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Deployable Probabilistic programming

acm sigplan International Symposium on New Ideas, New Paradigms, and Reflections on programming and Software (Onward) part of SPLASH conference

作者： Tolpin, David PUB Tel Aviv Israel

ISBN: (纸本)9781450369954

We propose design guidelines for a probabilistic programming facility suitable for deployment as a part of a production software system. As a reference implementation, we introduce Infergo, a probabilistic programming facility for Go, a modern programming language of choice for server-side software development. We argue that a similar probabilistic programming facility can be added to most modern general-purpose programming languages. Probabilistic programming enables automatic tuning of program parameters and algorithmic decision making through probabilistic inference based on the data. To facilitate addition of probabilistic programming capabilities to other programming languages, we share implementation choices and techniques employed in development of Infergo. We illustrate applicability of Infergo to various use cases on case studies, and evaluate Infergo's performance on several benchmarks, comparing Infergo to dedicated inference-centric probabilistic programming frameworks.

关键词： probabilistic programming algorithmic differentiation bayesian modeling

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Descend: A Safe GPU Systems programming language

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PROCEEDINGS OF THE acm ON programming languageS-PacmPL 2024年第PLDI期8卷 841-864页

作者： Koepcke, Bastian Gorlatch, Sergei Steuwer, Michel Univ Munster Munster Germany Tech Univ Berlin Berlin Germany

Graphics Processing Units (GPU) offer tremendous computational power by following a throughput oriented paradigm where many thousand computational units operate in parallel. programming such massively parallel hardware is challenging. Programmers must correctly and efficiently coordinate thousands of threads and their accesses to various shared memory spaces. Existing mainstream GPU programming languages, such as CUDA and OpenCL, are based on C/C++ inheriting their fundamentally unsafe ways to access memory via raw pointers. This facilitates easy to make, but hard to detect bugs, such as data races and deadlocks. In this paper, we present Descend: a safe GPU programming language. In contrast to prior safe high-level GPU programming approaches, Descend is an imperative GPU systems programming language in the spirit of Rust, enforcing safe CPU and GPU memory management in the type system by tracking Ownership and Lifetimes. Descend introduces a new holistic GPU programming model where computations are hierarchically scheduled over the GPU's execution resources: grid, blocks, warps, and threads. Descend's extended Borrow checking ensures that execution resources safely access memory regions without data races. For this, we introduced views describing safe parallel access patterns of memory regions, as well as atomic variables. For memory accesses that can't be checked by our type system, users can annotate limited code sections as unsafe. We discuss the memory safety guarantees offered by Descend and evaluate our implementation using multiple benchmarks, demonstrating that Descend is capable of expressing real-world GPU programs showing competitive performance compared to manually written CUDA programs lacking Descend's safety guarantees.

关键词： GPU programming language design memory safety type systems

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Concurrent Orchestration in Haskell

Concurrent Orchestration in Haskell

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3rd acm Haskell Symposium on Haskell

作者： Launchbury, John Elliott, Trevor Galois Inc. Portland OR United States

ISBN: (纸本)9781450302524

We present a concurrent scripting language embedded in Haskell, emulating the functionality of the Orc orchestration language by providing many-valued (real) non-determinism in the context of concurrent effects. We provide many examples of its use, as well as a brief description of how we use the embedded Ore DSL in practice. We describe the abstraction layers of the implementation, and use the fact that we have a layered approach to demonstrate algebraic properties satisfied by the combinators.

关键词： design languages Concurrency Haskell Orc DSL Monad

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Type-based race detection for Java

Type-based race detection for Java

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acm sigplan' 00 conference on programming language design and implementation (PLDI)

作者： Flanagan, C Freund, SN Compaq Syst Res Ctr Palo Alto CA 94301 USA Stanford Univ Dept Comp Sci Stanford CA 94305 USA

This paper presents a static race detection analysis for multithreaded Java programs. Our analysis is based on a formal type system that is capable of capturing many common synchronization patterns. These patterns include classes with internal synchronization, classes that require client-side synchronization, and thread-local classes. Experience checking over 40,000 lines of Java code with the type system demonstrates that it is an effective approach for eliminating races conditions. On large examples, fewer than 20 additional type annotations per 1000 lines of code were required by the type checker, and pie found a number of races in the standard Java libraries and other test programs.

关键词： Java programming language

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Using Camlp4 for Presenting Dynamic Mathematics on the Web: DynaMoW, an OCaml language Extension for the Run-Time Generation of Mathematical Contents and their Presentation on the Web 11

Using Camlp4 for Presenting Dynamic Mathematics on the Web: ...

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16th acm sigplan International conference on Functional programming (ICFP 11)

作者： Chyzak, Frederic Darrasse, Alexis INRIA Rocquencourt France

ISBN: (纸本)9781450308656

We report on the design and implementation of a programming tool, DynaMoW, to control interactive and incremental mathematical calculations to be presented on the web. This tool is implemented as a language extension of OCaml using Cam1p4. Fragments of mathematical code written for a computer-algebra system as well as fragments of mathematical web documents are embedded directly and naturally inside OCaml code. A DynaMoW-based application is made of independent web services, whose parameter types are checked by the OCaml extension. The approach is illustrated by two implementations of online mathematical encyclopedias on top of DynaMoW.

关键词： mathematical encyclopedias quotations antiquotations metaprogramming web services

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Efficient Synthesis of Probabilistic Programs 15

Efficient Synthesis of Probabilistic Programs

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36th acm sigplan conference on programming language design and implementation

作者： Nori, Aditya V. Ozair, Sherjil Rajamani, Sriram K. Vijaykeerthy, Deepak IIT Delhi Delhi India

ISBN: (纸本)9781450334686

We show how to automatically synthesize probabilistic programs from real-world datasets. Such a synthesis is feasible due to a combination of two techniques: (1) We borrow the idea of "sketching" from synthesis of deterministic programs, and allow the programmer to write a skeleton program with "holes". Sketches enable the programmer to communicate domain-specific intuition about the structure of the desired program and prune the search space, and (2) we design an efficient Markov Chain Monte Carlo (MCMC) based synthesis algorithm to instantiate the holes in the sketch with program fragments. Our algorithm efficiently synthesizes a probabilistic program that is most consistent with the data. A core difficulty in synthesizing probabilistic programs is computing the likelihood L (P vertical bar D) of a candidate program P generating data D. We propose an approximate method to compute likelihoods using mixtures of Gaussian distributions, thereby avoiding expensive computation of integrals. The use of such approximations enables us to speed up evaluation of the likelihood of candidate programs by a factor of 1000, and makes Markov Chain Monte Carlo based search feasible. We have implemented our algorithm in a tool called PSKETCH, and our results are encouraging-PSKETCH is able to automatically synthesize 16 non-trivial real-world probabilistic programs.

关键词： design Experimentation Performance Probabilistic programming Program Synthesis Markov Chain Monte Carlo

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Halide: A language and Compiler for Optimizing Parallelism, Locality, and Recomputation in Image Processing Pipelines

Halide: A Language and Compiler for Optimizing Parallelism, ...

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34th acm sigplan conference on programming language design and implementation (PLDI)

作者： Ragan-Kelley, Jonathan Barnes, Connelly Adams, Andrew Paris, Sylvain Durand, Fredo Amarasinghe, Saman MIT CSAIL United States Adobe United States

ISBN: (纸本)9781450320146

Image processing pipelines combine the challenges of stencil computations and stream programs. They are composed of large graphs of different stencil stages, as well as complex reductions, and stages with global or data-dependent access patterns. Because of their complex structure, the performance difference between a naive implementation of a pipeline and an optimized one is often an order of magnitude. Efficient implementations require optimization of both parallelism and locality, but due to the nature of stencils, there is a fundamental tension between parallelism, locality, and introducing redundant recomputation of shared values. We present a systematic model of the tradeoff space fundamental to stencil pipelines, a schedule representation which describes concrete points in this space for each stage in an image processing pipeline, and an optimizing compiler for the Halide image processing language that synthesizes high performance implementations from a Halide algorithm and a schedule. Combining this compiler with stochastic search over the space of schedules enables terse, composable programs to achieve state-of-the-art performance on a wide range of real image processing pipelines, and across different hardware architectures, including multicores with SIMD, and heterogeneous CPU+GPU execution. From simple Halide programs written in a few hours, we demonstrate performance up to 5x faster than hand-tuned C, intrinsics, and CUDA implementations optimized by experts over weeks or months, for image processing applications beyond the reach of past automatic compilers.

关键词： domain specific language compiler image processing locality parallelism redundant computation optimization GPU vectorization

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Automatic Generation of Library Bindings Using Static Analysis 09

Automatic Generation of Library Bindings Using Static Analys...

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acm sigplan conference on programming language design and implementation

作者： Ravitch, Tristan Jackson, Steve Aderhold, Eric Liblit, Ben Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

ISBN: (纸本)9781605583921

High-level languages are growing in popularity. However, decades of C software development have produced large libraries of fast, time-tested, meritorious code that are impractical to recreate from scratch. Cross-language bindings can expose low-level C code to high-level languages. Unfortunately, writing bindings by hand is tedious and error-prone, while mainstream binding generators require extensive manual annotation or fail to offer the language features that users of modem languages have come to expect. We present an improved binding-generation strategy based on static analysis of unannotated library source code. We characterize three high-level idioms that are not uniquely expressible in C's low-level type system: array parameters, resource managers, and multiple return values. We describe a suite of interprocedural analyses that recover this high-level information, and we show how the results can be used in a binding generator for the Python programming language. In experiments with four large C libraries, we find that our approach avoids the mistakes characteristic of hand-written bindings while offering a level of Python integration unmatched by prior automated approaches. Among the thousands of functions in the public interfaces of these libraries, roughly 40% exhibit the behaviors detected by our static analyses.

关键词： FFI foreign function interfaces bindings libraries dataflow analysis modular static program analysis multi-language code reuse

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The liberty structural specification language: A high-level modeling language for component reuse

The liberty structural specification language: A high-level ...

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Proceedings of the 2004 acm sigplan conference on programming language design and implementation (PLDI'04)

作者： Vachharajani, Manish Vachharajani, Neil August, David I. Department of Computer Science Princeton University Princeton NJ 08544 United States

Rapid exploration of the design space with simulation models is essential for quality hardware systems research and development. Despite striking commonalities across hardware systems, designers routinely fail to achieve high levels of reuse across models constructed in existing general-purpose and domain-specific languages. This lack of reuse adversely impacts hardware system design by slowing the rate at which ideas are evaluated. This paper presents an examination of existing languages to reveal their fundamental limitations regarding reuse in hardware modeling. With this understanding, a solution is described in the context of the design and implementation of the Liberty Structural Specification language (LSS), the input language for a publicly available high-level digital-hardware modeling tool called the Liberty Simulation Environment. LSS is the first language to enable low-overhead reuse by simultaneously supporting static inference based on hardware structure and flexibility via parameterizable structure. Through LSS, this paper also introduces a new type inference algorithm and a new programming language technique, called use-based specialization, which, in a manner analogous to type inference, customizes reusable components by statically inferring structural properties that otherwise would have had to have been specified manually.

关键词： Computer programming languages

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Incremental Inference for Probabilistic Programs 2018

Incremental Inference for Probabilistic Programs

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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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