Synthesizing iterators from abstraction functions

RaysideDerek,; MontaghamiVajihollah,; LeungFrancesca,; YuenAlbert,; XuKevin,; JacksonDaniel,

doi:10.1145/2480361.2371407

Cited by 5 publications

(4 citation statements)

References 42 publications

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“…None of these techniques consider block data structures like C-trees, and they do not generate parallel code. In addition, Rayside et al [41] show how Java iterators can be synthesized for pointer-based data structures given specifications written in relational logic, though their technique does not generate map functions or any other code to actually compute with elements stored in these data structures.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Sparse Tensor Algebra Compilation

Chou¹,

Amarasinghe²

2021

Preprint

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This paper shows how to generate efficient tensor algebra code that compute on dynamic sparse tensors, which have sparsity structures that evolve over time. We propose a language for precisely specifying recursive, pointer-based data structures, and we show how this language can express a wide range of dynamic data structures that support efficient modification, such as linked lists, binary search trees, and B-trees. We then describe how, given high-level specifications of such data structures, a compiler can generate code to efficiently iterate over and compute with dynamic sparse tensors that are stored in the aforementioned data structures. Furthermore, we define an abstract interface that captures how nonzeros can be inserted into dynamic data structures, and we show how this abstraction guides a compiler to emit efficient code that store the results of sparse tensor algebra computations in dynamic data structures.We evaluate our technique and find that it generates efficient dynamic sparse tensor algebra kernels. Code that our technique emits to compute the main kernel of the PageRank algorithm is 1.05× as fast as Aspen, a state-of-the-art dynamic graph processing framework. Furthermore, our technique outperforms PAM, a parallel ordered (key-value) maps library, by 7.40× when used to implement element-wise addition of a dynamic sparse matrix to a static sparse matrix.

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Section: Related Workmentioning

confidence: 99%

Dynamic Sparse Tensor Algebra Compilation

Chou¹,

Amarasinghe²

2021

Preprint

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“…An OR Proc is solved by iteratively solving its disjuncts (lines [29][30][31]. An instance is returned as soon as one is found; otherwise, None is returned.…”

Section: Satisfiability Solvingmentioning

confidence: 99%

“…Rayside et al used the Alloy Analyzer to synthesize iterators from abstraction functions [29], as well as complex (non-pure) AVL tree operations from abstract specifications [20]. In both cases, they target a very specific categories of programs, and their approach is based on insights that hold only for those particular categories.…”

Section: Related Workmentioning

confidence: 99%

Alloy*: A General-Purpose Higher-Order Relational Constraint Solver

Milićević

Near

Kang

et al. 2015

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering

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The last decade has seen a dramatic growth in the use of constraint solvers as a computational mechanism, not only for analysis and synthesis of software, but also at runtime. Solvers are available for a variety of logics but are generally restricted to first-order formulas. Some tasks, however, most notably those involving synthesis, are inherently higher order; these are typically handled by embedding a first-order solver (such as a SAT or SMT solver) in a domain-specific algorithm. Using strategies similar to those used in such algorithms, we show how to extend a first-order solver (in this case Kodkod, a model finder for relational logic used as the engine of the Alloy Analyzer) so that it can handle quantifications over higher-order structures. The resulting solver is sufficiently general that it can be applied to a range of problems; it is higher order, so that it can be applied directly, without embedding in another algorithm; and it performs well enough to be competitive with specialized tools on standard benchmarks. Although the approach is demonstrated for a particular relational logic, the principles behind it could be applied to other first-order solvers. Just as the identification of first-order solvers as reusable backends advanced the performance of specialized tools and simplified their architecture, factoring out higher-order solvers may bring similar benefits to a new class of tools.

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“…Alloy is declarative, and as such, it is not executable per se; it is instead used primarily for modeling and checking logical properties of various kinds of systems. Most of Alloy's expressive power comes from its relational base (including all the supported relational operators), which, however, can be efficiently executable in the context of object-oriented programs [57]. For example, the dot operator ('.')…”

Section: Domain-specific Programming Languagementioning

confidence: 99%

Model-based, event-driven programming paradigm for interactive web applications

Milićević

Jackson

Gligoric

et al. 2013

Proceedings of the 2013 ACM International Symposium on New Ideas, New Paradigms, and Reflections on Programming &Amp; Software

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Model-based, event-driven programming paradigm for interactive web applications The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Aleksandar Milicevic, Daniel Jackson, Milos Gligoric, and Darko Marinov. 2013. Model-based, event-driven programming paradigm for interactive web applications. In Proceedings of the 2013 ACM international symposium on New ideas, new paradigms, and reflections on programming & software (Onward! '13). ACM,

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Synthesizing iterators from abstraction functions

Cited by 5 publications

References 42 publications

Dynamic Sparse Tensor Algebra Compilation

Dynamic Sparse Tensor Algebra Compilation

Alloy*: A General-Purpose Higher-Order Relational Constraint Solver

Model-based, event-driven programming paradigm for interactive web applications

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