This paper describes Scala-Virtualized, which extends the Scala language and compiler with a small number of features that enable combining the benefits of shallow and deep embeddings of DSLs. We demonstrate our approach by showing how to embed three different domain-specific languages in Scala

Abstract Scala-Virtualized extends the Scala language to better support hosting em-bedded DSLs. Scala is an expressive language that provides a flexible syntax, type-level computation using implicits, and other features that facilitate the development of em-bedded DSLs. However, many

Complex networks describe a wide range of systems in nature and society, much quoted examples including the cell, a network of chemicals linked by chemical reactions, or the Internet, a network of routers and computers connected by physical links. While traditionally these systems were modeled as random graphs, it is increasingly recognized that the topology and evolution of real

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Scala is a powerful language that supports a variety of fea-tures, but it lacks virtual traits. Virtual traits are class-valued object attributes and can be redefined within sub-traits. They support higher-order hierarchies and family polymorphism. This work introduces virtual traits into Scala

In recent years, the Java Virtual Machine has become an at-tractive target for a multitude of programming languages, one of which is Scala. But while the Scala compiler emits plain Java bytecode, the performance characteristics of Scala pro-grams are not necessarily similar to those of Java

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for sums of products. In polytypic staging context we introduce an isomorphism lifting, – a transformation that automatically lifts isomorphisms out of the domain code and separates the domain semantics from the user-defined views. The implementation is based on the Scala-Virtualized compiler (an

In recent years, the Java Virtual Machine has become an attractive target for a multitude of programming languages, one of which is Scala. But while the Scala compiler emits plain Java bytecode, the performance characteristics of Scala programs are not necessarily similar to those of Java programs

We present Resilient Distributed Datasets (RDDs), a distributed memory abstraction that lets programmers perform in-memory computations on large clusters in a fault-tolerant manner. RDDs are motivated by two types of applications that current computing frameworks handle inefficiently: iterative algorithms and interactive data mining tools. In both cases, keeping data in memory can improve performance by an order of magnitude. To achieve fault tolerance efficiently, RDDs provide a restricted form of shared memory, based on coarsegrained transformations rather than fine-grained updates to shared state. However, we show that RDDs are expressive enough to capture a wide class of computations, including recent specialized programming models for iterative jobs, such as Pregel, and new applications that these models do not capture. We have implemented RDDs in a system called Spark, which we evaluate through a variety of user applications and benchmarks. 1