Unifying analytic and statically-typed quasiquotes

Parreaux, Lionel; Voizard, Antoine; Shaikhha, Amir; Koch, Christoph

doi:10.1145/3158101

Cited by 24 publications

(11 citation statements)

References 67 publications

(73 reference statements)

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“…Quotation-Based DSLs. Quotation-based DSLs [51], such as LINQ [50], LMS [59], and Squid [55], reuse the syntax and type system of the host language and gives access to the program's AST during compilation and runtime. In contrast to type-based and shallowly-embedded DSLs, quotation gives access to the entire AST of the GPL, including types, white-box UDFs, and control flow.…”

Section: Introductionmentioning

confidence: 99%

An intermediate representation for optimizing machine learning pipelines

et al. 2019

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Machine learning (ML) pipelines for model training and validation typically include preprocessing, such as data cleaning and feature engineering, prior to training an ML model. Preprocessing combines relational algebra and user-defined functions (UDFs), while model training uses iterations and linear algebra. Current systems are tailored to either of the two. As a consequence, preprocessing and ML steps are optimized in isolation. To enable holistic optimization of ML training pipelines, we present Lara, a declarative domainspecific language for collections and matrices. Lara's intermediate representation (IR) reflects on the complete program, i.e., UDFs, control flow, and both data types. Two views on the IR enable diverse optimizations. Monads enable operator pushdown and fusion across type and loop boundaries. Combinators provide the semantics of domainspecific operators and optimize data access and cross-validation of ML algorithms. Our experiments on preprocessing pipelines and selected ML algorithms show the effects of our proposed optimizations on dense and sparse data, which achieve speedups of up to an order of magnitude.

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Section: Introductionmentioning

confidence: 99%

An intermediate representation for optimizing machine learning pipelines

et al. 2019

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“…The underlying problem that our effect system solves is a general one: we need to restrict the lifetime of a resource (capabilities in our case) to a certain dynamic region (the call to handle in our case). This problem occurs in the domain of regionbased resource management (Kiselyov & Shan, 2008), object capabilities (Haller & Loiko, 2016), delimited control (Dybvig et al, 2007), scope safety in type-safe metaprogramming (Parreaux et al, 2017), as well as with prompt-based implementations of effect handlers (Brachthäuser & Schuster, 2017). Our effect system is inspired by the λ calculus (Zhang & Myers, 2019), which uses dependent types to track the set of used labels (i.e., capabilities) in the effect type.…”

Section: Establishing Effect Safetymentioning

confidence: 99%

“…We achieve effect safety by generalizing techniques of Launchbury & Sabry (1997) to nested regions (Kiselyov & Shan, 2008) and using intersection types of abstract type members (Parreaux et al, 2017) instead of rank-2 types. Our effect system rules out some use cases of multi-prompt delimited control (Kobori et al, 2016), but can express many interesting use cases of effect handlers.…”

Section: List Of Contributions and Paper Overviewmentioning

confidence: 99%

Effekt: Capability-passing style for type- and effect-safe, extensible effect handlers in Scala

Brachthäuser¹,

Schuster²,

Ostermann³

2020

J. Funct. Prog.

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Effect handlers are a promising way to structure effectful programs in a modular way. We present the Scala library Effekt, which is centered around capability passing and implemented in terms of a monad for multi-prompt delimited continuations. Effekt is the first library implementation of effect handlers that supports effect safety and effect polymorphism without resorting to type-level programming. We describe a novel way of achieving effect safety using intersection types and path-dependent types. The effect system of our library design fits well into the programming paradigm of capability passing and is inspired by the effect system of Zhang & Myers (2019, Proc. ACM Program. Lang.3(POPL), 5:1-5:29). Capabilities carry an abstract type member, which represents an individual effect type and reflects the use of the capability on the type level. We represent effect rows as the contravariant intersection of effect types. Handlers introduce capabilities and remove components of the intersection type. Reusing the existing type system of Scala, we get effect subtyping and effect polymorphism for free.

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“…In the area of DSL development, future research aims to provide a more stable foundation and better tooling for rapid development of optimizing DSLs embedded in Scala. An important milestone in this direction is the Squid metaprogramming framework by Parreaux et al (2018). Squid combines the flexibility of dynamic quasiquotes (in the style offered by Lisp) with the typing and scoping guarantees of static quasiquotes (in the style offered by MetaML).…”

Section: Future Directions For Researchmentioning

confidence: 99%