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检索条件"任意字段=16th ACM Symposium on Principles and Practice of Parallel Programming"

共 407 条记录，以下是211-220 订阅

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Using Data Structure Knowledge for Efficient Lock Generation and Strong Atomicity

Using Data Structure Knowledge for Efficient Lock Generation...

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15th acm SIGPLAN symposium on principles and practice of parallel programming

作者： Upadhyaya, Gautam Midkiff, Samuel P. Pai, Vijay S. Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781605587080

To achieve high-performance on multicore systems, shared-memory parallel languages must efficiently implement atomic operations. the commonly used and studied paradigms for atomicity are fine-grained locking, which is both difficult to program and error-prone;optimistic software transactions, which require substantial overhead to detect and recover from atomicity violations;and compiler-generation of locks from programmer-specified atomic sections, which leads to serialization whenever imprecise pointer analysis suggests the mere possibility of a conflicting operation. this paper presents a new strategy for compiler-generated locking that uses data structure knowledge to facilitate more precise alias and lock generation analyses and reduce unnecessary serialization. Implementing and evaluating these ideas in the Java language shows that the new strategy achieves eight-thread speedups of 0.83 to 5.9 for the five STAMP benchmarks studied, outperforming software transactions on all but one benchmark, and nearly matching programmer-specified fine-grained locks on all but one benchmark. the results also indicate that compiler knowledge of data structures improves the effectiveness of compiler analysis, boosting eight-thread performance by up to 300%. Further, the new analysis allows for software support of strong atomicity with less than 1% overhead for two benchmarks and less than 20% for three others. the strategy also nearly matches the performance of programmer-specified fine-grained locks for the SPECjbb2000 benchmark, which has traditionally not been amenable to static analyses.

关键词： Algorithms Performance Design Automatic lock generation transactional memory parallel programming

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Generative Datalog with Continuous Distributions 20

Generative Datalog with Continuous Distributions

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39th acm SIGMOD-SIGACT-SIGAI symposium on principles of Database Systems (PODS)

作者： Grohe, Martin Kaminski, Benjamin Lucien Katoen, Joost-Pieter Lindner, Peter Rhein Westfal TH Aachen Aachen Germany UCL London England

ISBN: (纸本)9781450371087

Arguing for the need to combine declarative and probabilistic programming, Barany et al. (TODS 2017) recently introduced a probabilistic extension of Datalog as a "purely declarative probabilistic programming language." We revisit this language and propose a more foundational approach towards defining its semantics. It is based on standard notions from probability theory known as stochastic kernels and Markov processes. this allows us to extend the semantics to continuous probability distributions, thereby settling an open problem posed by Barany et al. We show that our semantics is fairly robust, allowing both parallel execution and arbitrary chase orders when evaluating a program. We cast our semantics in the framework of infinite probabilistic databases (Grohe and Lindner, ICDT 2020), and we show that the semantics remains meaningful even when the input of a probabilistic Datalog program is an arbitrary probabilistic database.

关键词： Datalog Probabilistic Databases Generative Datalog Measure theory Stochastic Kernels Probabilistic programming

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Efficient Race Detection with Futures 19

Efficient Race Detection with Futures

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24th acm SIGPLAN symposium on principles and practice of parallel programming (PPoPP)

作者： Utterback, Robert Agrawal, Kunal Fineman, Jeremy Lee, I-Ting Angelina Monmouth Coll Monmouth IL 61462 USA Washington Univ St Louis MO 14263 USA Georgetown Univ Washington DC 20057 USA

ISBN: (纸本)9781450362252

this paper addresses the problem of provably efficient and practically good on-the-fly determinacy race detection in task parallel programs that use futures. Prior works on determinacy race detection have mostly focused on either task parallel programs that follow a series-parallel dependence structure or ones with unrestricted use of futures that generate arbitrary dependences. In this work, we consider a restricted use of futures and show that we can detect races more efficiently than with general use of futures. Specifically, we present two algorithms: MultiBags and MultiBags+. MultiBags targets programs that use futures in a restricted fashion and runs in time O(T-1 alpha(m, n)), where T-1 is the sequential running time of the program, a is the inverse Ackermann's function, m is the total number of memory accesses, n is the dynamic count of places at which parallelism is created. Since a is a very slowly growing function (upper bounded by 4 for all practical purposes), it can be treated as a close-to-constant overhead. MultiBags+ is an extension of MultiBags that target programs with general use of futures. It runs in time O((T-1 + k(2))alpha(m, n)) where T-1, alpha, m and n are defined as before, and k is the number of future operations in the computation. We implemented both algorithms and empirically demonstrate their efficiency.

关键词： dynamic program analysis determinacy race race detection series-parallel maintenance

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Flattening and parallelizing irregular, recurrent loop nests 95

Flattening and parallelizing irregular, recurrent loop nests

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Proceedings of the 5th acm SIGPLAN symposium on principles and practice of parallel programming

作者： Ghuloum, Anwar M. Fisher, Allan L. Carnegie Mellon Univ Pittsburgh PA United States

ISBN: (纸本)9780897917001

Irregular loop nests in which the loop bounds are determined dynamically by indexed arrays are difficult to compile into expressive parallel constructs, such as segmented scans and reductions. In this paper, we describe a suite of transformations to automatically parallelize such irregular loop nests, even in the presence of recurrences. We describe a simple, general loop flattening transformation, along with new optimizations which make it a viable compiler transformation. A robust recurrence parallelization technique is coupled to the loop flattening transformation, allowing parallelization of segmented reductions, scans, and combining-sends over arbitrary associative operators. We discuss the implementation and performance results of the transformations in a parallelizing Fortran 77 compiler for the Cray C90 supercomputer. In particular, we focus on important sparse matrix-vector multiplication kernels, for one of which we are able to automatically derive an algorithm used by one of the fastest library routines available.

关键词： parallel processing systems

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LoPC: modeling contention in parallel algorithms

LoPC: modeling contention in parallel algorithms

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Proceedings of the 1997 6th acm SIGPLAN symposium on principles and practice of parallel programming

作者： Frank, Matthew I. Agarwal, Anant Vernon, Mary K. Massachusetts Inst of Technology Cambridge United States

parallel algorithm designers need computational models that take first order system costs into account, but are also simple enough to use in practice. this paper introduces the LoPC model, which is inspired by the LogP model but accounts for contention for message processing resources in parallel algorithms on a multiprocessor or network of workstations. LoPC takes the L, o and P parameters directly from the LogP model and uses them to predict the cost of contention, C. this paper defines the LoPC model and derives the general form of the model for parallel applications that communicate via active messages. Model modifications for systems that implement coherent shared memory abstractions are also discussed. We carry out the analysis for two important classes of applications that have irregular communication. In the case of parallel applications with homogeneous all-to-any communication, such as sparse matrix computations, the analysis yields a simple rule of thumb and insight into contention costs. In the case of parallel client-server algorithms, the LoPC analysis provides a simple and accurate calculation of the optimal allocation of nodes between clients and servers. the LoPC estimates for these applications are shown to be accurate when compared against event driven simulation and against a sparse matrix computation on the MIT Alewife multiprocessor.

关键词： parallel algorithms

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An Adaptive Performance Modeling Tool for GPU Architectures

An Adaptive Performance Modeling Tool for GPU Architectures

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15th acm SIGPLAN symposium on principles and practice of parallel programming

作者： Baghsorkhi, Sara S. Delahaye, Matthieu Patel, Sanjay J. Gropp, William D. Hwu, Wen-mei W. Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9781605587080

this paper presents an analytical model to predict the performance of general-purpose applications on a GPU architecture. the model is designed to provide performance information to an auto-tuning compiler and assist it in narrowing down the search to the more promising implementations. It can also be incorporated into a tool to help programmers better assess the performance bottlenecks in their code. We analyze each GPU kernel and identify how the kernel exercises major GPU microarchitecture features. To identify the performance bottlenecks accurately, we introduce an abstract interpretation of a GPU kernel, work flow graph, based on which we estimate the execution time of a GPU kernel. We validated our performance model on the NVIDIA GPUs using CUDA (Compute Unified Device Architecture). For this purpose, we used data parallel benchmarks that stress different GPU microarchitecture events such as uncoalesced memory accesses, scratch-pad memory bank conflicts, and control flow divergence, which must be accurately modeled but represent challenges to the analytical performance models. the proposed model captures full system complexity and shows high accuracy in predicting the performance trends of different optimized kernel implementations. We also describe our approach to extracting the performance model automatically from a kernel code.

关键词： Design Measurement Performance Analytical model GPU parallel programming Performance estimation

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Modernizing parallel code with pattern analysis 21

Modernizing parallel code with pattern analysis

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26th acm SIGPLAN symposium on principles and practice of parallel programming, PPoPP 2021

作者： Lozano, Roberto Castañeda Cole, Murray Franke, Björn University of Edinburgh School of Informatics Edinburgh United Kingdom

ISBN: (纸本)9781450382946

Fifty years of parallel programming has generated a substantial legacy parallel codebase, creating a new portability challenge: re-parallelizing already parallel code. Our solution exploits inherently portable parallel patterns, and addresses the challenge of identifying patternization opportunities in legacy parallel code via constraint matching on traced dynamic dataflow graphs. Notably, this makes the analysis source-independent and equally applicable to sequential and parallel legacy code. We identify various map and reduction patterns, including compositions, in Pthreads code. Experiments with the Starbench suite show that our analysis is effective (finding 86% of the patterns known in the literature), accurate (reporting actual patterns in 98% of the cases), and efficient (scaling linearly with the size of the execution traces). We re-express the found patterns via a parallel pattern library, making code freely portable across CPU/GPU systems and performing competitively with hand-tuned implementations at zero additional effort. © 2021 acm.

关键词： Pattern matching

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the Tasks with Effects Model for Safe Concurrency 13

The Tasks with Effects Model for Safe Concurrency

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18th acm SIGPLAN symposium on principles and practice of parallel programming

作者： Heumann, Stephen T. Adve, Vikram S. Wang, Shengjie Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9781450319225

Today's widely-used concurrent programming models either provide weak safety guarantees, making it easy to write code with subtle errors, or are limited in the class of programs that they can express. We propose a new concurrent programming model based on tasks with effects that offers strong safety guarantees while still providing the flexibility needed to support the many ways that con-currency is used in complex applications. the core unit of work in our model is a dynamically-created task. the model's key feature is that each task has programmer-specified effects, and a run-time scheduler is used to ensure that two tasks are run concurrently only if they have non-interfering effects. through the combination of statically verifying the declared effects of tasks and using an effect-aware run-time scheduler, our model is able to guarantee strong safety properties, including data race freedom and atomicity. It is also possible to use our model to write programs and computations that can be statically proven to behave deterministically. We describe the tasks with effects programming model and provide a formal dynamic semantics for it. We also describe our implementation of this model in an extended version of Java and evaluate its use in several programs exhibiting various patterns of concurrency.

关键词： Languages Verification Design Performance Tasks effects task scheduling concurrent and parallel programming task isolation data race freedom atomicity determinism

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Poster: the Problem-Based Benchmark Suite PBBS), V2 27

Poster: The Problem-Based Benchmark Suite PBBS), V2

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27th acm SIGPLAN symposium on principles and practice of parallel programming (PPoPP)

作者： Anderson, Daniel Blelloch, Guy E. Dhulipala, Laxman Dobson, Magdalen Sun, Yihan Carnegie Mellon Univ Pittsburgh Pittsburgh PA USA Univ Maryland Coll Park MD USA UC Riverside Riverside Riverside CA USA

ISBN: (纸本)9781450392044

the Problem-Based Benchmark Suite (PBBS) is a set of benchmark problems designed for comparing algorithms, implementations and platforms. For each problem, the suite defines the problem in terms of the input-output relationship, and supplies a set of input instances along with input generators, a default implementation, code for checking correctness or accuracy, and a timing harness. the suite makes it possible to compare different algorithms, platforms (e.g. CPU vs CPU), and implementations using different programming languages or libraries. the purpose is to better understand how well a wide variety of problems parallelize, and what techniques/algorithms are most effective. the suite was first announced in 2012 with 14 benchmark problems. Here we describe some significant updates. In particular, we have added nine new benchmarks from a mix of problems in text processing, computational geometry and machine learning. We have further optimized the default implementations;several are the fastest available for multicore CPUs, often achieving near perfect speedup on the 72 core machine we test them on. the suite now also supplies significantly larger default test instances, as well as a broader variety, with many derived from real-world data.

关键词： benchmarking parallel algorithms performance

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A framework for practical parallel fast matrix multiplication 2015

A framework for practical parallel fast matrix multiplicatio...

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20th acm SIGPLAN symposium on principles and practice of parallel programming, PPoPP 2015

作者： Benson, Austin R. Ballard, Grey Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States Sandia National Laboratories LivermoreCA United States

ISBN: (纸本)9781450332057

Matrix multiplication is a fundamental computation in many scientific disciplines. In this paper, we show that novel fast matrix multiplication algorithms can significantly outperform vendor implementations of the classical algorithm and Strassen's fast algorithm on modest problem sizes and shapes. Furthermore, we show that the best choice of fast algorithm depends not only on the size of the matrices but also the shape. We develop a code generation tool to automatically implement multiple sequential and shared-memory parallel variants of each fast algorithm, including our novel parallelization scheme. this allows us to rapidly benchmark over 20 fast algorithms on several problem sizes. Furthermore, we discuss a number of practical implementation issues for these algorithms on shared-memory machines that can direct further research on making fast algorithms practical. Copyright 2015 acm.

关键词： Matrix algebra

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