13. Space vs Time Time (imperative/concurrent) Space (functional/parallel)
14. Scala is a Unifier Agile, with lightweight syntax Object-Oriented Scala Functional Safe and performant, with strong static tpying
15. Scala is a Unifier Agile, with lightweight syntax Parallel Object-Oriented Scala Functional Sequential Safe and performant, with strong static tpying
27. 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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31. (Bare-Bones) Reflection in Java Want to know whether type A conforms to B? Write your own Java compiler! Why not add some meaningful operations? Need to write essential parts of a compiler (hard). Need to ensure that both compilers agree (almost impossible).
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33. Using Guice for Dependency Injection (Example by Jan Kriesten)
35. Dependency Injection in Scala Components are classes or traits Requirements are abstract values Wiring by implementing requirement values But what about cyclic dependencies?
36. The Cake Pattern Requirements are types of this Components are traits Wiring by mixin composition
41. How to Make a Facade The Facade The Implementation Interfaces are not enough!
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Editor's Notes
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.
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.
