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A collection-oriented programming model for performance portability 2015

A collection-oriented programming model for performance port...

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

作者： Muralidharan, Saurav Garland, Michael Catanzaro, Bryan Sidelnik, Albert Hall, Mary University of Utah Salt Lake CityUT United States NVIDIA Corporation Santa ClaraCA United States Baidu Inc. SunnyvaleCA United States

ISBN: (纸本)9781450332057

this paper describes Surge, a collection-oriented programming model that enables programmers to compose parallel computations using nested high-level data collections and operators. Surge exposes a code generation interface, decoupled from the core computation, that enables programmers and autotuners to easily generate multiple implementations of the same computation on various parallel architectures such as multi-core CPUs and GPUs. By decoupling computations from architecture-specific implementation, programmers can target multiple architectures more easily, and generate a search space that facilitates optimization and customization for specific architectures. We express in Surge four real-world benchmarks from domains such as sparse linear-algebra and machine learning and from the same performance-portable specification, generate OpenMP and CUDA C++ implementations. Surge generates efficient, scalable code which achieves up to 1.32x speedup over handcrafted, well-optimized CUDA code.

关键词： parallel architectures

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A framework for practical parallel fast matrix multiplication 2015

A framework for practical parallel fast matrix multiplicatio...

引用

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

作者： Benson, Austin R. Ballard, Grey Institute for Computational and Mathematical Engineering Stanford University StanfordCA United States Sandia National Laboratories LivermoreCA United States

ISBN: (纸本)9781450332057

Matrix multiplication is a fundamental computation in many scientific disciplines. In this paper, we show that novel fast matrix multiplication algorithms can significantly outperform vendor implementations of the classical algorithm and Strassen's fast algorithm on modest problem sizes and shapes. Furthermore, we show that the best choice of fast algorithm depends not only on the size of the matrices but also the shape. We develop a code generation tool to automatically implement multiple sequential and shared-memory parallel variants of each fast algorithm, including our novel parallelization scheme. this allows us to rapidly benchmark over 20 fast algorithms on several problem sizes. Furthermore, we discuss a number of practical implementation issues for these algorithms on shared-memory machines that can direct further research on making fast algorithms practical. Copyright 2015 acm.

关键词： Matrix algebra

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A programming model and runtime system for significance-aware energy-efficient computing 2015

A programming model and runtime system for significance-awar...

引用

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

作者： Vassiliadis, Vassilis Parasyris, Konstantinos Chalios, Charalambos Antonopoulos, Christos D. Lalis, Spyros Bellas, Nikolaos Vandierendonck, Hans Nikolopoulos, Dimitrios S. Electrical and Computer Eng. Dept. University of Thessaly Greece Greece Queen's University Belfast United Kingdom

ISBN: (纸本)9781450332057

We introduce a task-based programming model and runtime system that exploit the observation that not all parts of a program are equally significant for the accuracy of the end-result, in order to trade off the quality of program outputs for increased energy-efficiency. this is done in a structured and flexible way, allowing for easy exploitation of different points in the quality/energy space, without adversely affecting application performance. the runtime system can apply a number of different policies to decide whether it will execute less-significant tasks accurately or approximately. the experimental evaluation indicates that our system can achieve an energy reduction of up to 83% compared with a fully accurate execution and up to 35% compared with an approximate version employing loop perforation. At the same time, our approach always results in graceful quality degradation.

关键词： Energy efficiency

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Combining phase identification and statistic modeling for automated parallel benchmark generation 2015

Combining phase identification and statistic modeling for au...

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

作者： Jin, Ye Liu, Mingliang Ma, Xiaosong Liu, Qing Logan, Jeremy Podhorszki, Norbert Choi, Jong Youl Klasky, Scott NCSU United States QCRI Qatar Oak Ridge National Lab. United States

ISBN: (纸本)9781450332057

parallel application benchmarks are indispensable for evaluating/optimizing HPC software and hardware. However, it is very challenging and costly to obtain high-fidelity benchmarks reflecting the scale and complexity of state-of-the-art parallel applications. Hand-extracted synthetic benchmarks are time- and labor-intensive to create. Real applications themselves, while offering most accurate performance evaluation, are expensive to compile, port, reconfigure, and often plainly inaccessible due to security or ownership concerns. this work contributes APPRIME, a novel tool for trace-based automatic parallel benchmark generation. Taking as input standard communication-I/O traces of an application's execution, it couples accurate automatic phase identification with statistical regeneration of event parameters to create compact, portable, and to some degree reconfigurable parallel application benchmarks. Experiments with four NAS parallel Benchmarks (NPB) and three real scientific simulation codes confirm the fidelity of APPRIME benchmarks. they retain the original applications' performance characteristics, in particular the relative performance across platforms.

关键词： Benchmarking

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Barrier elision for production parallel programs 2015

Barrier elision for production parallel programs

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

作者： Chabbi, Milind Lavrijsen, Wim De Jong, Wibe Sen, Koushik Mellor-Crummey, John Iancu, Costin Rice University United States Lawrence Berkeley National Laboratory United States UC Berkeley United States

ISBN: (纸本)9781450332057

Large scientific code bases are often composed of several layers of runtime libraries, implemented in multiple programming languages. In such situation, programmers often choose conservative synchronization patterns leading to suboptimal performance. In this paper, we present context-sensitive dynamic optimizations that elide barriers redundant during the program execution. In our technique, we perform data race detection alongside the program to identify redundant barriers in their calling contexts;after an initial learning, we start eliding all future instances of barriers occurring in the same calling context. We present an automatic on-the-fly optimization and a multi-pass guided optimization. We apply our techniques to NWChem - a 6 million line computational chemistry code written in C/C++/Fortran that uses several runtime libraries such as Global Arrays, ComEx, DMAPP, and MPI. Our technique elides a surprisingly high fraction of barriers (as many as 63%) in production runs. this redundancy elimination translates to application speedups as high as 14% on 2048 cores. Our techniques also provided valuable insight about the application behavior, later used by NWChem developers. Overall, we demonstrate the value of holistic context-sensitive analyses that consider the domain science in conjunction with the associated runtime software stack. Copyright 2015 acm.

关键词： Computational chemistry

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Scalable and efficient implementation of 3d unstructured meshes computation: A case study on matrix assembly 2015

Scalable and efficient implementation of 3d unstructured mes...

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

作者： thébault, Loïc Petit, Eric Dinh, Quang PRISM University of Versailles France Dassault Aviation Saint-Cloud France

ISBN: (纸本)9781450332057

Exposing massive parallelism on 3D unstructured meshes computation with efficient load balancing and minimal synchronizations is challenging. Current approaches relying on domain decomposition and mesh coloring struggle to scale with the increasing number of cores per nodes, especially with new many-core processors. In this paper, we propose an hybrid approach using domain decomposition to exploit distributed memory parallelism, Divide-and-Conquer, D&C, to exploit shared memory parallelism and improve locality, and mesh coloring at core level to exploit vectors. It illustrates a new trade-off for many-cores between structuredness, memory locality, and vectorization. We evaluate our approach on the finite element matrix assembly of an industrial fluid dynamic code developed by Dassault Aviation. We compare our D&C approach to domain decomposition and to mesh coloring. D&C achieves a high parallel efficiency, a good data locality as well as an improved bandwidth usage. It competes on current nodes with the optimized pure MPI version with a minimum 10% speed-up. D&C shows an impressive 319x strong scaling on 512 cores (32 nodes) with only 2000 vertices per core. Finally, the Intel Xeon Phi version has a performance similar to 10 Intel E5-2665 Xeon Sandy Bridge cores and 95% parallel efficiency on the 60 physical cores. Running on 4 Xeon Phi (240 cores), D&C has 92% efficiency on the physical cores and performance similar to 33 Intel E5-2665 Xeon Sandy Bridge cores. Copyright 2015 acm.

关键词： Computational fluid dynamics

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A parallel algorithm for global states enumeration in concurrent systems 2015

A parallel algorithm for global states enumeration in concur...

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

作者： Chang, Yen-Jung Garg, Vijay K. Department of Electrical and Computer Engineering University of Texas Austin United States

ISBN: (纸本)9781450332057

Verifying the correctness of the executions of a concurrent program is difficult because of its nondeterministic behavior. One of the verification methods is predicate detection, which predicts whether the user specified condition (predicate) could become true in any global states of the program. the method is predictive because it generates inferred execution paths from the observed execution path and then checks the predicate on the global states of inferred paths. One important part of predicate detection is global states enumeration, which generates the global states on inferred paths. Cooper and Marzullo gave the first enumeration algorithm based on a breadth first strategy (BFS). Later, many algorithms have been proposed to improve space and time complexity. None of them, however, takes parallelism into consideration. In this paper, we present the first parallel and online algorithm, named ParaMount, for global state enumeration. Our experimental results show that ParaMount speeds up the existing sequential algorithms by a factor of 6 with 8 threads. We have implemented an online predicate detector using ParaMount. For predicate detection, our detector based on ParaMount is 10 to 50 times faster than RV runtime (a verification tool that uses Cooper and Marzullo's BFS enumeration algorithm). Copyright 2015 acm.

关键词： parallel algorithms

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Cache-oblivious wavefront: Improving parallelism of recursive dynamic programming algorithms without losing cache-efficiency 2015

Cache-oblivious wavefront: Improving parallelism of recursiv...

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

作者： Tang, Yuan You, Ronghui Kan, Haibin Tithi, Jesmin Jahan Ganapathi, Pramod Chowdhury, Rezaul A. Software School School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Department of Computer Science Stony Brook University Stony BrookNY11790 United States

ISBN: (纸本)9781450332057

State-of-the-art cache-oblivious parallel algorithms for dynamic programming (DP) problems usually guarantee asymptotically optimal cache performance without any tuning of cache parameters, but they often fail to exploit the theoretically best parallelism at the same time. While these algorithms achieve cache-optimality through the use of a recursive divide-and-conquer (DAC) strategy, scheduling tasks at the granularity of task dependency introduces artificial dependencies in addition to those arising from the defining recurrence equations. We removed the artificial dependency by scheduling tasks ready for execution as soon as all its real dependency constraints are satisfied, while preserving the cache-optimality by inheriting the DAC strategy. We applied our approach to a set of widely known dynamic programming problems, such as Floyd-Warshall's All-Pairs Shortest Paths, Stencil, and LCS. theoretical analyses show that our techniques improve the span of 2-way DAC-based Floyd Warshall's algorithm on an n node graph from Θ(nlog2 n) to Θ(n), stencil computations on a d-dimensional hypercubic grid of width w for h time steps from Θ ((d2h) wlog(d+2)-1) to Θ(h), and LCS on two sequences of length n each from Θ (nlog23) to Θ(n). In each case, the total work and cache complexity remain asymptotically optimal. Experimental measurements exhibit a 3-5 times improvement in absolute running time, 10-20 times improvement in burdened span by Cilkview, and approximately the same L1/L2 cache misses by PAPI. Copyright 2015 acm.

关键词： parallel algorithms

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SYNC or ASYNC: Time to fuse for distributed graph-parallel computation 2015

SYNC or ASYNC: Time to fuse for distributed graph-parallel c...

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

作者： Xie, Chenning Chen, Rong Guan, Haibing Zang, Binyu Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science Shanghai Jiao Tong University China

ISBN: (纸本)9781450332057

Large-scale graph-structured computation usually exhibits iterative and convergence-oriented computing nature, where input data is computed iteratively until a convergence condition is reached. Such features have led to the development of two different computation modes for graph-structured programs, namely synchronous (Sync) and asynchronous (Async) modes. Unfortunately, there is currently no in-depth study on their execution properties and thus programmers have to manually choose a mode, either requiring a deep understanding of underlying graph engines, or suffering from suboptimal performance. this paper makes the first comprehensive characterization on the performance of the two modes on a set of typical graph-parallel applications. Our study shows that the performance of the two modes varies significantly with different graph algorithms, partitioning methods, execution stages, input graphs and cluster scales, and no single mode consistently outperforms the other. To this end, this paper proposes Hsync, a hybrid graph computation mode that adaptively switches a graph-parallel program between the two modes for optimal performance. Hsync constantly collects execution statistics on-the-fly and leverages a set of heuristics to predict future performance and determine when a mode switch could be profitable. We have built online sampling and offline profiling approaches combined with a set of heuristics to accurately predicting future performance in the two modes. A prototype called PowerSwitch has been built based on PowerGraph, a state-of-the-art distributed graph-parallel system, to support adaptive execution of graph algorithms. On a 48-node EC2-like cluster, PowerSwitch consistently outperforms the best of both modes, with a speedup ranging from 9% to 73% due to timely switch between two modes. Copyright 2015 acm.

关键词： Graphic methods

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A hierarchical approach to reducing communication in parallel graph algorithms 2015

A hierarchical approach to reducing communication in paralle...

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

作者： Harshvardhan Amato, Nancy M. Rauchwerger, Lawrence Parasol Laboratory Department of Computer Science and Engineering Texas A and M University United States

ISBN: (纸本)9781450332057

Large-scale graph computing has become critical due to the ever-increasing size of data. However, distributed graph computations are limited in their scalability and performance due to the heavy communication inherent in such computations. this is exacerbated in scale-free networks, such as social and web graphs, which contain hub vertices that have large degrees and therefore send a large number of messages over the network. Furthermore, many graph algorithms and computations send the same data to each of the neighbors of a vertex. Our proposed approach recognizes this, and reduces communication performed by the algorithm without change to user-code, through a hierarchical machine model imposed upon the input graph. the hierarchical model takes advantage of locale information of the neighboring vertices to reduce communication, both in message volume and total number of bytes sent. It is also able to better exploit the machine hierarchy to further reduce the communication costs, by aggregating traffic between different levels of the machine hierarchy. Results of an implementation in the STAPL GL shows improved scalability and performance over the traditional level-synchronous approach, with 2.5 × - 8× improvement for a variety of graph algorithms at 12, 000+ cores.

关键词： Big data

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