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检索条件"任意字段=Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming"

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Evaluating the locality benefits of active messages

Evaluating the locality benefits of active messages

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proceedings of the 5th acm sigplan symposium on principles and practice of parallel programming

作者： Spertus, Ellen Dally, William J. Massachusetts Inst of Technology Cambridge United States

A major challenge in fine-grained computing is achieving locality without excessive scheduling overhead. We built two J-Machine implementations of a fine-grained programming model, the Berkeley threaded Abstract Machine. One implementation takes an Active Messages approach, maintaining a scheduling hierarchy in software in order to improve data cache performance. Another approach relies on the J-Machine's message queues and fast task switch, lowering the control costs at the expense of data locality. Our analysis measures the costs and benefits of each approach, for a variety of programs and cache configurations. the Active Messages implementation is strongest when miss penalties are high and for the finest-grained programs. the hardware-buffered implementation is strongest in direct-mapped caches, where it achieves substantially better instruction cache performance.

关键词： parallel processing systems

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Data and computation transformations for multiprocessors

Data and computation transformations for multiprocessors

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proceedings of the 5th acm sigplan symposium on principles and practice of parallel programming

作者： Anderson, Jennifer M. Amarasinghe, Saman P. Lam, Monica S. Stanford Univ Stanford United States

Effective memory hierarchy utilization is critical to the performance of modem multiprocessor architectures. We have developed the first compiler system that fully automatically parallelizes sequential programs and changes the original array layouts to improve memory system performance. Our optimization algorithm consists of two steps. the first step chooses the parallelization and computation assignment such that synchronization and data sharing are minimized. the second step then restructures the layout of the data in the shared address space with an algorithm that is based on a new data transformation framework. We ran our compiler on a set of application programs and measured their performance on the Stanford DASH multiprocessor. Our results show that the compiler can effectively optimize parallelism in conjunction with memory subsystem performance.

关键词： parallel processing systems

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Triolet: A programming system that unifies algorithmic skeleton interfaces for high-performance cluster computing 14

Triolet: A programming system that unifies algorithmic skele...

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2014 19th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2014

作者： Rodrigues, Christopher Jablin, thomas Dakkak, Abdul Hwu, Wen-Mei University of Illinois at Urbana-Champaign United States

ISBN: (纸本)9781450326568

Functional algorithmic skeletons promise a high-level programming interface for distributed-memory clusters that free developers from concerns of task decomposition, scheduling, and communication. Unfortunately, prior distributed functional skeleton frameworks do not deliver performance comparable to that achievable in a low-level distributed programming model such as C with MPI and OpenMP, even when used in concert with high-performance array libraries. there are several causes: they do not take advantage of shared memory on each cluster node;they impose a fixed partitioning strategy on input data;and they have limited ability to fuse loops involving skeletons that produce a variable number of outputs per input. We address these shortcomings in the Triolet programming language through a modular library design that separates concerns of parallelism, loop nesting, and data partitioning. We show how Triolet substantially improves the parallel performance of algorithms involving array traversals and nested, variable-size loops over what is achievable in Eden, a distributed variant of Haskell. We further demonstrate how Triolet can substantially simplify parallel programming relative to C with MPI and OpenMP while achieving 23.100% of its performance on a 128-core cluster. Copyright © 2014 acm.

关键词： parallel programming

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Improving parallelism and Locality with Asynchronous Algorithms

Improving Parallelism and Locality with Asynchronous Algorit...

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

作者： Liu, Lixia Li, Zhiyuan Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

ISBN: (纸本)9781605587080

As multicore chips become the main building blocks for high performance computers, many numerical applications face a performance impediment due to the limited hardware capacity to move data between the CPU and the off-chip memory. this is especially true for large computing problems solved by iterative algorithms because of the large data set typically used. Loop tiling, also known as loop blocking, was shown previously to be an effective way to enhance data locality, and hence to reduce the memory bandwidth pressure, for a class of iterative algorithms executed on a single processor. Unfortunately, the tiled programs suffer from reduced parallelism because only the loop iterations within a single tile can be easily parallelized. In this work, we propose to use the asynchronous model to enable effective loop tiling such that both parallelism and locality can be attained simultaneously. Asynchronous algorithms were previously proposed to reduce the communication cost and synchronization overhead between processors. Our new discovery is that carefully controlled asynchrony and loop tiling can significantly improve the performance of parallel iterative algorithms on multicore processors due to simultaneously attained data locality and loop-level parallelism. We present supporting evidence from experiments with three well-known numerical kernels.

关键词： asynchronous algorithms loop tiling parallel numerical programs data locality memory performance

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OPTIMAL MAPPING OF SEQUENCES OF DATA-parallel TASKS 95

OPTIMAL MAPPING OF SEQUENCES OF DATA-PARALLEL TASKS

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5th acm sigplan symposium on principles and practice of parallel programming

作者： SUBHLOK, J VONDRAN, G HEWLETT PACKARD CORP ADV LASER JET OPERAT BOISE ID 83714 USA

ISBN: (纸本)9780897917001

Many applications in a variety of domains including digital signal processing, image processing, and computer vision are composed of a sequence of tasks that act on a stream of input data sets in a pipelined manner. Recent research has established that these applications are best mapped to a massively parallel machine by dividing the tasks into modules and assigning a subset of the available processors to each module. this paper addresses the problem of optimally mapping such applications onto a massively parallel machine. We formulate the problem of optimizing throughput in task pipelines and present two new solution algorithms. the formulation uses a general and realistic model for inter-task communication, takes memory constraints into account, and addresses the entire problem of mapping which includes clustering tasks into modules, assignment of processors to modules, and possible replication of modules. the first algorithm is based on dynamic programming and finds the optimal mapping of k tasks onto P processors in O(P(4)k(2)) time. We also present a heuristic algorithm that is linear in the number of processors and establish with theoretical and practical results that the solutions obtained are optimal in practical situations. the entire framework is implemented as an automatic mapping tool for the Fx parallelizing compiler for High Performance Fortran. We present experimental results that demonstrate the importance of choosing a good mapping and show that the methods presented yield efficient mappings and predict optimal performance accurately.

关键词： parallel processing systems

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Space-efficient implementation of nested parallelism

Space-efficient implementation of nested parallelism

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proceedings of the 1997 6th acm sigplan symposium on principles and practice of parallel programming

作者： Narlikar, Girija J. Blelloch, Guy E. CMU Sch of Computer Science Pittsburgh United States

Many of today's high level parallel languages support dynamic, fine-grained parallelism. these languages allow the user to expose all the parallelism in the program, which is typically of a much higher degree than the number of processors. Hence an efficient scheduling algorithm is required to assign computations to processors at runtime. Besides having low overheads and good load balancing, it is important for the scheduling algorithm to minimize the space usage of the parallel program. this paper presents a scheduling algorithm that is provably space-efficient and time-efficient for nested parallel languages. In addition to proving the space and time bounds of the parallel schedule generated by the algorithm, we demonstrate that it is efficient in practice. We have implemented a runtime system that uses our algorithm to schedule parallel threads. the results of executing parallel programs on this system show that our scheduling algorithm significantly reduces memory usage compared to previous techniques, without compromising performance.

关键词： parallel processing systems

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Backtracking-based Load Balancing

Backtracking-based Load Balancing

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14th acm sigplan symposium on principles and practice of parallel programming

作者： Hiraishi, Tasuku Yasugi, Masahiro Umatani, Seiji Yuasa, Taiichi Kyoto Univ Acad Ctr Comp & Media Studies Kyoto 6068501 Japan Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan

ISBN: (纸本)9781605583976

High-productivity languages for parallel computing become more important as parallel environments including multicores become more common. Cilk is such a language. It provides good load balancing for many applications including irregular ones;that is, it keeps all workers busy by creating plenty of "logical" threads and adopting the oldest-first work stealing strategy. this paper proposes a "logical thread"-free framework called Tascell, which achieves a higher performance and supports a wider range of parallel environments including clusters without loss of productivity. A Tascell worker spawns a "real" task only when requested by another idle worker. the worker performs the spawning by temporarily "backtracking" and restoring its oldest task-spawnable state. Our approach eliminates the cost of spawning/managing logical threads. It also promotes the reuse of workspaces and improves the locality of reference since it does not need to prepare a workspace for each concurrently runnable logical thread. Furthermore, Tascell enables elegant and highly-efficient backtrack search algorithms with delayed workspace copying. For instance, our 16-queens problem solver is 1.86 times faster than Cilk on a system with two dual-core processors. Our approach also enables a single program to run in both shared and distributed memory environments with reasonable efficiency and scalability.

关键词： Design Languages Performance load balancing parallel computing backtracking backtrack search

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PARRAY: A Unifying Array Representation for Heterogeneous parallelism 12

PARRAY: A Unifying Array Representation for Heterogeneous Pa...

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17th acm sigplan symposium on principles and practice of parallel programming

作者： Chen, Yifeng Cui, Xiang Mei, Hong Peking Univ Sch EECS HCST Key Lab Beijing 100871 Peoples R China

ISBN: (纸本)9781450311601

this paper introduces a programming interface called PARRAY (or parallelizing ARRAYs) that supports system-level succinct programming for heterogeneous parallel systems like GPU clusters. the current practice of software development requires combining several low-level libraries like Pthread, OpenMP, CUDA and MPI. Achieving productivity and portability is hard with different numbers and models of GPUs. PARRAY extends mainstream C programming with novel array types of the following features:1)the dimensions of an array type are nested in a tree structure, conceptually reflecting the memory hierarchy;2) the definition of an array type may contain references to other array types, allowing sophisticated array types to be created for parallelization;3) threads also form arrays that allow programming in a Single-Program Multiple-Code block (SPMC) style to unify various sophisticated communication patterns. this leads to shorter, more portable and maintainable parallel codes, while the programmer still has control over performance-related features necessary for deep manual optimization. Although the source-to-source code generator only faithfully generates low-level library calls according to the type information,higher-level programming and automatic performance optimization are still possible through building libraries of subprograms on top of PARRAY. the case study on cluster FFT illustrates a simple 30-line code that 2x-outperforms Intel Cluster MKL on the Tianhe-1A system with 7168 Fermi GPUs and 14336 CPUs.

关键词： Languages Performance theory parallel programming Array Representation Heterogeneous parallelism GPU Clusters

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Teaching parallel computing to science faculty: Best practices and common pitfalls

Teaching parallel computing to science faculty: Best practic...

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2006 acm sigplan symposium on principles and practice of parallel programming, PPOPP'06

作者： Joiner, David A. Gray, Paul Murphy, thomas Peck, Charles Kean University 1000 Morris Ave Union NJ 07083 University of Northern Iowa 1227 West 27th St Cedar Falls IA 50614 Contra Costa College 2600 Mission Bell Dr San Pablo CA 94806 Earlham College 801 National Rd West Richmond IN 47374

ISBN: (纸本)1595931899

In 2002, we first brought High Performance Computing (HPC) methods to the college classroom as a way to enrich Computational Science education. through the years, we have continued to facilitate college faculty in science, technology, engineering, and mathematics (STEM) disciplines to stay current with HPC methodologies. We have accomplished this by designing and delivering faculty workshops, hosted in a variety of lab settings, as well as by developing tools supporting the technical infrastructure necessary for HPC education, all this without requiring access to traditional HPC computing platforms. In all, we have so far presented 16 professional development workshops for close to 400 predominantly undergraduate STEM faculty. this paper presents the result of internal formative evaluation by workshop instructors and the materials and tools developed during that process. We did this work as part of the National Computational Science Institute (NCSI) and in collaboration with the following groups: the Minority Serving Institutions - High Performance Computing (MSI-HPC) program of the National Computational Science Alliance the Consortium for Computing Sciences in Colleges (CCSC) the Center for Excellence in High Performance Computing the Oklahoma University Supercomputing symposium series the Super Computing (SC) conference series education program We presented learners with a sequence of interactive, "run it, modify it, build it" open-ended lab exercises drawn from a variety of disciplines. Interactivity means having the ability to change parallel and algorithmic parameters, e.g. running software on more than one machine, using different models, refining the model, changing the problem scale, using different parallel algorithms. there is a lack of scientific parallel curricula suitable for illustrating Computational Science principles in the classroom. We addressed this need by locating, and where necessary creating, suitable open source software, data-sets, and cur

关键词： parallel processing systems

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Effective Performance Measurement and Analysis of Multithreaded Applications

Effective Performance Measurement and Analysis of Multithrea...

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14th acm sigplan symposium on principles and practice of parallel programming

作者： Tallent, Nathan R. Mellor-Crummey, John M. Rice Univ Houston TX 77251 USA

ISBN: (纸本)9781605583976

Understanding why the performance of a multithreaded program does not improve linearly with the number of cores in a shared-memory node populated with one or more multicore processors is a problem of growing practical importance. this paper makes three contributions to performance analysis of multithreaded programs. First, we describe how to measure and attribute parallel idleness, namely, where threads are stalled and unable to work. this technique applies broadly to programming models ranging from explicit threading (e. g., Pthreads) to higher-level models such as Cilk and OpenMP. Second, we describe how to measure and attribute parallel overhead-when a thread is performing miscellaneous work other than executing the user's computation. By employing a combination of compiler support and post-mortem analysis, we incur no measurement cost beyond normal profiling to glean this information. Using idleness and overhead metrics enables one to pinpoint areas of an application where concurrency should be increased (to reduce idleness), decreased (to reduce overhead), or where the present parallelization is hopeless (where idleness and overhead are both high). third, we describe how to measure and attribute arbitrary performance metrics for high-level multithreaded programming models, such as Cilk. this requires bridging the gap between the expression of logical concurrency in programs and its realization at run-time as it is adaptively partitioned and scheduled onto a pool of threads. We have prototyped these ideas in the context of Rice University's HPCTOOLKIT performance tools. We describe our approach, implementation, and experiences applying this approach to measure and attribute work, idleness, and overhead in executions of Cilk programs.

关键词： Performance Measurement Algorithms Performance Analysis Call Path Profiling Multithreaded programming Models HPCTOOLKIT

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