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Incremental Inference for Probabilistic Programs 2018

Incremental Inference for Probabilistic Programs

引用

39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Cusumano-Towner, Marco Bichsel, Benjamin Gehr, Timon Vechev, Martin Mansinghka, Vikash K. MIT 77 Massachusetts Ave Cambridge MA 02139 USA Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781450356985

We present a novel approach for approximate sampling in probabilistic programs based on incremental inference. The key idea is to adapt the samples for a program P into samples for a program Q, thereby avoiding the expensive sampling computation for program Q. To enable incremental inference in probabilistic programming, our work: (i) introduces the concept of a trace translator which adapts samples from P into samples of Q, (ii) phrases this translation approach in the context of sequential Monte Carlo (SMC), which gives theoretical guarantees that the adapted samples converge to the distribution induced by Q, and (iii) shows how to obtain a concrete trace translator by establishing a correspondence between the random choices of the two probabilistic programs. We implemented our approach in two different probabilistic programming systems and showed that, compared to methods that sample the program Q from scratch, incremental inference can lead to orders of magnitude increase in efficiency, depending on how closely related P and Q are.

关键词： Probabilistic programming sequential Monte Carlo incremental computation

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Bounded Expectations: Resource Analysis for Probabilistic Programs 2018

Bounded Expectations: Resource Analysis for Probabilistic Pr...

引用

39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Van Chan Ngo Carbonneaux, Quentin Hoffmann, Jan Carnegie Mellon Univ Pittsburgh PA 15213 USA Yale Univ New Haven CT 06520 USA

ISBN: (纸本)9781450356985

This paper presents a new static analysis for deriving upper bounds on the expected resource consumption of probabilistic programs. The analysis is fully automatic and derives symbolic bounds that are multivariate polynomials in the inputs. The new technique combines manual state-of-the-art reasoning techniques for probabilistic programs with an effective method for automatic resource-bound analysis of deterministic programs. It can be seen as both, an extension of automatic amortized resource analysis (AARA) to probabilistic programs and an automation of manual reasoning for probabilistic programs that is based on weakest preconditions. An advantage of the technique is that it combines the clarity and compositionality of a weakest-precondition calculus with the efficient automation of AARA. As a result, bound inference can be reduced to off-the-shelf LP solving in many cases and automatically-derived bounds can be interactively extended with standard program logics if the automation fails. Building on existing work, the soundness of the analysis is proved with respect to an operational semantics that is based on Markov decision processes. The effectiveness of the technique is demonstrated with a prototype implementation that is used to automatically analyze 39 challenging probabilistic programs and randomized algorithms. Experiments indicate that the derived constant factors in the bounds are very precise and even optimal for some programs.

关键词： probabilistic programming resource bound analysis static analysis

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Heartbeat Scheduling: Provable Efficiency for Nested Parallelism 2018

Heartbeat Scheduling: Provable Efficiency for Nested Paralle...

引用

39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Acar, Umut A. Chargueraud, Arthur Guatto, Adrien Rainey, Mike Sieczkowski, Filip Carnegie Mellon Univ Pittsburgh PA 15213 USA INRIA Rocquencourt France Univ Strasbourg ICube Strasbourg France CREST Bloomington IN USA

ISBN: (纸本)9781450356985

A classic problem in parallel computing is to take a high-level parallel program written, for example, in nested-parallel style with fork-join constructs and run it efficiently on a real machine. The problem could be considered solved in theory, but not in practice, because the overheads of creating and managing parallel threads can overwhelm their benefits. Developing efficient parallel codes therefore usually requires extensive tuning and optimizations to reduce parallelism just to a point where the overheads become acceptable. In this paper, we present a scheduling technique that delivers provably efficient results for arbitrary nested-parallel programs, without the tuning needed for controlling parallelism overheads. The basic idea behind our technique is to create threads only at a beat (which we refer to as the "heartbeat") and make sure to do useful work in between. We specify our heartbeat scheduler using an abstract-machine semantics and provide mechanized proofs that the scheduler guarantees low overheads for all nested parallel programs. We present a prototype C++ implementation and an evaluation that shows that Heartbeat competes well with manually optimized Cilk Plus codes, without requiring manual tuning.

关键词： parallel programming languages granularity control

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MixT: A language for Mixing Consistency in Geodistributed Transactions 2018

MixT: A Language for Mixing Consistency in Geodistributed Tr...

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39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Milano, Matthew Myers, Andrew C. Cornell Univ Dept Comp Sci Ithaca NY 14853 USA

ISBN: (纸本)9781450356985

programming concurrent, distributed systems is hard-especially when these systems mutate shared, persistent state replicated at geographic scale. To enable high availability and scalability, a new class of weakly consistent data stores has become popular. However, some data needs strong consistency. To manipulate both weakly and strongly consistent data in a single transaction, we introduce a new abstraction: mixed-consistency transactions, embodied in a new embedded language, MixT. Programmers explicitly associate consistency models with remote storage sites;each atomic, isolated transaction can access a mixture of data with different consistency models. Compile-time information-flow checking, applied to consistency models, ensures that these models are mixed safely and enables the compiler to automatically partition transactions. New run-time mechanisms ensure that consistency models can also be mixed safely, even when the data used by a transaction resides on separate, mutually unaware stores. Performance measurements show that despite their stronger guarantees, mixed-consistency transactions retain much of the speed of weak consistency, significantly outperforming traditional serializable transactions.

关键词： Consistency Transactions Information Flow

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To-Many or To-One? All-in-One! Efficient Purely Functional Multi-maps with Type-Heterogeneous Hash-Tries 2018

To-Many or To-One? All-in-One! Efficient Purely Functional M...

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39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Steindorfer, Michael J. Vinju, Jurgen J. Delft Univ Technol Delft Netherlands Ctr Wiskunde & Informat Amsterdam Netherlands TU Eindhoven Eindhoven Netherlands

ISBN: (纸本)9781450356985

An immutable multi-map is a many-to-many map data structure with expected fast insert and lookup operations. This data structure is used for applications processing graphs or many-to-many relations as applied in compilers, runtimes of programming languages, or in static analysis of object-oriented systems. Collection data structures are assumed to carefully balance execution time of operations with memory consumption characteristics and need to scale gracefully from a few elements to multiple gigabytes at least. When processing larger in-memory data sets the overhead of the data structure encoding itself becomes a memory usage bottleneck, dominating the overall performance. In this paper we propose AXIOM, a novel hash-trie data structure that allows for a highly efficient and type-safe multi-map encoding by distinguishing inlined values of singleton sets from nested sets of multi-mappings. AXIOM strictly generalizes over previous hash-trie data structures by supporting the processing of fine-grained type-heterogeneous content on the implementation level (while API and language support for type-heterogeneity are not scope of this paper). We detail the design and optimizations of AXIOM and further compare it against state-of-the-art immutable maps and multi-maps in Java, Scala and Clojure. We isolate key differences using microbenchmarks and validate the resulting conclusions on a case study in static analysis. AXIOM reduces the key-value storage overhead by 1.87 x;with specializing and inlining across collection boundaries it improves by 5.1 x.

关键词： Data structures persistent data structures functional programming hashtable multi-map many-to-many relation graph optimization performance JVM

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A General Path-Based Representation for Predicting Program Properties 2018

A General Path-Based Representation for Predicting Program P...

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39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Alon, Uri Zilberstein, Meital Levy, Omer Yahav, Eran Technion Haifa Israel Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781450356985

Predicting program properties such as names or expression types has a wide range of applications. It can ease the task of programming, and increase programmer productivity. A major challenge when learning from programs is how to represent programs in a way that facilitates effective learning. We present a general path-based representation for learning from programs. Our representation is purely syntactic and extracted automatically. The main idea is to represent a program using paths in its abstract syntax tree (AST). This allows a learning model to leverage the structured nature of code rather than treating it as a flat sequence of tokens. We showthat this representation is general and can: (i) cover different prediction tasks, (ii) drive different learning algorithms (for both generative and discriminative models), and (iii) work across different programming languages. We evaluate our approach on the tasks of predicting variable names, method names, and full types. We use our representation to drive both CRF-based and word2vec-based learning, for programs of four languages: JavaScript, Java, Python and C#. Our evaluation shows that our approach obtains better results than task-specific handcrafted representations across different tasks and programming languages.

关键词： programming languages Big Code Machine Learning Learning Representations

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Persistency for Synchronization-Free Regions 2018

Persistency for Synchronization-Free Regions

引用

39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Gogte, Vaibhav Diestelhorst, Stephan Wang, William Narayanasamy, Satish Chen, Peter M. Wenisch, Thomas F. Univ Michigan Ann Arbor MI 48109 USA Arm Res Cambridge England

ISBN: (纸本)9781450356985

Nascent persistent memory (PM) technologies promise the performance of DRAM with the durability of disk, but how best to integrate them into programming systems remains an open question. Recent work extends language memory models with a persistency model prescribing semantics for updates to PM. These semantics enable programmers to design data structures in PM that are accessed like memory and yet are recoverable upon crash or failure. Alas, we find the semantics and performance of existing approaches unsatisfying. Existing approaches require high-overhead mechanisms, are restricted to certain synchronization constructs, provide incomplete semantics, and/or may recover to state that cannot arise in fault-free execution. We propose persistency semantics that guarantee failure atomicity of synchronization-free regions (SFRs) -program regions delimited by synchronization operations. Our approach provides clear semantics for the PM state recovery code may observe and extends C++11's "sequential consistency for data-race-free" guarantee to post-failure recovery code. We investigate two designs for failure-atomic SFRs that vary in performance and the degree to which commit of persistent state may lag execution. We demonstrate both approaches in LLVM v3.6.0 and compare to a state-of-the-art baseline to show performance improvement up to 87.5% (65.5% avg).

关键词： Persistent memories persistency models language memory models failure-atomicity synchronization-free regions

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Symbolic Reasoning for Automatic Signal Placement 2018

Symbolic Reasoning for Automatic Signal Placement

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39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Ferles, Kostas Van Geffen, Jacob Dillig, Isil Smaragdakis, Yannis Univ Texas Austin Austin TX 78712 USA Univ Athens Athens Greece

ISBN: (纸本)9781450356985

Explicit signaling between threads is a perennial cause of bugs in concurrent programs. While there are several runtime techniques to automatically notify threads upon the availability of some shared resource, such techniques are not widely-adopted due to their run-time overhead. This paper proposes a new solution based on static analysis for automatically generating a performant explicit-signal program from its corresponding implicit-signal implementation. The key idea is to generate verification conditions that allow us to minimize the number of required signals and unnecessary context switches, while guaranteeing semantic equivalence between the source and target programs. We have implemented our method in a tool called Expresso and evaluate it on challenging benchmarks from prior papers and open-source software. Expresso-generated code significantly outperforms past automatic signaling mechanisms (avg. 1.56x speedup) and closely matches the performance of hand-optimized explicit-signal code.

关键词： Implicit signal monitors concurrent programming symbolic reasoning verification conditions abductive reasoning monitor invariant

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Polyhedral Auto-transformation with No Integer Linear programming 2018

Polyhedral Auto-transformation with No Integer Linear Progra...

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39th acm sigplan conference on programming language design and implementation (PLDI)

作者： Acharya, Aravind Bondhugula, Uday Cohen, Albert Indian Inst Sci Dept Comp Sci & Automat Bangalore Karnataka India INRIA Paris France DI Ecole Normale Super Paris France

ISBN: (纸本)9781450356985

State-of-the-art algorithms used in automatic polyhedral transformation for parallelization and locality optimization typically rely on Integer Linear programming (ILP). This poses a scalability issue when scaling to tens or hundreds of statements, and may be disconcerting in production compiler settings. In this work, we consider relaxing integrality in the ILP formulation of the Pluto algorithm, a popular algorithm used to find good affine transformations. We show that the rational solutions obtained from the relaxed LP formulation can easily be scaled to valid integral ones to obtain desired solutions, although with some caveats. We first present formal results connecting the solution of the relaxed LP to the original Pluto ILP. We then show that there are difficulties in realizing the above theoretical results in practice, and propose an alternate approach to overcome those while still leveraging linear programming. Our new approach obtains dramatic compile-time speedups for a range of large benchmarks. While achieving these compile-time improvements, we show that the performance of the transformed code is not sacrificed. Our approach to automatic transformation provides a mean compilation time improvement of 5.6x over state-of-the-art on relevant challenging benchmarks from the NAS PB, SPEC CPU 2006, and PolyBench suites. We also came across situations where prior frameworks failed to find a transformation in a reasonable amount of time, while our new approach did so instantaneously.

关键词： Polyhedral transformation Pluto algorithm

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A Domain-Specific language for Exploratory Data Visualization 2018

A Domain-Specific Language for Exploratory Data Visualizatio...

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17th acm sigplan International conference on Generative programming - Concepts and Experiences (GPCE) part of the SPLASH Collection of conferences and Workshops

作者： Smeltzer, Karl Erwig, Martin Oregon State Univ Corvallis OR 97331 USA

ISBN: (纸本)9781450360456

With an ever-growing amount of collected data, the importance of visualization as an analysis component is growing in concert. The creation of good visualizations often doesn't happen in one step but is rather an iterative and exploratory process. However, this process is currently not well supported in most of the available visualization tools and systems. Visualization authors are forced to commit prematurely to particular design aspects of their creations, and when exploring potential variant visualizations, they are forced to adopt ad hoc techniques such as copying code snippets or keeping a collection of separate files. We propose variational visualizations as a model supporting open-ended exploration of the design space of information visualization. Together with that model, we present a prototype implementation in the form of a domain-specific language embedded in Purescript.

关键词： Data Visualization Exploratory programming

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