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Working Hard to Keep it Simple Martin Odersky Typesafe
The Challenge ,[object Object],[object Object],[object Object],[object Object],Data from Kunle Olukotun, Lance Hammond, Herb Sutter, Burton Smith, Chris Batten, and Krste Asanovic
Concurrency and Parallelism ,[object Object],[object Object],[object Object]
The Root of The Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],var  x = 0 async { x = x + 1 } async { x = x * 2 } // can give 0, 1, 2
Space vs Time Time (imperative/concurrent) Space (functional/parallel)
Scala is a Unifier Agile, with lightweight syntax  Object-Oriented  Scala  Functional Safe and performant, with strong static tpying
Scala is a Unifier Agile, with lightweight syntax  Parallel Object-Oriented  Scala  Functional Sequential Safe and performant, with strong static tpying
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Scala’s Toolbox
Different Tools for Different Purposes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]
A class ... ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],class  Person( val  name: String,    val  age:  Int ) ... in Java: ... in Scala:
... and its usage ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],... in Java: ... in Scala: val  people:  Array [Person] val   (minors, adults) = people partition (_.age < 18) A simple pattern match An infix method call A function value
Going Parallel ,[object Object],... in Java: ... in Scala: val  people:  Array [Person] val   (minors, adults) = people .par  partition (_.age < 18)
Actors for Concurrent Programming ,[object Object],[object Object],[object Object],[object Object]
Going further: Parallel DSLs ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
EPFL / Stanford Research Applications Domain Specific Languages Heterogeneous Hardware DSL Infrastructure OOO Cores SIMD Cores Threaded Cores Specialized Cores Programmable Hierarchies Scalable  Coherence Isolation & Atomicity On-chip Networks Pervasive Monitoring Domain Embedding Language ( Scala ) Virtual Worlds Personal Robotics Data informatics Scientific Engineering Physics ( Liszt ) Scripting Probabilistic (RandomT) Machine Learning ( OptiML ) Rendering Parallel Runtime ( Delite, Sequoia, GRAMPS ) Dynamic Domain Spec. Opt. Locality Aware Scheduling Staging Polymorphic Embedding Task & Data Parallelism Hardware Architecture Static Domain Specific Opt.
Example: Liszt - A DSL for Physics Simulation ,[object Object],[object Object],[object Object],[object Object],[object Object],Fuel injection Transition Thermal Turbulence Turbulence Combustion
Liszt as Virtualized Scala ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],AST Hardware DSL Library Optimisers Generators … … Schedulers GPU, Multi-Core, etc
Follow us on twitter: @typesafe ,[object Object],typesafe.com

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Oscon keynote: Working hard to keep it simple

  • 1. Working Hard to Keep it Simple Martin Odersky Typesafe
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  • 5. Space vs Time Time (imperative/concurrent) Space (functional/parallel)
  • 6. Scala is a Unifier Agile, with lightweight syntax Object-Oriented Scala Functional Safe and performant, with strong static tpying
  • 7. Scala is a Unifier Agile, with lightweight syntax Parallel Object-Oriented Scala Functional Sequential Safe and performant, with strong static tpying
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  • 18. EPFL / Stanford Research Applications Domain Specific Languages Heterogeneous Hardware DSL Infrastructure OOO Cores SIMD Cores Threaded Cores Specialized Cores Programmable Hierarchies Scalable Coherence Isolation & Atomicity On-chip Networks Pervasive Monitoring Domain Embedding Language ( Scala ) Virtual Worlds Personal Robotics Data informatics Scientific Engineering Physics ( Liszt ) Scripting Probabilistic (RandomT) Machine Learning ( OptiML ) Rendering Parallel Runtime ( Delite, Sequoia, GRAMPS ) Dynamic Domain Spec. Opt. Locality Aware Scheduling Staging Polymorphic Embedding Task & Data Parallelism Hardware Architecture Static Domain Specific Opt.
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Editor's Notes

  1. This leads to our vision, applications driven by a set of interoperable DSLs. We are developing DSLs to provide evidence as to their effectiveness in extracting parallel performance. But we are also very interested in empowering other to easily build such DSLs, so we are investing heavily in developing frameworks and runtimes to make parallel DSL development easier. And the goal is to run single source programs on a variety of very different hardware targets.
  2. Liszt is another language we have implemented. It is designed to support the creation of solvers for mesh-based partial differential equations. Problems in this domain typically simulate complex physical systems such as fluid flow or mechanics by breaking up space into discrete cells. A typical mesh may contain hundreds of millions of these cells (here we are visualizing a scram-jet designed to work at hypersonic speeds). Liszt is an ideal candidate for a DSL because while the problems are large and highly parallel, the mesh introduces many data-dependencies that are difficult to reason about, making writing solvers tedious.