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检索条件"任意字段=2003 ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming"

共 560 条记录，以下是121-130 订阅

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Juggler: A Dependence-Aware Task-Based Execution Framework for GPUs 18

Juggler: A Dependence-Aware Task-Based Execution Framework f...

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

作者： Belviranli, Mehmet E. Lee, Seyong Vetter, Jeffrey S. Bhuyan, Laxmi N. Oak Ridge Natl Lab Oak Ridge TN 37830 USA Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9781450349826

Scientific applications with single instruction, multiple data (SIMD) computations show considerable performance improvements when run on today's graphics processing units (GPUs). However, the existence of data dependences across thread blocks may significantly impact the speedup by requiring global synchronization across multiprocessors (SMs) inside the GPU. To efficiently run applications with inter-block data dependences, we need fine-granular task-based execution models that will treat SMs inside a GPU as stand-alone parallel processing units. Such a scheme will enable faster execution by utilizing all internal computation elements inside the GPU and eliminating unnecessary waits during device-wide global barriers. In this paper, we propose Juggler, a task-based execution scheme for GPU workloads with data dependences. The Juggler framework takes applications embedding OpenMP 4.5 tasks as input and executes them on the GPU via an efficient in-device runtime, hence eliminating the need for kernel-wide global synchronization. Juggler requires no or little modification to the source code, and once launched, the runtime entirely runs on the GPU without relying on the host through the entire execution. We have evaluated Juggler on an NVIDIA Tesla P100 GPU and obtained up to 31% performance improvement against global barrier based implementation, with minimal runtime overhead.

关键词： GP-GPU programming task-based execution data dependence OpenMP 4.5

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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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Interval-Based Memory Reclamation 18

Interval-Based Memory Reclamation

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

作者： Wen, Haosen Izraelevitz, Joseph Cai, Wentao Beadle, H. Alan Scott, Michael L. Univ Rochester Dept Comp Sci Rochester NY 14627 USA

ISBN: (纸本)9781450349826

In this paper we present interval-based reclamation (IBR), a new approach to safe reclamation of disconnected memory blocks in nonblocking concurrent data structures. Safe reclamation is a difficult problem: a thread, before freeing a block, must ensure that no other threads are accessing that block;the required synchronization tends to be expensive. In contrast with epoch-based reclamation, in which threads reserve all blocks created after a certain time, or pointer-based reclamation (e.g., hazard pointers), in which threads reserve individual blocks, IBR allows a thread to reserve all blocks known to have existed in a bounded interval of time. By comparing a thread's reserved interval with the lifetime of a detached but not yet reclaimed block, the system can determine if the block is safe to free. Like hazard pointers, IBR avoids the possibility that a single stalled thread may reserve an unbounded number of blocks;unlike hazard pointers, it avoids a memory fence on most pointer-following operations. It also avoids the need to explicitly "unreserve" a no-longer-needed pointer. We describe three specific IBR schemes (one with several variants) that trade off performance, applicability, and space requirements. IBR requires no special hardware or OS support. In experiments with data structure microbenchmarks, it also compares favorably (in both time and space) to other state-of-the-art approaches, making it an attractive alternative for libraries of concurrent data structures.

关键词： shared memory garbage collection parallel algorithms

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Proceedings of the acm sigplan symposium on principles and practice of parallel programming, PPOPP

Proceedings of the ACM SIGPLAN Symposium on Principles and P...

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

ISBN: (纸本)9781450332057

The proceedings contain 44 papers. The topics discussed include: predicate RCU: an RCU for scalable concurrent updates;automatic scalable atomicity via semantic locking;a framework for practical parallel fast matrix multiplication;PLUTO+: near-complete modeling of affine transformations for parallelism and locality;distributed memory code generation for mixed irregular/regular computations;performance implications of dynamic memory allocators on transactional memory systems;low-overhead software transactional memory with progress guarantees and strong semantics∗;barrier elision for production parallel programs;scalable and efficient implementation of 3D unstructured meshes computation: a case study on matrix assembly;and diagnosing the causes and severity of one-sided message contention.

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LazyGraph: Lazy Data Coherency for Replicas in Distributed Graph-parallel Computation 18

LazyGraph: Lazy Data Coherency for Replicas in Distributed G...

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

作者： Wang, Lei Zhuang, Liangji Chen, Junhang Cui, Huimin Lv, Fang Liu, Ying Fen, Xiaobing Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781450349826

Replicas of a vertex play an important role in existing distributed graph processing systems which make a single vertex to be parallel processed by multiple machines and access remote neighbors locally without any remote access. However, replicas of vertices introduce data coherency problem. Existing distributed graph systems treat replicas of a vertex v as an atomic and indivisible vertex, and use an eager data coherency approach to guarantee replicas atomicity. In eager data coherency approach, any changes to vertex data must be immediately communicated to all replicas of v, thus leading to frequent global synchronizations and communications. In this paper, we propose a lazy data coherency approach, called LazyAsync, which treats replicas of a vertex as independent vertices and maintains the data coherency by computations, rather than communications in existing eager approach. Our approach automatically selects some data coherency points from the graph algorithm, and maintains all replicas to share the same global view only at such points, which means the replicas are enabled to maintain different local views between any two adjacent data coherency points. Based on PowerGraph, we develop a distributed graph processing system LazyGraph to implement the LazyAsync approach and exploit graph-aware optimizations. On a 48-node EC2-like cluster, LazyGraph outperforms PowerGraph on four widely used graph algorithms across a variety of real-world graphs, with a speedup ranging from 1.25x to 10.69x.

关键词： Lazy Data Coherency Distributed Graphparallel Computation Execution Model

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Certified Compilation of Financial Contracts 18

Certified Compilation of Financial Contracts

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20th International symposium on principles and practice of Declarative programming (PPDP)

作者： Annenkov, Danil Elsman, Martin INRIA GALLINETTE Res Ream Nantes France Univ Copenhagen Dept Comp Sci DIKU Copenhagen Denmark

ISBN: (纸本)9781450364416

We present an extension to a certified financial contract management system that allows for templated declarative financial contracts and for integration with financial stochastic models through verified compilation into so-called payoff-expressions. Such expressions readily allow for determining the value of a contract in a given evaluation context, such as contexts created for stochastic simulations. The templating mechanism is useful both at the contract specification level, for writing generic reusable contracts, and for reuse of code that, without the templating mechanism, needs to be recompiled for different evaluation contexts. We report on the effect of using the certified system in the context of a GPGPU-based Monte Carlo simulation engine for pricing various over-the-counter (OTC) financial contracts. The full contract-management system, including the payoff-language compilation, is verified in the Coq proof assistant and certified Haskell code is extracted from our Coq development along with Futhark code for use in a data-parallel pricing engine.

关键词： financial contracts contract languages domain-specific languages certified programming software correctness Coq

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Performance portable C++ programming with RAJA 19

Performance portable C++ programming with RAJA

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Proceedings of the 24th symposium on principles and practice of parallel programming

作者： David Beckingsale Richard Hornung Tom Scogland Arturo Vargas Lawrence Livermore National Laboratory

ISBN: (纸本)9781450362252

With the rapid change of computing architectures, and variety of programming models; the ability to develop performance portable applications has become of great importance. This is particularly true in large production codes where developing and maintaining hardware specific versions is *** simplify the development of performance portable code, we introduce RAJA, our C++ library that allows developers to write single-source applications that can target multiple hardware and programming model back-ends. We provide a thorough introduction to all of RAJA features, and walk through some hands-on examples that will allow attendees to understand how RAJA might benefit their own applications. Attendees should bring a laptop computer to participate in the hands-on *** tutorial will introduce attendees to RAJA, a C++ library for developing performance portable applications. Attendees will learn how to write performance portable code that can execute on a range of programming models (OpenMP, CUDA, Intel TBB, and HCC) and hardware (CPU, GPU, Xeon Phi).Specifically, attendees will learn how to convert existing C++ applications to use RAJA, and how to use RAJA's programming abstractions to expose existing parallelism in their applications without complex algorithm rewrites. We will also cover specific guidelines for using RAJA in a large application, including some common "gotchas" and how to handle memory management. Finally, attendees will learn how to categorize loops to allow for simple and systematic performance tuning on any architecture.

关键词： parallel programming

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An Architecture and programming Model for Accelerating parallel Commutative Computations via Privatization 22

An Architecture and Programming Model for Accelerating Paral...

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

作者： Balaji, Vignesh Tirumala, Dhruva Lucia, Brandon Carnegie Mellon Univ Pittsburgh PA 15213 USA

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Recovering Performance for Vector-based Machine Learning on Managed Runtime 22

Recovering Performance for Vector-based Machine Learning on ...

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

作者： Wu, Mingyu Guan, Haibing Zang, Binyu Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Key Lab Scalable Comp & Syst Shanghai Peoples R China

ISBN: (纸本)9781450344937

The explosive growth of data and the demand for learning useful information from such data have stimulated a crossdisciplinary area named big learning, which leverages a cluster of commodity machines to run machine learning applications on large datasets. Those applications are usually implemented on high-level programming frameworks [1-4], which are running atop managed runtimes. Since the applications usually involve many iterations of computation over input data, the frameworks will cache them for performance consideration. In this work, we investigate a set of vectorbased machine learning applications in Spark MLlib [3] and find that cached datasets have a strong impact on overall efficiency. Copyright is held by the Owner/Author(s).

关键词： Learning systems

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Synchronized-by-Default Concurrency for Shared-Memory Systems 17

Synchronized-by-Default Concurrency for Shared-Memory System...

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

作者： Battig, Martin Gross, Thomas R. Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781450344937

We explore a programming approach for concurrency that synchronizes all accesses to shared memory by default. Synchronization takes place by ensuring that all program code runs inside atomic sections even if the program code has external side effects. Threads are mapped to atomic sections that a programmer must explicitly split to increase concurrency. A naive implementation of this approach incurs a large amount of overhead. We show how to reduce this overhead to make the approach suitable for realistic application programs on existing hardware. We present an implementation technique based on a special-purpose software transactional memory system. To reduce the overhead, the technique exploits properties of managed, object-oriented programming languages as well as intraprocedural static analyses and uses field-level granularity locking in combination with transactional I/O to provide good scaling properties. We implemented the synchronized-by-default (SBD) approach for the Java language and evaluate its performance for six programs from the DaCapo benchmark suite. The evaluation shows that, compared to explicit synchronization, the SBD approach has an overhead between 0.4% and 102% depending on the benchmark and the number of threads, with a mean (geom.) of 23.9%.

关键词： Atomic blocks Concurrency parallel programming Synchronization Transactional memory

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