Staging for generic programming in space and time

Ofenbeck, Georg; Rompf, Tiark; Püschel, Markus

doi:10.1145/3136040.3136060

Cited by 11 publications

(11 citation statements)

References 21 publications

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“…Binding-time Abstraction. The binding-time type is declared as a higher-kinded type R[ _ ] [Ofenbeck et al 2017]. Now we can use R to annotate other data types in the interpreter.…”

Section: Generic Interpretermentioning

confidence: 99%

“…Monadic interpreters are known to be able to decouple the interpretation procedure and the underlying semantics. The key insight in this paper is that by making the monadic interpreter stage polymorphic [Amin and Rompf 2018;Ofenbeck et al 2017], the abstract interpreter can be extended to generate efficient code. The underlying semantics and staging are two orthogonal dimensions.…”

Section: Summarizing the Approachmentioning

confidence: 99%

“…In short, we borrow ideas and techniques from multi-stage programming and embedded DSLs, and apply them to abstract interpreters: 1) To remove the overhead from interpretation and effect layers, we specialize the abstract interpreter via staging, and then generate efficient low-level code that performs the actual analysis. 2) Inspired by tagless-final interpreters [Carette et al 2009], we construct a generic interpreter that abstracts over binding-times [Amin and Rompf 2018;Ofenbeck et al 2017] and different semantics, which allows the staged abstract interpreter to be derived from its unstaged counterpart. As a result, the derived staged abstract interpreter has no intrusive changes to the underlying abstract semantics, thereby preserving soundness.…”

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confidence: 99%

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Staged abstract interpreters: fast and modular whole-program analysis via meta-programming

Wei

Chen

Rompf

2019

Proc. ACM Program. Lang.

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It is well known that a staged interpreter is a compiler: specializing an interpreter to a given program produces an equivalent executable that runs faster. This connection is known as the first Futamura projection. It is even more widely known that an abstract interpreter is a program analyzer: tweaking an interpreter to run on abstract domains produces a sound static analysis. What happens when we combine these two ideas, and apply specialization to an abstract interpreter? In this paper, we present a unifying framework that naturally extends the first Futamura projection from concrete interpreters to abstract interpreters. Our approach derives a sound staged abstract interpreter based on a generic interpreter with type-level binding-time abstractions and monadic abstractions. By using different instantiations of these abstractions, the generic interpreter can flexibly behave in one of four modes: as an unstaged concrete interpreter, a staged concrete interpreter, an unstaged abstract interpreter, or a staged abstract interpreter. As an example of abstraction without regret, the overhead of these abstraction layers is eliminated in the generated code after staging. We show that staging abstract interpreters is practical and useful to optimize static analysis, all while requiring less engineering effort and without compromising soundness. We conduct three case studies, including a comparison with Boucher and Feeley's abstract compilation, applications to various control-flow analyses, and a demonstration for modular analysis. We also empirically evaluate the effect of staging on the execution time. The experiment shows an order of magnitude speedup with staging for control-flow analyses. CCS Concepts: • Software and its engineering → Automated static analysis; Functional languages; Interpreters.

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“…Binding-time Abstraction. The binding-time type is declared as a higher-kinded type R[ _ ] [Ofenbeck et al 2017]. Now we can use R to annotate other data types in the interpreter.…”

Section: Generic Interpretermentioning

confidence: 99%

Section: Summarizing the Approachmentioning

confidence: 99%

mentioning

confidence: 99%

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Staged abstract interpreters: fast and modular whole-program analysis via meta-programming

Wei

Chen

Rompf

2019

Proc. ACM Program. Lang.

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“…Furthermore, polyvariance can only be achieved by introducing type variables for each desired staging combination [Ofenbeck et al 2017…”

Section: Dsl Embeddingmentioning

confidence: 99%

AnyDSL: a partial evaluation framework for programming high-performance libraries

Leißa

Boesche

Hack

et al. 2018

Proc. ACM Program. Lang.

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This paper advocates programming high-performance code using partial evaluation. We present a clean-slate programming system with a simple, annotation-based, online partial evaluator that operates on a CPS-style intermediate representation. Our system exposes code generation for accelerators (vectorization/parallelization for CPUs and GPUs) via compiler-known higher-order functions that can be subjected to partial evaluation. This way, generic implementations can be instantiated with target-specific code at compile time. In our experimental evaluation we present three extensive case studies from image processing, ray tracing, and genome sequence alignment. We demonstrate that using partial evaluation, we obtain high-performance implementations for CPUs and GPUs from one language and one code base in a generic way. The performance of our codes is mostly within 10%, often closer to the performance of multi man-year, industry-grade, manuallyoptimized expert codes that are considered to be among the top contenders in their fields. CCS Concepts: • Software and its engineering → Compilers; Parallel programming languages; • Hardware → Emerging languages and compilers; • Computing methodologies → Image processing; Ray tracing; • Applied computing → Bioinformatics;

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“…Note how the order of operations is important. Metaprogramming is often used to programmatically generate faster specialised code when some parameters are known in advance, this is particularly useful where the specialisation mechanism is too complicated for a generic compiler to automatically derive [6]. A metaprogram is a program, method or function that produces code.…”

Section: Introductionmentioning

confidence: 99%

Iteratively Composing Statically Verified Traits

Gariano¹,

Servetto²,

Potanin³

et al. 2019

Electron. Proc. Theor. Comput. Sci.

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Static verification relying on an automated theorem prover can be very slow and brittle: since static verification is undecidable, correct code may not pass a particular static verifier. In this work we use metaprogramming to generate code that is correct by construction. A theorem prover is used only to verify initial "traits": units of code that can be used to compose bigger programs.In our work, meta-programming is done by trait composition, which starting from correct code, is guaranteed to produce correct code. We do this by extending conventional traits with pre-and postconditions for the methods; we also extend the traditional trait composition (+) operator to check the compatibility of contracts. In this way, there is no need to re-verify the produced code.We show how our approach can be applied to the standard "power" function example, where metaprogramming generates optimised, and correct, versions when the exponent is known in advance.

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Staging for generic programming in space and time

Cited by 11 publications

References 21 publications

Staged abstract interpreters: fast and modular whole-program analysis via meta-programming

Staged abstract interpreters: fast and modular whole-program analysis via meta-programming

AnyDSL: a partial evaluation framework for programming high-performance libraries

Iteratively Composing Statically Verified Traits

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