeneous parallelism

4 Virtual Worlds Personal Robotics Data informatics Scientific Engineering Applications Heterogeneous Hardware New Arch. Programming Heterogeneous Parallelism

MPI Hadoop Verilog VHDL CUDA OpenCL Threads OpenM P Architecture Specific Programming Models

High Performance But Low Productivity

Virtual Worlds Personal Robotics Data informatics Scientific Engineering Applications Heterogeneous Hardware New Arch. Scal a Jav a Python Ruby C++ Clojure Not enough semantic knowledge to compile automatically No restrictions General-Purpose Languages

Make Specialized PL Practical  Inevitable  Already the norm  Practical

Domain Specific Languages Domain Specific Languages (DSLs) Programming language with restricted expressiveness for a particular domain High-level, usually declarative, and deterministic Most popular parallel PL Most Science apps start with this PL Enabled GPU innovation

Virtual Worlds Personal Robotics Data informatics Scientific Engineering Physics (Liszt) Data Analytics (OptiQL) Graph Alg. (Green Marl) Machine Learning (OptiML) Statistics (R) Applications Domain - Specific Languages Heterogeneous Hardware DSL Compiler DSL Compiler DSL Compiler DSL Compiler DSL Compiler New Arch. High Performance Domain- Specific Languages

HP DSLs Will Be Practical Embedded DSLs Flexible embedding languages: Scala, F# Metaprogramming (aka staging) Blurring line between libraries and languages DSL Frameworks Support domain specific optimizations Support parallelism and locality optimizations Multiple backends e.g. Spoofax, Rose, Delite, Terra/Lua