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检索条件"任意字段=14th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming"

共 382 条记录，以下是111-120 订阅

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Automatic Problem Size Sensitive Task Partitioning on Heterogeneous parallel Systems 13

Automatic Problem Size Sensitive Task Partitioning on Hetero...

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

作者： Grasso, Ivan Kofler, Klaus Cosenza, Biagio Fahringer, thomas Univ Innsbruck Inst Informat A-6020 Innsbruck Austria

ISBN: (纸本)9781450319225

In this paper we propose a novel approach which automatizes task partitioning in heterogeneous systems. Our framework is based on the Insieme Compiler and Runtime infrastructure [1]. the compiler translates a single-device OpenCL program into a multi-device OpenCL program. the runtime system then performs dynamic task partitioning based on an offline-generated prediction model. In order to derive the prediction model, we use a machine learning approach that incorporates static program features as well as dynamic, input sensitive features. Our approach has been evaluated over a suite of 23 programs and achieves performance improvements compared to an execution of the benchmarks on a single CPU and a single GPU only.

关键词： Languages Algorithms Performance heterogeneous computing compilers GPU task partitioning code analysis machine learning runtime system

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Data Transformations Enabling Loop Vectorization on Multithreaded Data parallel Architectures

Data Transformations Enabling Loop Vectorization on Multithr...

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

作者： Jang, Byunghyun Mistry, Perhaad Schaa, Dana Dominguez, Rodrigo Kaeli, David Northeastern Univ Dept ECE Boston MA 02115 USA

ISBN: (纸本)9781605587080

Loop vectorization, a key feature exploited to obtain high performance on Single Instruction Multiple Data (SIMD) vector architectures, is significantly hindered by irregular memory access patterns in the data stream. this paper describes data transformations that allow us to vectorize loops targeting massively multithreaded data parallel architectures. We present a mathematical model that captures loop-based memory access patterns and computes the most appropriate data transformations in order to enable vectorization. Our experimental results show that the proposed data transformations can significantly increase the number of loops that can be vectorized and enhance the data-level parallelism of applications. Our results also show that the overhead associated with our data transformations can be easily amortized as the size of the input data set increases. For the set of high performance benchmark kernels studied, we achieve consistent and significant performance improvements (up to 11.4X) by applying vectorization using our data transformation approach.

关键词： Loop Vectorization Data Transformation GPGPU

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Exploiting Vector and Multicore parallelism for Recursive, Data- and Task-parallel Programs 17

Exploiting Vector and Multicore Parallelism for Recursive, D...

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22nd acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Ren, Bin Krishnamoorthy, Sriram Agrawal, Kunal Kulkarni, Milind Coll William & Mary Williamsburg VA 23187 USA Pacific Northwest Natl Labs Richland WA USA Washington Univ St Louis St Louis MO USA Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781450344937

Modern hardware contains parallel execution resources that are well-suited for data-parallelism vector units and task parallelism multicores. However, most work on parallel scheduling focuses on one type of hardware or the other. In this work, we present a scheduling framework that allows for a unified treatment of task- and data-parallelism. Our key insight is an abstraction, task blocks, that uniformly handles data-parallel iterations and task-parallel tasks, allowing them to be scheduled on vector units or executed independently as multicores. Our framework allows us to define schedulers that can dynamically select between executing task blocks on vector units or multicores. We show that these schedulers are asymptotically optimal, and deliver the maximum amount of parallelism available in computation trees. To evaluate our schedulers, we develop program transformations that can convert mixed data- and task-parallel programs into task block based programs. Using a prototype instantiation of our scheduling framework, we show that, on an 8-core system, we can simultaneously exploit vector and multicore parallelism to achieve 14 x-108 x speedup over sequential baselines.

关键词： Task parallelism Data parallelism General Scheduler

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Effective Communication and Computation Overlap with Hybrid MPI/SMPSs

Effective Communication and Computation Overlap with Hybrid ...

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

作者： Marjanovic, Vladimir Labarta, Jesus Ayguade, Eduard Valero, Mateo Barcelona Supercomp Ctr BSC CNS Dept Comp Sci Barcelona Spain Tech Univ Catalunya UPC Comp Architecture Dept Barcelona Spain

ISBN: (纸本)9781605587080

Communication overhead is one of the dominant factors affecting performance in high-performance computing systems. To reduce the negative impact of communication, programmers overlap communication and computation by using asynchronous communication primitives. this increases code complexity, requiring more development effort and making less readable programs. this paper presents the hybrid use of MPI and SMPSs (SMP superscalar, a task-based shared-memory programming model) that allows the programmer to easily introduce the asynchrony necessary to overlap communication and computation. We demonstrate the hybrid use of MPI/SMPSs with the high-performance LINPACK benchmark (HPL), and compare it to the pure MPI implementation, which uses the look-ahead technique to overlap communication and computation. the hybrid MPI/SMPSs version significantly improves the performance of the pure MPI version, getting close to the asymptotic performance at medium problem sizes and still getting significant benefits at small/large problem sizes.

关键词： Algorithms Performance Languages parallel programming model MPI hybrid MPI/SMPSs LINPACK

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Work-stealing with Configurable Scheduling Strategies 13

Work-stealing with Configurable Scheduling Strategies

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

作者： Wimmer, Martin Traeff, Jesper Larsson Cederman, Daniel Tsigas, Philippas Vienna Univ Technol Fac Informat Res Grp Parallel Comp A-1040 Vienna Austria Chalmers Univ Technol S-41296 Gothenburg Sweden

ISBN: (纸本)9781450319225

Work-stealing systems are typically oblivious to the nature of the tasks they are scheduling. they do not know or take into account how long a task will take to execute or how many subtasks it will spawn. Moreover, task execution order is typically determined by an underlying task storage data structure, and cannot be changed. there are thus possibilities for optimizing task parallel executions by providing information on specific tasks and their preferred execution order to the scheduling system. We investigate generalizations of work-stealing and introduce a framework enabling applications to dynamically provide hints on the nature of specific tasks using scheduling strategies. Strategies can be used to independently control both local task execution and steal order. Strategies allow optimizations on specific tasks, in contrast to more conventional scheduling policies that are typically global in scope. Strategies are composable and allow different, specific scheduling choices for different parts of an application simultaneously. We have implemented a work-stealing system based on our strategy framework. A series of benchmarks demonstrates beneficial effects that can be achieved with scheduling strategies.

关键词： Algorithms Work-stealing scheduler hints strategies priorities

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POSTER: RadiK: Scalable Radix Top-K Selection on GPUs 24

POSTER: RadiK: Scalable Radix Top-K Selection on GPUs

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29th acm sigplan Annual symposium on principles and practice of parallel programming (PPoPP)

作者： Li, Yifei Zhou, Bole Zhang, Jiejing Wei, Xuechao Li, Yinghan Chen, Yingda Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798400704352

By identifying the.. largest or smallest elements in a set of data, top-k selection is critical for modern high-performance databases and machine learning systems, especially with large data volumes. However, previous studies on its GPU implementation are mostly merge-based and rely heavily on the high-speed but size-limited on-chip memory, thereby resulting in a restricted upper bound on... this paper introduces RadiK, a highly optimized GPU-parallel radix top-k selection that is scalable with.., input length, and batch size. With a carefully designed optimization framework targeting high memory bandwidth and resource utilization, RadiK supports far larger.. than the prior art, achieving up to 2.5x speedup for non-batch queries and up to 4.8x speedup for batch queries. We also propose a lightweight refinement that strengthens the robustness of RadiK against skewed distributions by adaptively scaling the input elements.

关键词： Top-K Radix Select GPU-parallel Algorithm

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Model and compilation strategy for out-of-core data parallel programs

Model and compilation strategy for out-of-core data parallel...

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

作者： Bordawekar, Rajesh Choudhary, Alok Kennedy, Ken Koelbel, Charles Paleczny, Michael Syracuse Univ Syracuse United States

It is widely acknowledged in high-performance computing circles that parallel input/output needs substantial improvement in order to make scalable computers truly usable. We present a data storage model that allows processors independent access to their own data and a corresponding compilation strategy that integrates data-parallel computation with data distribution for out-of-core problems. Our results compare several communication methods and I/O optimizations using two out-of-core problems, Jacobi iteration and LU factorization.

关键词： parallel processing systems

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Lock-free channels for programming via communicating sequential processes 19

Lock-free channels for programming via communicating sequent...

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24th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2019

作者： Koval, Nikita Alistarh, Dan Elizarov, Roman IST Austria Austria JetBrains Austria

ISBN: (纸本)9781450362252

Traditional concurrent programming involves manipulating shared mutable state. Alternatives to this programming style are communicating sequential processes (CSP) [1] and actor [2] models, which share data via explicit communication. Rendezvous channel is the common abstraction for communication between several processes, where senders and receivers perform a rendezvous handshake as a part of their protocol (senders wait for receivers and vice versa). Additionally to this, channels support the select expression. In this work, we present the first eficient lock-free channel algorithm, and compare it against Go [3] and Kotlin [4] baseline implementations. © 2019 Copyright held by the owner/author(s).

关键词： Locks (fasteners)

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Shared-memory performance profiling 97

Shared-memory performance profiling

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

作者： Xu, Zhichen Larus, James R. Miller, Barton P. Univ of Wisconsin Madison WI United States

ISBN: (纸本)9780897919067

this paper describes a new approach to finding performance bottlenecks in shared-memory parallel programs and its embodiment in the Paradyn parallel Performance Tools running with the Blizzard fine-grain distributed shared memory system. this approach exploits the underlying system's cache coherence protocol to detect data sharing patterns that indicate potential performance bottlenecks and presents performance measurements in a data-centric manner. As a demonstration, Paradyn helped us improve the performance of a new shared-memory application program by a factor of four.

关键词： parallel processing systems

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Is Transactional programming Actually Easier?

Is Transactional Programming Actually Easier?

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

作者： Rossbach, Christopher J. Hofmann, Owen S. Witchel, Emmett Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9781605587080

Chip multi-processors (CMPs) have become ubiquitous, while tools that ease concurrent programming have not. the promise of increased performance for all applications through ever more parallel hardware requires good tools for concurrent programming, especially for average programmers. Transactional memory (TM) has enjoyed recent interest as a tool that can help programmers program concurrently. the transactional memory (TM) research community is heavily invested in the claim that programming with transactional memory is easier than alternatives (like locks), but evidence for or against the veracity of this claim is scant. In this paper, we describe a user-study in which 237 undergraduate students in an operating systems course implement the same programs using coarse and fine-grain locks, monitors, and transactions. We surveyed the students after the assignment, and examined their code to determine the types and frequency of programming errors for each synchronization technique. Inexperienced programmers found baroque syntax a barrier to entry for transactional programming. On average, subjective evaluation showed that students found transactions harder to use than coarse-grain locks, but slightly easier to use than fine-grained locks. Detailed examination of synchronization errors in the students' code tells a rather different story. Overwhelmingly, the number and types of programming errors the students made was much lower for transactions than for locks. On a similar programming problem, over 70% of students made errors with fine-grained locking, while less than 10% made errors with transactions.

关键词： Design Performance Transactional Memory Optimistic Concurrency Synchronization

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