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检索条件"任意字段=Proceedings of the ninth ACM SIGPLAN symposium on Principles and practice of parallel programming"

共 564 条记录，以下是81-90 订阅

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XIndex: A Scalable Learned Index for Multicore Data Storage 20

XIndex: A Scalable Learned Index for Multicore Data Storage

引用

25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Tang, Chuzhe Wang, Youyun Dong, Zhiyuan Hu, Gansen Wang, Zhaoguo Wang, Minjie Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Scalable Comp & Syst Shanghai Peoples R China NYU Dept Comp Sci New York NY 10003 USA

ISBN: (纸本)9781450368186

We present XIndex, a concurrent ordered index designed for fast queries. Similar to a recent proposal of the learned index, XIndex uses learned models to optimize index efficiency. Comparing with the learned index, XIndex is able to effectively handle concurrent writes without affecting the query performance by leveraging fine-grained synchronization and a new compaction scheme, Two-Phase Compaction. Furthermore, XIndex adapts its structure according to runtime workload characteristics to support dynamic workload. We demonstrate the advantages of XIndex with both YCSB and TPC-C (KV), a TPC-C variant for key-value stores. XIndex achieves up to 3.2x and 4.4x performance improvement comparing with Masstree and Wormhole, respectively, on a 24-core machine, and it is open-sourced(1).

关键词： Compaction

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GPU Initiated OpenSHMEM: Correct and Eicient Intra-Kernel Networking for dGPUs 25

GPU Initiated OpenSHMEM: Correct and Eicient Intra-Kernel Ne...

引用

25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Hamidouche, Khaled LeBeane, Michael Adv Micro Devices Inc Santa Clara CA 95054 USA

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 ineicient utilization of network and/or GPU resources. In 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： GPUs Distributed programming models RDMA networks

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On the fly MHP Analysis 20

On the fly MHP Analysis

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25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Saha, Sonali Nandivada, V. Krishna IIT Madras Madras Tamil Nadu India

ISBN: (纸本)9781450368186

May-Happen-in-parallel (MHP) analysis forms the basis for many problems of program analysis and program understanding. MHP analysis can also be used by IDEs (integrateddevelopment-environments) to help programmers to refactor parallel-programs, identify racy programs, understand which parts of the program run in parallel, and so on. Since the code keeps changing in the IDE, re-computing the MHP information after every change can be an expensive affair. In this manuscript, we propose a novel scheme to perform incremental MHP analysis (on the fly) of programs written in task parallel languages like X10 to keep the MHP information up to date, in an IDE environment. The key insight of our proposed approach to maintain the MHP information up to date is that we need not rebuild (from scratch) every data structure related to MHP information, after each modification (addition or deletion of statements) in the source code. The idea is to reuse the old MHP information as much as possible and incrementally recompute the MHP information (of a small set of statements) which depends on the statement added/removed. We introduce two new algorithms that deal with addition and removal of parallel constructs like finish, async, atomic, and sequential constructs like loop, if, if-else and other sequential statements, on the fly. Our evaluation shows that our algorithms run much faster than the repeated invocations of the fastest known MHP analysis for X10 programs [Sankar et al. 2016].

关键词： Concurrent programs may happen in parallel analysis incremental analysis

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Practical parallel Hypergraph Algorithms 20

Practical Parallel Hypergraph Algorithms

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25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Shun, Julian MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9781450368186

While there has been significant work on parallel graph processing, there has been very surprisingly little work on high-performance hypergraph processing. This paper presents a collection of efficient parallel algorithms for hypergraph processing, including algorithms for betweenness centrality, maximal independent set, k-core decomposition, hyper-trees, hyperpaths, connected components, PageRank, and single-source shortest paths. For these problems, we either provide new parallel algorithms or more efficient implementations than prior work. Furthermore, our algorithms are theoretically-efficient in terms of work and depth. To implement our algorithms, we extend the Ligra graph processing framework to support hypergraphs, and our implementations benefit from graph optimizations including switching between sparse and dense traversals based on the frontier size, edge-aware parallelization, using buckets to prioritize processing of vertices, and compression. Our experiments on a 72-core machine and show that our algorithms obtain excellent parallel speedups, and are *** faster than algorithms in existing hypergraph processing frameworks.

关键词： parallel algorithms

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Scaling Out Speculative Execution of Finite-State Machines with parallel Merge 20

Scaling Out Speculative Execution of Finite-State Machines w...

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25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Xia, Yang Jiang, Peng Agrawal, Gagan Ohio State Univ Columbus OH 43210 USA Univ Iowa Iowa City IA USA

ISBN: (纸本)9781450368186

A finite-state machine (FSM) is a key component for many important applications, such as Huffman decoding, regular expression matching and HTML tokenization. Due to its inherent dependencies and unpredictable memory access pattern, FSM computations are considered to be extremely difficult to parallelize. As such, significant research efforts have been made to accelerate FSM computations. Although they achieve promising performance results on multi-core machines, these methods are not scalable for emerging many-core architectures such as the GPUs. Based on our experiments, we point out that the bottleneck of achieving scalability on GPUs is the sequential merge inherent to these methods. However, unlike the case for simple reduction loops, parallel merge implementations for FSM computations typically require runtime checks and re-executions, which can also impede performance. Based on these observations, we develop parallel merge techniques that select efficient runtime check implementations and avoids unnecessary re-executions. Further, based on GPU architectural features, we develop optimization techniques to improve performance. We evaluate our parallel merge implementations on a set of representative algorithms. Experimental results show that our parallel merge implementations are 2.02-6.74 times more efficient than corresponding sequential merge implementations and achieve better scalability on an Nvidia V100 GPU.

关键词： Finite-State Machines Speculation GPUs

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No Barrier in the Road: A Comprehensive Study and Optimization of ARM Barriers 20

No Barrier in the Road: A Comprehensive Study and Optimizati...

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25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Liu, Nian Zang, Binyu Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Key Lab Scalable Comp & Syst Shanghai Peoples R China

ISBN: (纸本)9781450368186

In this paper, we present the first comprehensive performance characterization and optimization of ARM barriers on both mobile and server platforms. We draw a set of observations through several abstracted models and validate them in scenarios where barriers are intensively used. We find that (1) order-preserving approaches without involving the bus significantly outperform other approaches, and (2) the tremendous overhead mostly comes from barriers strictly following remote memory references. Usually, such barriers are inserted when threads are exchanging data, and they are used to ensure the relative order between storing the data to a shared buffer and setting a flag to inform the receiver. Based on the observations, we propose a new mechanism, Pilot, to remove such barriers by leveraging the single-copy atomicity to piggyback the flag with the data. Applying Pilot only requires minor changes to applications and provides 10%-360% performance improvements in multiple benchmarks, which are close to the ideal performance without barriers.

关键词： barrier synchronization concurrency lock

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Using Sample-Based Time Series Data for Automated Diagnosis of Scalability Losses in parallel Programs 20

Using Sample-Based Time Series Data for Automated Diagnosis ...

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25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Wei, Lai Mellor-Crummey, John Rice Univ Dept Comp Sci Houston TX 77005 USA Rice Univ Houston TX USA Pony Ai Fremont CA USA

ISBN: (纸本)9781450368186

The performance of many parallel applications has failed to scale as fast as successive generations of hardware on which these applications execute. To understand the cause of scalability losses, experts use performance tools to monitor and analyze application behavior. Profiles generated by performance tools can usually indicate the presence of scalability losses while time series data are generally necessary to pinpoint the root causes of such losses. However, manual analysis of time series data can be difficult in executions with a large number of processes, long running times, and deep call chains. This paper describes an automated framework that analyzes sample-based time series data to diagnose scalability losses in parallel executions. The framework's automated diagnosis of scalability losses indicates their symptoms, severity, and causes. Two case studies illustrate the effectiveness of this framework. When compared to a tool that analyzes performance using instrumentation-based traces, our overhead for collecting sample-based time series is 1/28 in time and 1/1600 in space while our automated analysis takes 1/25 of the time.

关键词： Performance automated diagnosis scalability losses sample-based time series data

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Identifying Scalability Bottlenecks for Large-Scale parallel Programs with Graph Analysis 20

Identifying Scalability Bottlenecks for Large-Scale Parallel...

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25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Jin, Yuyang Wang, Haojie Tang, Xiongchao Hoefler, Torsten Liu, Xu Zhai, Jidong Tsinghua Univ Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland Coll William & Mary Williamsburg VA 23187 USA

ISBN: (纸本)9781450368186

Scaling a parallel program to modern supercomputers is challenging due to inter-process communication, code serialization, and resource contention. Performance analysis tools for finding such scaling bottlenecks either base on profiling or tracing. Profiling incurs lower overheads but does not capture detailed dependencies needed for root-cause analyses. Tracing collects all information at prohibitive overheads. In this work, we develop ScalAna that uses static analysis techniques to achieve the best of both worlds-it enables the analyzability of traces at a cost similar to profiling. We leverage compiler and runtime lightweight techniques to generate performance graph and perform graph analysis algorithm to detect the root cause of scaling issues. We evaluate ScalAna with real applications on the Tianhe-2 supercomputer. Results show that our approach can effectively locate the root cause of scalability bottlenecks for real applications and incur less than 6.38% overhead (1.89% on average) for up to 2,048 processes.

关键词： Profiling Tools Scaling Loss Analysis Performance Optimization

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Scalable Top-K Retrieval with Sparta 20

Scalable Top-K Retrieval with Sparta

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25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Sheffi, Gali Basin, Dmitry Bortnikov, Edward Carmel, David Keidar, Idit Technion Haifa Israel Yahoo Res Haifa Israel Amazon Haifa Israel

ISBN: (纸本)9781450368186

Many big data processing applications rely on a top-k retrieval building block, which selects (or approximates) the k highest-scoring data items based on an aggregation of features. In web search, for instance, a document's score is the sum of its scores for all query terms. Top-k retrieval is often used to sift through massive data and identify a smaller subset of it for further analysis. Because it filters out the bulk of the data, it often constitutes the main performance bottleneck. Beyond the rise in data sizes, today's data processing scenarios also increase the number of features contributing to the overall score. In web search, for example, verbose queries are becoming mainstream, while state-of-the-art algorithms fail to process long queries in real-time. We present Sparta, a practical parallel algorithm that exploits multi-core hardware for fast (approximate) top-k retrieval. Thanks to lightweight coordination and judicious context sharing among threads, Sparta scales both in the number of features and in the searched index size. In our web search case study on 50M documents, Sparta processes 12-term queries more than twice as fast as the state-of-the-art. On a tenfold bigger index, Sparta processes queries at the same speed, whereas the average latency of existing algorithms soars to be an order-of-magnitude larger than Sparta's.

关键词： parallel computing multi-threading performance information retrieval web search top-k search

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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...

引用

23rd acm sigplan symposium on principles and practice of parallel programming, PPoPP 2018

ISBN: (纸本)9781450349826

The proceedings contain 50 papers. The topics discussed include: interval-based memory reclamation;harnessing epoch-based reclamation for efficient range queries;a persistent lock-free queue for non-volatile memory;hierarchical memory management for mutable state;bridging the gap between deep learning and sparse matrix format selection;optimizing N-dimensional, winograd-based convolution for manycore CPUs;vSensor: leveraging fixed-workload snippets of programs for performance variance detection;featherlight on-the-fly false-sharing detection;communication-avoiding parallel minimum cuts and connected components;and an effective fusion and tile size model for optimizing image processing pipelines.

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