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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"

共 330 条记录，以下是211-220 订阅

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Efficient support for irregular applications on distributed-memory machines

Efficient support for irregular applications on distributed-...

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

作者： Mukherjee, Shubhendu S. Sharma, Shamik D. Hill, Mark D. Larus, James R. Rogers, Anne Saltz, Joel Univ of Wisconsin-Madison Madison United States

Irregular computation problems underlie many important scientific applications. Although these problems are computationally expensive, and so would seem appropriate for parallel machines, their irregular and unpredictable run-time behavior makes this type of parallel program difficult to write and adversely affects run-time performance. this paper explores three issues - partitioning, mutual exclusion, and data transfer - crucial to the efficient execution of irregular problems on distributed-memory machines. Unlike previous work, we studied the same programs running in three alternative systems on the same hardware base (a thinking Machines CM-5): the CHAOS irregular application library, Transparent Shared Memory (TSM), and extensible Shared Memory (XSM). CHAOS and XSM performed equivalently for all three applications. Both systems were somewhat (13%) to significantly faster (991%) than TSM.

关键词： parallel processing systems

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Provably Fast and Space-Efficient parallel Biconnectivity 23

Provably Fast and Space-Efficient Parallel Biconnectivity

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28th acm sigplan Annual symposium on principles and practice of parallel programming, PPoPP 2023

作者： Dong, Xiaojun Wang, Letong Gu, Yan Sun, Yihan UC Riverside

ISBN: (纸本)9798400700156

Computing biconnected components (BCC) of a graph is a fundamental graph problem. the canonical parallel BCC algorithm is the Tarjan-Vishkin algorithm, which has O(n + m) optimal work and polylogarithmic span on a graph with n vertices and m edges. However, Tarjan-Vishkin is not widely used in practice. We believe the reason is the space-inefficiency (it uses O(m) extra space). In practice, existing parallel implementations are based on breath-first search (BFS). Since BFS has span proportional to the diameter of the graph, existing parallel BCC implementations suffer from poor performance on large-diameter graphs and can be slower than the sequential algorithm on many real-world graphs. We propose the first p arallel b iconnectivity algorithm (FAST-BCC) that has optimal work, polylogarithmic span, and is space-efficient. Our algorithm creates a skeleton graph based on any spanning tree of the input graph. then we use the connectivity information of the skeleton to compute the biconnectivity of the original input. We carefully analyze the correctness of our algorithm, which is highly non-trivial. We implemented FAST-BCC and compared it with existing implementations, including GBBS, Slota and Madduri's algorithm, and the sequential Hopcroft-Tarjan algorithm. We tested them on a 96-core machine on 27 graphs with varying edge distributions. FAST-BCC is the fastest on all graphs. On average (geometric means), FAST-BCC is 3.1× faster than the best existing baseline on each graph. © 2023 Owner/Author.

关键词： Graphic methods

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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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Modernizing parallel code with pattern analysis 21

Modernizing parallel code with pattern analysis

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26th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2021

作者： Lozano, Roberto Castañeda Cole, Murray Franke, Björn University of Edinburgh School of Informatics Edinburgh United Kingdom

ISBN: (纸本)9781450382946

Fifty years of parallel programming has generated a substantial legacy parallel codebase, creating a new portability challenge: re-parallelizing already parallel code. Our solution exploits inherently portable parallel patterns, and addresses the challenge of identifying patternization opportunities in legacy parallel code via constraint matching on traced dynamic dataflow graphs. Notably, this makes the analysis source-independent and equally applicable to sequential and parallel legacy code. We identify various map and reduction patterns, including compositions, in Pthreads code. Experiments with the Starbench suite show that our analysis is effective (finding 86% of the patterns known in the literature), accurate (reporting actual patterns in 98% of the cases), and efficient (scaling linearly with the size of the execution traces). We re-express the found patterns via a parallel pattern library, making code freely portable across CPU/GPU systems and performing competitively with hand-tuned implementations at zero additional effort. © 2021 acm.

关键词： Pattern matching

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INFINEL: An efficient GPU-based processing method for unpredictable large output graph queries 24

INFINEL: An efficient GPU-based processing method for unpred...

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

作者： Park, Sungwoo Oh, Seyeon Kim, Min-Soo Korea Adv Inst Sci & Technol Seoul South Korea GraphAI Seoul South Korea

ISBN: (纸本)9798400704352

With the introduction of GPUs, which are specialized for iterative parallel computations, the execution of computationintensive graph queries using a GPU has seen significant performance improvements. However, due to the memory constraints of GPUs, there has been limited research on handling large-scale output graph queries with unpredictable output sizes on a GPU. Traditionally, two-phase methods have been used, where the query is re-executed after splitting it into sub-tasks while only considering the size of the output in a static manner. However, two-phase methods become highly inefficient when used with graph data with extreme skew, failing to maximize the GPU performance. this paper proposes INFINEL, which handles unpredictable large output graph queries in a one-phase method through chunk allocation per thread and kernel stop/restart methods. We also propose applicable optimization techniques due to the corresponding unique characteristics of operating with low time/space overhead and not heavily relying on the GPU output buffer size. through extensive experiments, we demonstrate that our one-phase method of INFINEL improves the performance by up to 31.5 times over the conventional twophase methods for triangle listing ULO query.

关键词： Graph query processing Large output parallel computing GPU

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Scheduling parallel Programs by Work Stealing with Private Deques 13

Scheduling Parallel Programs by Work Stealing with Private D...

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

作者： Acar, Umut A. Chargueraud, Arthur Rainey, Mike Carnegie Mellon Univ Dept Comp Sci Pittsburgh PA 15213 USA Univ Paris 11 CNRS LRI Orsay France

ISBN: (纸本)9781450319225

Work stealing has proven to be an effective method for scheduling parallel programs on multicore computers. To achieve high performance, work stealing distributes tasks between concurrent queues, called deques, which are assigned to each processor. Each processor operates on its deque locally except when performing load balancing via steals. Unfortunately, concurrent deques suffer from two limitations: 1) local deque operations require expensive memory fences in modern weak-memory architectures, 2) they can be very difficult to extend to support various optimizations and flexible forms of task distribution strategies needed many applications, e. g., those that do not fit nicely into the divide-and-conquer, nested data parallel paradigm. For these reasons, there has been a lot recent interest in implementations of work stealing with non-concurrent deques, where deques remain entirely private to each processor and load balancing is performed via message passing. Private deques eliminate the need for memory fences from local operations and enable the design and implementation of efficient techniques for reducing task-creation overheads and improving task distribution. these advantages, however, come at the cost of communication. It is not known whether work stealing with private deques enjoys the theoretical guarantees of concurrent deques and whether they can be effective in practice. In this paper, we propose two work-stealing algorithms with private deques and prove that the algorithms guarantee similar theoretical bounds as work stealing with concurrent deques. For the analysis, we use a probabilistic model and consider a new parameter, the branching depth of the computation. We present an implementation of the algorithm as a C++ library and show that it compares well to Cilk on a range of benchmarks. Since our approach relies on private deques, it enables implementing flexible task creation and distribution strategies. As a specific example, we show how to impleme

关键词： work stealing nested parallelism dynamic load balancing

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Scaling implicit parallelism via dynamic control replication 21

Scaling implicit parallelism via dynamic control replication

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26th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2021

作者： Bauer, Michael Lee, Wonchan Slaughter, Elliott Jia, Zhihao Di Renzo, Mario Papadakis, Manolis Shipman, Galen McCormick, Patrick Garland, Michael Aiken, Alex NVIDIA United States Slac National Accelerator Laboratory United States Carnegie Mellon University United States Sapienza University of Rome Italy Los Alamos National Laboratory United States Stanford University United States

ISBN: (纸本)9781450382946

We present dynamic control replication, a run-time program analysis that enables scalable execution of implicitly parallel programs on large machines through a distributed and efficient dynamic dependence analysis. Dynamic control replication distributes dependence analysis by executing multiple copies of an implicitly parallel program while ensuring that they still collectively behave as a single execution. By distributing and parallelizing the dependence analysis, dynamic control replication supports efficient, on-the-fly computation of dependences for programs with arbitrary control flow at scale. We describe an asymptotically scalable algorithm for implementing dynamic control replication that maintains the sequential semantics of implicitly parallel programs. An implementation of dynamic control replication in the Legion runtime delivers the same programmer productivity as writing in other implicitly parallel programming models, such as Dask or TensorFlow, while providing better performance (11.4X and 14.9X respectively in our experiments), and scalability to hundreds of nodes. We also show that dynamic control replication provides good absolute performance and scaling for HPC applications, competitive in many cases with explicitly parallel programming systems. © 2021 acm.

关键词： parallel programming

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Reducing false sharing on shared memory multiprocessors through compile time data transformations 95

Reducing false sharing on shared memory multiprocessors thro...

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

作者： Jeremiassen, Tor E. Eggers, Susan J. AT&T Bell Lab Murray Hill NJ United States

ISBN: (纸本)9780897917001

We have developed compiler algorithms that analyze explicitly parallel programs and restructure their shared data to reduce the number of false sharing misses. the algorithms analyze per-process shared data accesses, pinpoint the data structures that are susceptible to false sharing and choose an appropriate transformation to reduce it. the transformations either group data that is accessed by the same processor or separate individual data items that are shared. this paper evaluates that technique. We show through simulation that our analysis successfully identifies the data structures that are responsible for most false sharing misses, and then transforms them without unduly decreasing spatial locality. the reduction in false sharing positively impacts both execution time and program scalability when executed on a KSR2. Both factors combine to increase the maximum achievable speedup for all programs, more than doubling it for several. Despite being able to only approximate actual inter-processor memory accesses, the compiler-directed transformations always outperform programmer efforts to eliminate false sharing.

关键词： parallel processing systems

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Portable parallel Performance from Sequential, Productive, Embedded Domain-Specific Languages 12

Portable Parallel Performance from Sequential, Productive, E...

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

作者： Kamil, Shoaib Coetzee, Derrick Beamer, Scott Cook, Henry Gonina, Ekaterina Harper, Jonathan Morlan, Jeffrey Fox, Armando Univ Calif Berkeley Berkeley CA 94720 USA Mississippi State Univ Mississippi State MS USA

ISBN: (纸本)9781450311601

Domain-expert productivity programmers desire scalable application performance, but usually must rely on efficiency programmers who are experts in explicit parallel programming to achieve it. Since such programmers are rare, to maximize reuse of their work we propose encapsulating their strategies in mini-compilers for domain-specific embedded languages (DSELs) glued together by a common high-level host language familiar to productivity programmers. the nontrivial applications that use these DSELs perform up to 98% of peak attainable performance, and comparable to or better than existing hand-coded implementations. Our approach is unique in that each mini-compiler not only performs conventional compiler transformations and optimizations, but includes imperative procedural code that captures an efficiency expert's strategy for mapping a narrow domain onto a specific type of hardware. the result is source-and performance-portability for productivity programmers and parallel performance that rivals that of hand-coded efficiency-language implementations of the same applications. We describe a framework that supports our methodology and five implemented DSELs supporting common computation kernels. Our results demonstrate that for several interesting classes of problems, efficiency-level parallel performance can be achieved by packaging efficiency programmers' expertise in a reusable framework that is easy to use for both productivity programmers and efficiency programmers.

关键词： Design Languages Performance Asp SEJITS Python Domain-Specific Languages

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PHANTOM: Predicting Performance of parallel Applications on Large-Scale parallel Machines Using a Single Node

PHANTOM: Predicting Performance of Parallel Applications on ...

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

作者： Zhai, Jidong Chen, Wenguang Zheng, Weimin Tsinghua Univ Tsinghua Natl Lab Informat Sci & Technol Dept Comp Sci & Technol Beijing 100084 Peoples R China

ISBN: (纸本)9781605587080

For designers of large-scale parallel computers, it is greatly desired that performance of parallel applications can be predicted at the design phase. However, this is difficult because the execution time of parallel applications is determined by several factors, including sequential computation time in each process, communication time and their convolution. Despite previous efforts, it remains an open problem to estimate sequential computation time in each process accurately and efficiently for large-scale parallel applications on non-existing target machines. this paper proposes a novel approach to predict the sequential computation time accurately and efficiently. We assume that there is at least one node of the target platform but the whole target system need not be available. We make two main technical contributions. First, we employ deterministic replay techniques to execute any process of a parallel application on a single node at real speed. As a result, we can simply measure the real sequential computation time on a target node for each process one by one. Second, we observe that computation behavior of processes in parallel applications can be clustered into a few groups while processes in each group have similar computation behavior. this observation helps us reduce measurement time significantly because we only need to execute representative parallel processes instead of all of them. We have implemented a performance prediction framework, called PHANTOM, which integrates the above computation-time acquisition approach with a trace-driven network simulator. We validate our approach on several platforms. For ASCI Sweep3D, the error of our approach is less than 5% on 1024 processor cores. Compared to a recent regression-based prediction approach, PHANTOM presents better prediction accuracy across different platforms.

关键词： Performance Prediction parallel Application Deterministic Replay Trace-driven Simulation

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