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

作者： Mueller, Frank Byrd, Greg Dreher, Patrick North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9781450362252

this tutorial provides a hands-on introduction to quantum computing. It will feature the three pillars, architectures, programming, and algorithms/applications of quantum computing. Its focus is on the applicability of problems to quantum computing from a practical point, with only the necessary foundational coverage of the physics and theoretical aspects to understand quantum computing. Simulation software will be utilized complemented by access to actual quantum computers to prototype problem solutions. this should develop a better understanding of how problems are transformed into quantum algorithms and what programming language support is best suited for a given application area. As a first of its kind, to the best of our knowledge, the tutorial includes hands-on programming experience with IBM Q and D-Wave hardware.

关键词： quantum computing

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S-EnKF: Co-designing for Scalable Ensemble Kalman Filter 19

S-EnKF: Co-designing for Scalable Ensemble Kalman Filter

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

作者： Xiao, Junmin Wang, Shijie Wan, Weiqiang Hong, Xuehai Tan, Guangming Univ Chinese Acad Sci Inst Comp Technol State Key Lab Comp Architecture Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781450362252

Ensemble Kalman filter (EnKF) is one of the most important methods for data assimilation, which is widely applied to the reconstruction of observed historical data for providing initial conditions of numerical atmospheric and oceanic models. With the improvement of data resolution and the increase in the amount of model data, the scalability of recent parallel implementations suffers from high overhead on data transfer. In this paper, we propose, S-EnKF: a scalable and distributed EnKF adaptation for modern clusters. With an in-depth analysis of new requirements brought forward by recent frameworks and limitations of current designs, we present a co-design of S-EnKF. For fully exploiting the resources available in modern parallel file systems, we design a concurrent access approach to accelerate the process of reading large amounts of background data. through a deeper investigation of the data dependence relations, we modify EnKF's workflow to maximize the overlap of file reading and local analysis with a new multi-stage computation approach. Furthermore, we push the envelope of performance further with aggressive co-design of auto-tuning through tradeoff between the benefit on runtime and the cost on processors based on classic cost models. the experimental evaluation of S-EnKF demonstrates nearly ideal strong scalability on up to 12,000 processors. the largest run sustains a performance of 3x-speedup compared with P-EnKF, which represents the state-of-art parallel implementation of EnKF.

关键词： ensemble Kalman filter parallel implementation domain localization data assimilation scalability

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Proactive Work Stealing for Futures 19

Proactive Work Stealing for Futures

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

作者： Singer, Kyle Xu, Yifan Lee, I-Ting Angelina Washington Univ St Louis MO USA

ISBN: (纸本)9781450362252

the use of futures provides a flexible way to express parallelism and can generate arbitrary dependences among parallel subcomputations. the additional flexibility that futures provide comes with a cost, however. When scheduled using classic work stealing, a program with futures, compared to a program that uses only fork-join parallelism, can incur a much higher number of "deviations," a metric for evaluating the performance of parallel executions. All prior works assume a parsimonious work-stealing scheduler, however, where a worker thread (surrogate of a processor) steals work only when its local deque becomes empty. In this work, we investigate an alternative scheduling approach, called ProWS, where the workers perform proactive work stealing when handling future operations. We show that ProWS, for programs that use futures, can provide provably efficient execution time and equal or better bounds on the number of deviations compared to classic parsimonious work stealing. Given a computation with T-1 work and T-infinity span, ProWS executes the computation on P processors in expected time O(T-1/P + T-infinity lg P), with an additional lg P overhead on the span term compared to the parsimonious variant. For structured use of futures, where each future is single touch with no race on the future handle, the algorithm incurs O(PT infinity 2) deviations, matching that of the parsimonious variant. For general use of futures, the algorithm incurs O(m(k)T(infinity) + PT infinity lg P) deviations, where m(k) is the maximum number of future touches that are logically parallel. Compared to the bound for the parsimonious variant, O(kT(infinity) + PT infinity), with k being the total number of touches in the entire computation, this bound is better assuming m(k) = Omega(P lg P) and is smaller than k, which holds true for all the benchmarks we examined.

关键词： parallel processing systems

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GPU initiated OpenSHMEM: correct and efficient intra-kernel networking for dGPUs 20

GPU initiated OpenSHMEM: correct and ef...

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Proceedings of the 25th acm sigplan symposium on principles and practice of parallel programming

作者： Khaled Hamidouche Michael LeBeane Advanced Micro Devices Inc.

ISBN: (纸本)9781450368186

Current state-of-the-art in GPU networking utilizes a host-centric, kernel-boundary communication model that reduces performance and increases code complexity. To address these concerns, recent works have explored performing network operations from within a GPU kernel itself. However, these approaches typically involve the CPU in the critical path, which leads to high latency and inefficient utilization of network and/or GPU *** this work, we introduce GPU Initiated OpenSHMEM (GIO), a new intra-kernel PGAS programming model and runtime that enables GPUs to communicate directly with a NIC without the intervention of the CPU. We accomplish this by exploring the GPU's coarse-grained memory model and correcting semantic mismatches when GPUs wish to directly interact with the network. GIO also reduces latency by relying on a novel template-based design to minimize the overhead of initiating a network operation. We illustrate that for structured applications like a Jacobi 2D stencil, GIO can improve application performance by up to 40% compared to traditional kernel-boundary networking. Furthermore, we demonstrate that on irregular applications like Sparse Triangular Solve (SpTS), GIO provides up to 44% improvement compared to existing intra-kernel networking schemes.

关键词： distributed programming models RDMA networks GPUs

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Sequential and parallel Improvements in a Concurrent Functional programming Language 18

Sequential and Parallel Improvements in a Concurrent Functio...

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

作者： Schmidt-Schauss, Manfred Sabel, David Dallmeyer, Nils Goethe Univ Frankfurt Frankfurt Germany

ISBN: (纸本)9781450364416

We propose a model for measuring the runtime of concurrent programs by the minimal number of evaluation steps. the focus of this paper are improvements, which are program transformations that improve this number in every context, where we distinguish between sequential and parallel improvements, for one or more processors, respectively. We apply the methods to CHF, a model of Concurrent Haskell extended by futures allowing declarative implementations of concurrent programs. the language CHF is a typed higher-order functional language with concurrent threads, monadic IO and MVars as synchronizing variables. We show that all deterministic reduction rules and several further useful program transformations are sequential and parallel improvements.

关键词： functional programming call-by-need evaluation parallel improvements

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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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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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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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Deadlock-free buffer configuration for stream computing

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2017年第5期31卷 441-450页

作者： Li, Peng Beard, Jonathan C. Buhler, Jeremy D. Washington Univ Dept Comp Sci & Engn St Louis MO 63130 USA

Stream computing is a popular paradigm for parallel and distributed computing, where compute nodes are connected by first-in first-out data channels. Each channel can be considered as a concatenation of several data buffers, including an output buffer for the sender and an input buffer for the receiver. the configuration of buffer sizes impacts the performance as well as the correctness of the application. In this article, we focus on application deadlocks that are caused by incorrect configuration of buffer sizes. We describe three types of deadlock in streaming applications, categorized by how they can be created. To avoid them, we first prove necessary and sufficient conditions for deadlock-free computations;then based on the theorems, we propose both compile-time and runtime solutions for deadlock avoidance.

关键词： Buffer configuration deadlock avoidance feedback channels parallel and distributed computing streaming computing

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