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检索条件"任意字段=25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2020"

共 39 条记录，以下是1-10 订阅

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ppopp 2020 - Proceedings of the 2020 25th acm sigplan symposium on principles and practice of parallel programming

PPoPP 2020 - Proceedings of the 2020 25th ACM SIGPLAN Sympos...

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25th acm sigplan symposium on principles and practice of parallel programming, ppopp 2020

the proceedings contain 46 papers. the topics discussed include: kite: efficient and available release consistency for the datacenter;Oak: a scalable off-heap allocated key-value map;optimizing batched Winograd convolution on GPUs;taming unbalanced training workloads in deep learning with partial collective operations;scalable top-K retrieval with Sparta;waveSZ: a hardware-algorithm co-design of efficient lossy compression for scientific data;scaling concurrent queues by using HTM to profit from failed atomic operations;a wait-free universal construction for large objects;using sample-based time series data for automated diagnosis of scalability losses in parallel programs;scaling out speculative execution of finite-state machines with parallel merge;and detecting and reproducing error-code propagation bugs in MPI implementations.

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ppopp 2020 - Proceedings of the 2020 25th acm sigplan symposium on principles and practice of parallel programming

PPoPP 2020 - Proceedings of the 2020 25th ACM SIGPLAN Sympos...

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8th International symposium on Highly-Efficient Accelerators and Reconfigurable Technologies, HEART 2017

ISBN: (纸本)9781450368186

the proceedings contain 46 papers. the topics discussed include: kite: efficient and available release consistency for the datacenter;oak: a scalable off-heap allocated key-value map;taming unbalanced training workloads in deep learning with partial collective operations;scalable top-k retrieval with sparta;waveSZ: a hardware-algorithm co-design of efficient lossy compression for scientific data;scaling concurrent queues by using HTM to profit from failed atomic operations;a wait-free universal construction for large objects;universal wait-free memory reclamation;and using sample-based time series data for automated diagnosis of scalability losses in parallel programs.

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ppopp 2020 - Proceedings of the 2020 25th acm sigplan symposium on principles and practice of parallel programming

PPoPP 2020 - Proceedings of the 2020 25th ACM SIGPLAN Sympos...

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Journal Track at 18th International Semantic Web Conference, JT@ISWC 2019

the proceedings contain 13 papers. the topics discussed include: a guided walk into link key candidate extraction with relational concept analysis;reflections on profiling and cataloguing the content of SPARQL endpoints using SPORTAL;reflections on: modeling linked open statistical data;reflections on: DCAT-AP representation of Czech national open data catalog and its impact;reflections on: deep learning for noise-tolerant RDFS reasoning;reflections on: finding melanoma drugs through a probabilistic knowledge graph;reflections on: knowledge graph fact prediction via knowledge-enriched tensor factorization;the semantic sensor network ontology, revamped;and reflections on: knowmore - knowledge base augmentation with structured web markup.

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POSTER: TensorMD: Molecular Dynamics Simulation with Ab Initio Accuracy of 50 Billion Atoms 30

POSTER: TensorMD: Molecular Dynamics Simulation with Ab Init...

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30th symposium on principles and practice of parallel programming

作者： Ouyang, Yucheng Liu, Yin Shang, Honghui Chen, Zhenchuan Shan, Jiahao Cui, Huimin Feng, Xiaobing Chen, Xin Gao, Xingyu Wang, Lifang Song, Haifeng Chen, Xin Lin, Rongfen Li, Fang Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Inst Appl Phys & Computat Math Beijing Peoples R China Natl Res Ctr Parallel Comp Engn & Technol Beijing Peoples R China

ISBN: (纸本)9798400714436

Molecular dynamics simulation emerges as an important area that HPC+AI helps to investigate the physical properties, with machine-learning interatomic potentials (MLIPs) being used. General-purpose machine-learning (ML) tools have been leveraged in MLIPs, but they are not perfectly matched with each other, since many optimization opportunities in MLIPs have been missed by ML tools. this inefficiency arises from the fact that HPC+AI applications work with far more computational complexity compared with pure AI scenarios. this paper has developed an MLIP, named TensorMD, independently from any ML tool. TensorMD has been evaluated on two supercomputers and scaled to 51.8 billion atoms, i.e., similar to 3x compared with state-of-the-art.

关键词： Machine Learning Interatomic Potentials ManyCore Processor GPU Molecular Dynamics

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Proceedings of the 1997 6th acm sigplan symposium on principles and practice of parallel programming

Proceedings of the 1997 6th ACM SIGPLAN Symposium on Princip...

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Proceedings of the 1997 6th acm sigplan symposium on principles and practice of parallel programming

the proceedings contains 25 papers. Topics discussed include data and task parallelism, irregular applications, coherence protocols, shared memory, compilers and performances issue.

关键词： parallel processing systems

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parallel and Distributed Bounded Model Checking of Multi-threaded Programs 20

Parallel and Distributed Bounded Model Checking of Multi-thr...

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

作者： Inverso, Omar Trubiani, Catia Gran Sasso Sci Inst Laquila Italy

ISBN: (纸本)9781450368186

We introduce a structure-aware parallel technique for context-bounded analysis of concurrent programs. the key intuition consists in decomposing the set of concurrent traces into symbolic subsets that are separately explored by multiple instances of the same decision procedure running in parallel. the decision procedures work on different partitions of the search space without cooperating, whence distribution follows effortlessly. Our experiments on a selection of complex multi-threaded programs show significant analysis speedups and scalability, and greater performance gains than with general-purpose parallel solvers.

关键词： Concurrency Multithreading Sequentialization Software Verification parallel Analysis Bounded Model Checking SAT

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PLUM: Static parallel Program Locality Analysis under Uniform Multiplexing 20

PLUM: Static Parallel Program Locality Analysis under Unifor...

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

作者： Liu, Fangzhou Chen, Dong Smith, Wesley Ding, Chen Univ Rochester Rochester NY 14627 USA Natl Univ Def Technol Changsha Peoples R China

ISBN: (纸本)9781450368186

Data movement has a significant impact on program performance. For multithread programs, this impact is amplified, since different threads often interfere with each other by competing for shared cache space. However, recent de facto locality metrics consider either sequential execution only, or derive locality for multithread programs in an inefficient way, i.e. exhaustive simulation. this paper presents PLUM, a compiler solution for timescale locality analysis for parallel programs. Experiments demonstrate that the prediction accuracy is 93.97% on average. PLUM is the first tool that analyzes data locality for parallel programs during compile time;in addition, it provides an approach for efficiently studying the representative interleaving pattern for parallel executions.

关键词： Static analysis Locality Multithread

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parallel Determinacy Race Detection for Futures 20

Parallel Determinacy Race Detection for Futures

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

作者： Xu, Yifan Singer, Kyle Lee, I-Ting Angelina Washington Univ St Louis St Louis MO 63130 USA

ISBN: (纸本)9781450368186

the use of futures can generate arbitrary dependences in the computation, making it difficult to detect races efficiently. Algorithms proposed by priorwork to detect races on programs with futures all have to execute the program sequentially. We propose F-Order, the first known parallel race detection algorithm that detects races on programs that use futures. Given a computation with work T-1 and span T-infinity, our algorithm detects races in time O((T-1 lg (k) over cap + k(2))/ P + T-infinity(k + lg r lg (k) over cap)) on P processors, where k is the number of future operations, r is the maximum number of readers per memory location, and (k) over cap is the maximum number of future operations done by a single future task, which is typically small. We have also implemented a prototype system based on the proposed algorithm and empirically demonstrates its practical efficiency and scalability.

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A parallel Sparse Tensor Benchmark Suite on CPUs and GPUs 20

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

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

作者： Li, Jiajia Lakshminarasimhan, Mahesh Wu, Xiaolong Li, Ang Olschanowsky, Catherine Barker, Kevin Pacific Northwest Natl Lab Richland WA 99352 USA Univ Utah Salt Lake City UT USA Purdue Univ W Lafayette IN 47907 USA Boise State Univ Boise ID 83725 USA

ISBN: (纸本)9781450368186

Tensor computations present significant performance challenges that impact a wide spectrum of applications. Efforts on improving the performance of tensor computations include exploring data layout, execution scheduling, and parallelism in common tensor kernels. this work presents a benchmark suite for arbitrary-order sparse tensor kernels using state-of-the-art tensor formats: coordinate (COO) and hierarchical coordinate (HiCOO). It demonstrates a set of reference tensor kernel implementations and some observations on Intel CPUs and NVIDIA GPUs. the full paper can be referred to at http://***/abs/2001.00660.

关键词： sparse tensors benchmark GPU roofline model

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XIndex: A Scalable Learned Index for Multicore Data Storage 20

XIndex: A Scalable Learned Index for Multicore Data Storage

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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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