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检索条件"任意字段=Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming"

共 53 条记录，以下是11-20 订阅

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Hierarchical Memory Management for Mutable State 18

Hierarchical Memory Management for Mutable State

引用

23rd acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Guatto, Adrien Westrick, Sam Raghunathan, Ram Acar, Umut Fluet, Matthew Carnegie Mellon Univ Pittsburgh PA 15213 USA Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9781450349826

It is well known that modern functional programming languages are naturally amenable to parallel programming. Achieving efficient parallelism using functional languages, however, remains difficult. Perhaps the most important reason for this is their lack of support for efficient in-place updates, i.e., mutation, which is important for the implementation of both parallel algorithms and the run-time system services (e.g., schedulers and synchronization primitives) used to execute them. In this paper, we propose techniques for efficient mutation in parallel functional languages. To this end, we couple the memory manager with the thread scheduler to make reading and updating data allocated by nested threads efficient. We describe the key algorithms behind our technique, implement them in the MLton Standard ML compiler, and present an empirical evaluation. Our experiments show that the approach performs well, significantly improving efficiency over existing functional language implementations.

关键词： parallel functional language implementation garbage collection hierarchical heaps mutation promotion

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Poster: Performance Challenges in Modular parallel Programs 23

Poster: Performance Challenges in Modular Parallel Programs

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23rd acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Acar, Umut A. Aksenov, Vitaly Chargueraud, Arthur Rainey, Mike Carnegie Mellon Univ Pittsburgh PA 15213 USA INRIA Rocquencourt France ITMO Univ St Petersburg Russia Univ Strasbourg CNRS ICube Strasbourg France

ISBN: (纸本)9781450349826

Over the past decade, many programming languages and systems for parallel-computing have been developed, including Cilk, Fork/Join Java, Habanero Java, parallel Haskell, parallel ML, and X10. Although these systems raise the level of abstraction at which parallel code are written, performance continues to require the programmer to perform extensive optimizations and tuning, often by taking various architectural details into account. One such key optimization is granularity control, which requires the programmer to determine when and how parallel tasks should be sequentialized. In this paper, we briefly describe some of the challenges associated with automatic granularity control when trying to achieve portable performance for parallel programs with arbitrary nesting of parallel constructs. We consider a result from the functional-programming community, whose starting point is to consider an "oracle" that can predict the work of parallel codes, and thereby control granularity. We discuss the challenges in implementing such an oracle and proving that it has the desired theoretical properties under the nested-parallel programming model.

关键词： Including parallel loops and nested parallel computations Such as Cilk's spawn and sync Suffice to express many common parallel patterns Wherein parallel computations may themselves start and synchronize with other parallel computations

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programming with Hardware Lock Elision 13

Programming with Hardware Lock Elision

引用

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

作者： Afek, Yehuda Levy, Amir Morrison, Adam Tel Aviv Univ Blavatnik Sch Comp Sci IL-69978 Tel Aviv Israel

We present a simple yet effective technique for improving performance of lock-based code using the hardware lock elision (HLE) feature in Intel's upcoming Haswell processor. We also describe how to extend Haswell&... 详细信息

ISBN: (纸本)9781450319225

关键词： Haswell hardware lock elision speculative execution

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Reducing Contention Through Priority Updates 13

Reducing Contention Through Priority Updates

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

作者： Shun, Julian Blelloch, Guy E. Fineman, Jeremy T. Gibbons, Phillip B. Carnegie Mellon Univ Pittsburgh PA 15213 USA Georgetown Univ Washington DC 20057 USA Intel Labs Pittsburgh PA USA

No abstract available.

ISBN: (纸本)9781450319225

No abstract available.

关键词： Experimentation Performance parallel programming Contention

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Efficient parallel determinacy race detection for two-dimensional dags 18

Efficient parallel determinacy race detection for two-dimens...

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23rd acm sigplan symposium on principles and practice of parallel programming, PPoPP 2018

作者： Xu, Yifan Angelina Lee, I-Ting Agrawal, Kunal Washington University St. Louis United States

ISBN: (纸本)9781450349826

A program is said to have a determinacy race if logically parallel parts of a program access the same memory location and one of the accesses is a write. These races are generally bugs in the program since they lead to non-deterministic program behavior - different schedules of the program can lead to different results. Most prior work on detecting these races focuses on a subclass of programs with fork-join parallelism. This paper presents a race-detection algorithm, 2D-Order, for detecting races in a more general class of programs, namely programs whose dependence structure can be represented as planar dags embedded in 2D grids. Such dependence structures arise from programs that use pipelined parallelism or dynamic programming recurrences. Given a computation with T1 work and T∞ span, 2D-Order executes it while also detecting races in O(T1/P +T∞) time on P processors, which is asymptotically optimal. We also implemented PRacer, a race-detection algorithm based on 2D-Order for Cilk-P, which is a language for expressing pipeline parallelism. Empirical results demonstrate that PRacer incurs reasonable overhead and exhibits scalability similar to the baseline (executions without race detection) when running on multiple cores. © 2018 Copyright held by the owner/author(s).

关键词： Dynamic programming

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Swift/T: Scalable Data Flow programming for Many-Task Applications 13

Swift/T: Scalable Data Flow Programming for Many-Task Applic...

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

作者： Wozniak, Justin M. Armstrong, Timothy G. Wilde, Michael Katz, Daniel S. Lusk, Ewing Foster, Ian T. Argonne Natl Lab Argonne IL 60439 USA Univ Chicago Chicago IL 60637 USA

Swift/T, a novel programming language implementation for highly scalable data flow programs, is presented.

ISBN: (纸本)9781450319225

Swift/T, a novel programming language implementation for highly scalable data flow programs, is presented.

关键词： Languages MPI ADLB Swift Turbine exascale concurrency dataflow futures

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FlashR: parallelize and Scale R for Machine Learning using SSDs 18

FlashR: Parallelize and Scale R for Machine Learning using S...

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23rd acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Zheng, Da Mhembere, Disa Vogelstein, Joshua T. Priebe, Carey E. Burns, Randal Amazon Seattle WA 98109 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Johns Hopkins Univ Dept Biomed Engn Inst Computat Med Baltimore MD 21218 USA Johns Hopkins Univ Dept Appl Math & Stat Baltimore MD 21218 USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9781450349826

R is one of the most popular programming languages for statistics and machine learning, but it is slow and unable to scale to large datasets. The general approach for having an efficient algorithm in R is to implement it in C or FORTRAN and provide an R wrapper. FlashR accelerates and scales existing R code by parallelizing a large number of matrix functions in the R base package and scaling them beyond memory capacity with solid-state drives (SSDs). FlashR performs memory hierarchy aware execution to speed up parallelized R code by (i) evaluating matrix operations lazily, (ii) performing all operations in a DAG in a single execution and with only one pass over data to increase the ratio of computation to I/O, (iii) performing two levels of matrix partitioning and reordering computation on matrix partitions to reduce data movement in the memory hierarchy. We evaluate FlashR on various machine learning and statistics algorithms on inputs of up to four billion data points. Despite the huge performance gap between SSDs and RAM, FlashR on SSDs closely tracks the performance of FlashR in memory for many algorithms. The R implementations in FlashR outperforms H2O and Spark MLlib by a factor of 3-20.

关键词： R parallel machine learning solid-state drives

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Griffin: Uniting CPU and GPU in Information Retrieval Systems for Intra-Query parallelism 18

Griffin: Uniting CPU and GPU in Information Retrieval System...

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23rd acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Liu, Yang Wang, Jianguo Swanson, Steven WDC Res Chippenham Wilts England Univ Calif San Diego San Diego CA USA

ISBN: (纸本)9781450349826

Interactive information retrieval services, such as enterprise search and document search, must provide relevant results with consistent, low response times in the face of rapidly growing data sets and query loads. These growing demands have led researchers to consider a wide range of optimizations to reduce response latency, including query processing parallelization and acceleration with co-processors such as GPUs. However, previous work runs queries either on GPU or CPU, ignoring the fact that the best processor for a given query depends on the query's characteristics, which may change as the processing proceeds. We present Griffin, an IR systems that dynamically combines GPU- and CPU-based algorithms to process individual queries according to their characteristics. Griffin uses state-of-the-art CPU-based query processing techniques and incorporates a novel approach to GPU-based query evaluation. Our GPU-based approach, as far as we know, achieves the best available GPU search performance by leveraging a new compression scheme and exploiting an advanced merge-based intersection algorithm. We evaluate Griffin with real world queries and datasets, and show that it improves query performance by 10x compared to a highly optimized CPU-only implementation, and 1.5x compared to our GPU-approach running alone. We also find that Griffin helps reduce the 95th-, 99th-, and 99.9th-percentile query response time by 10.4x, 16.1x, and 26.8x, respectively.

关键词： Information Retrieval Search Engines Query Processing GPU parallel Algorithms

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HPVM: Heterogeneous parallel Virtual Machine 18

HPVM: <i>Heterogeneous Parallel Virtual Machine</i>

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23rd acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Kotsifakou, Maria Srivastava, Prakalp Sinclair, Matthew D. Komuravelli, Rakesh Adve, Vikram Adve, Sarita Univ Illinois Dept Comp Sci Champaign IL 61820 USA Qualcomm Technol Inc San Diego CA USA

ISBN: (纸本)9781450349826

We propose a parallel program representation for heterogeneous systems, designed to enable performance portability across a wide range of popular parallel hardware, including GPUs, vector instruction sets, multicore CPUs and potentially FPGAs. Our representation, which we call HPVM, is a hierarchical dataflow graph with shared memory and vector instructions. HPVM supports three important capabilities for programming heterogeneous systems: a compiler intermediate representation (IR), a virtual instruction set (ISA), and a basis for runtime scheduling;previous systems focus on only one of these capabilities. As a compiler IR, HPVM aims to enable effective code generation and optimization for heterogeneous systems. As a virtual ISA, it can be used to ship executable programs, in order to achieve both functional portability and performance portability across such systems. At runtime, HPVM enables flexible scheduling policies, both through the graph structure and the ability to compile individual nodes in a program to any of the target devices on a system. We have implemented a prototype HPVM system, defining the HPVM IR as an extension of the LLVM compiler IR, compiler optimizations that operate directly on HPVM graphs, and code generators that translate the virtual ISA to NVIDIA GPUs, Intel's AVX vector units, and to multicore X86-64 processors. Experimental results show that HPVM optimizations achieve significant performance improvements, HPVM translators achieve performance competitive with manually developed OpenCL code for both GPUs and vector hardware, and that runtime scheduling policies can make use of both program and runtime information to exploit the flexible compilation capabilities. Overall, we conclude that the HPVM representation is a promising basis for achieving performance portability and for implementing parallelizing compilers for heterogeneous parallel systems.

关键词： Virtual ISA Compiler parallel IR Heterogeneous Systems GPU Vector SIMD

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Coarse-grain parallel programming 3

Coarse-grain parallel programming

引用

3rd acm sigplan symposium on principles and practice of parallel programming, PPOPP 1991

作者： Lam, Monica S. Rinard, Martin C. Computer Systems Laboratory Stanford University CA94305 United States

ISBN: (纸本)0897913906

This paper presents Jade, a language which allows a programmer to easily express dynamic coarse-grain parallelism. Starting with a sequential program, a programmer augments those sections of code to be parallelized with abstract data usage information. The compiler and run-Time system use this information to concurrently execute the program while respecting the progmm's data dependence constraints. Using Jade can significantly reduce the time and effort required to develop and maintain a parallel version of an imperative application with serial semantics. The paper introduces the basic principles of the language, compares Jade with other existing lrmguages, and presents the performance of a sparse matrix Cholesky factorization algorithm implemented in Jade. © 1991 Association for Computing Machinery. All Rights Reserved.

关键词： parallel programming

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