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检索条件"任意字段=Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming"

共 328 条记录，以下是21-30 订阅

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GraphCube: Interconnection Hierarchy-aware Graph Processing 24

GraphCube: Interconnection Hierarchy-aware Graph Processing

引用

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

作者： Gan, Xinbiao Wu, Guang Qiu, Shenghao Xiong, Feng Si, Jiaqi Fang, Jianbin Dong, Dezun Gong, Chunye Li, Tiejun Wang, Zheng NUDT Beijing Peoples R China Univ Leeds Leeds W Yorkshire England Natl Supercomputer Ctr Tianjin Peoples R China

ISBN: (纸本)9798400704352

Processing large-scale graphs with billions to trillions of edges requires efficiently utilizing parallel systems. However, current graph processing engines do not scale well beyond a few tens of computing nodes because they are oblivious to the communication cost variations across the interconnection hierarchy. We introduce GraphCube, a better approach to optimizing graph processing on large-scale parallel systems with complex interconnections. GraphCube features a new graph partitioning approach to achieve better load balancing and minimize communication overhead across multiple levels of the interconnection hierarchy. We evaluate GraphCube by applying it to fundamental graph operations performed on synthetic and real-world graph datasets. Our evaluation used up to 79,024 computing nodes and 1.2+ million processor cores. Our large-scale experiments show that GraphCube outperforms state-of-the-art parallel graph processing methods in throughput and scalability. Furthermore, GraphCube outperformed the top-ranked systems on the Graph 500 list.

关键词： Graph processing Graph partitioning parallel computing Vectorization Graph500

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CPMA: An Efficient Batch-parallel Compressed Set Without Pointers 24

CPMA: An Efficient Batch-Parallel Compressed Set Without Poi...

引用

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

作者： Wheatman, Brian Burns, Randal Buluc, Aydin Xu, Helen Johns Hopkins Univ Baltimore MD 21218 USA Lawrence Berkeley Natl Lab Lawrence KS USA Georgia Inst Technol Atlanta GA USA

ISBN: (纸本)9798400704352

this paper introduces the batch-parallel Compressed Packed Memory Array (CPMA), a compressed, dynamic, ordered set data structure based on the Packed Memory Array (PMA). Traditionally, batch-parallel sets are built on pointerbased data structures such as trees because pointer-based structures enable fast parallel unions via pointer manipulation. Whencompared with cache-optimized trees, PMAswere slower to update but faster to scan. the batch-parallel CPMA overcomes this tradeoff between updates and scans by optimizing for cache-friendliness. On average, the CPMA achieves 3x faster batch-insert throughput and 4x faster range-query throughput compared with compressed PaC-trees, a state-of-the-art batch-parallel set library based on cache-optimized trees. We further evaluate the CPMA compared with compressed PaC-trees and Aspen, a state-of-the-art system, on a realworld application of dynamic-graph processing. the CPMA is on average 1.2x faster on a suite of graph algorithms and 2x faster on batch inserts when compared with compressed PaC-trees. Furthermore, the CPMA is on average 1.3x faster on graph algorithms and 2x faster on batch inserts compared with Aspen.

关键词： packed memory array batch-parallel compression data structures dynamic graphs

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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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ParlayANN: Scalable and Deterministic parallel Graph-Based Approximate Nearest Neighbor Search Algorithms 24

ParlayANN: Scalable and Deterministic Parallel Graph-Based A...

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

作者： Manohar, Magdalen Dobson Shen, Zheqi Blelloch, Guy E. Dhulipala, Laxman Gu, Yan Simhadri, Harsha Vardhan Sun, Yihan Carnegie Mellon Univ Pittsburgh PA 15213 USA UC Riverside Riverside CA USA Univ Maryland Baltimore MD USA Microsoft Res Redmond WA USA

ISBN: (纸本)9798400704352

Approximate nearest-neighbor search (ANNS) algorithms are a key part of the modern deep learning stack due to enabling efficient similarity search over high-dimensional vector space representations (i.e., embeddings) of data. Among various ANNS algorithms, graph-based algorithms are known to achieve the best throughput-recall tradeoffs. Despite the large scale of modern ANNS datasets, existing parallel graphbased implementations suffer from significant challenges to scale to large datasets due to heavy use of locks and other sequential bottlenecks, which 1) prevents them from efficiently scaling to a large number of processors, and 2) results in nondeterminism that is undesirable in certain applications. In this paper, we introduce ParlayANN, a library of deterministic and parallel graph-based approximate nearest neighbor search algorithms, along with a set of useful tools for developing such algorithms. In this library, we develop novel parallel implementations for four state-of-the-art graph-based ANNS algorithms that scale to billion-scale datasets. Our algorithms are deterministic and achieve high scalability across a diverse set of challenging datasets. In addition to the new algorithmic ideas, we also conduct a detailed experimental study of our new algorithms as well as two existing non-graph approaches. Our experimental results both validate the effectiveness of our new techniques, and lead to a comprehensive comparison among ANNS algorithms on large scale datasets with a list of interesting findings.

关键词： nearest neighbor search vector search parallel algorithms

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AGAthA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping 24

AGAThA: Fast and Efficient GPU Acceleration of Guided Sequen...

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

作者： Park, Seongyeon Hong, Junguk Song, Jaeyong Kim, Hajin Kim, Youngsok Lee, Jinho Seoul Natl Univ Seoul South Korea Yonsei Univ Seoul South Korea

ISBN: (纸本)9798400704352

With the advance in genome sequencing technology, the lengths of deoxyribonucleic acid (DNA) sequencing results are rapidly increasing at lower prices than ever. However, the longer lengths come at the cost of a heavy computational burden on aligning them. For example, aligning sequences to a human reference genome can take tens or even hundreds of hours. the current de facto standard approach for alignment is based on the guided dynamic programming method. Although this takes a long time and could potentially benefit from high-throughput graphic processing units (GPUs), the existing GPU-accelerated approaches often compromise the algorithm's structure, due to the GPU-unfriendly nature of the computational pattern. Unfortunately, such compromise in the algorithm is not tolerable in the field, because sequence alignment is a part of complicated bioinformatics analysis pipelines. In such circumstances, we propose AGAthA, an exact and efficient GPU-based acceleration of guided sequence alignment. We diagnose and address the problems of the algorithm being unfriendly to GPUs, which comprises strided/redundant memory accesses and workload imbalances that are difficult to predict. According to the experiments on modern GPUs, AGAthA achieves 18.8x speedup against the CPU-based baseline, 9.6x against the best GPU-based baseline, and 3.6x against GPU-based algorithms with different heuristics.

关键词： GPU Acceleration Genome Sequence Alignment Long Reads Dynamic programming

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POSTER: Fast parallel Exact Inference on Bayesian Networks 28

POSTER: Fast Parallel Exact Inference on Bayesian Networks

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

作者： Jiang, Jiantong Wen, Zeyi Mansoor, Atif Mian, Ajmal The University of Western Australia Australia Hong Kong University of Science and Technology Guangzhou China

ISBN: (纸本)9798400700156

Bayesian networks (BNs) are attractive, because they are graphical and interpretable machine learning models. However, exact inference on BNs is time-consuming, especially for complex problems. To improve the efficiency, we propose a fast BN exact inference solution named Fast-BNI on multi-core CPUs. Fast-BNI enhances the efficiency of exact inference through hybrid parallelism that tightly integrates coarse- and fine-grained parallelism. We also propose techniques to further simplify the bottleneck operations of BN exact inference. Fast-BNI source code is freely available at https://***/jjiantong/FastBN. © 2023 Owner/Author.

关键词： Bayesian networks

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PiPAD: Pipelined and parallel Dynamic GNN Training on GPUs 23

PiPAD: Pipelined and Parallel Dynamic GNN Training on GPUs

引用

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

作者： Wang, Chunyang Sun, Desen Bai, Yuebin Beihang University Beijing China

ISBN: (纸本)9798400700156

Dynamic Graph Neural Networks (DGNNs) have been widely applied in various real-life applications, such as link prediction and pandemic forecast, to capture both static structural information and temporal characteristics from dynamic graphs. Combining both time-dependent and -independent components, DGNNs manifest substantial parallel computation and data reuse potentials, but suffer from severe memory access inefficiency and data transfer overhead under the canonical one-graph-at-a-time training pattern. To tackle these challenges, we propose PiPAD, a Pipelined and parallel DGNN training framework for the end-to-end performance optimization on GPUs. From both algorithm and runtime level, PiPAD holistically reconstructs the overall training paradigm from the data organization to computation manner. Capable of processing multiple graph snapshots in parallel, PiPAD eliminates unnecessary data transmission and alleviates memory access inefficiency to improve the overall performance. Our evaluation across various datasets shows PiPAD achieves 1.22 × - 9.57× speedup over the state-of-the-art DGNN frameworks on three representative models. © 2023 acm.

关键词： Memory architecture

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POSTER: Stream-K:Work-centric parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU 28

POSTER: Stream-K:Work-centric Parallel Decomposition for Den...

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

作者： Osama, Muhammad Merrill, Duane Cecka, Cris Garland, Michael Owens, John D. University of California Davis United States NVIDIA Corporation

ISBN: (纸本)9798400700156

We introduce Stream-K, a work-centric parallelization of matrix multiplication (GEMM) and related computations in dense linear algebra. Whereas contemporary decompositions are primarily tile-based, our method operates by partitioning an even share of the aggregate inner loop iterations among physical processing elements. this provides a near-perfect utilization of computing resources, regardless of how efficiently the output tiling for any given problem quantizes across the underlying processing elements. © 2023 Owner/Author.

关键词： Graphics processing unit

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POSTER: ParGeo: A Library for parallel Computational Geometry 27

POSTER: ParGeo: A Library for Parallel Computational Geometr...

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27th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Wang, Yiqiu Yu, Shangdi Dhulipala, Laxman Gu, Yan Shun, Julian IMIT CSAIL Riverside CA USA

We present PARGEO, a multicore library for computational geometry algorithms. We describe two of the algorithms from PARGEO, convex hull and the smallest enclosing ball, and present a short evaluation of all implement... 详细信息

ISBN: (纸本)9781450392044

关键词： Computational geometry

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Training one DeePMD Model in Minutes: a Step towards Online Learning 24

Training one DeePMD Model in Minutes: a Step towards Online ...

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

作者： Hu, Siyu Zhao, Tong Sha, Qiuchen Li, Enji Meng, Xiangyu Liu, Liping Wang, Lin-Wang Tan, Guangming Jia, Weile Chinese Acad Sci Inst Comp Technol State Key Lab Proc Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China China Univ Petr Qingdao Inst Software Coll Comp Sci & Technol Qingdao Peoples R China Chinese Acad Sci Inst Semicond Beijing Peoples R China

ISBN: (纸本)9798400704352

Neural Network Molecular Dynamics (NNMD) has become a major approach in material simulations, which can speedup the molecular dynamics (MD) simulation for thousands of times, while maintaining ab initio accuracy, thus has a potential to fundamentally change the paradigm of material simulations. However, there are two time-consuming bottlenecks of the NNMD developments. One is the data access of ab initio calculation results. the other, which is the focus of the current work, is reducing the training time of NNMD model. the training of NNMD model is different from most other neural network training because the atomic force (which is related to the gradient of the network) is an important physical property to be fit. Tests show the traditional stochastic gradient methods, like the Adam algorithms, cannot efficiently deploy the multisample minibatch algorithm. As a result, a typical training (taking the Deep Potential Molecular Dynamics (DeePMD) as an example) can take many hours. In this work, we designed a heuristic minibatch quasi-Newtonian optimizer based on Extended Kalman Filter method. An early reduction of gradient and error is adopted to reduce memory footprint and communication. the memory footprint, communication and settings of hyper-parameters of this new method are analyzed in detail. Computational innovations such as customized kernels of the symmetry-preserving descriptor are applied to exploit the computing power of the heterogeneous architecture. Experiments are performed on 8 different datasets representing different real case situations, and numerical results show that our new method has an average speedup of 32.2 compared to the Reorganized Layer-wised Extended Kalman Filter with 1 GPU, reducing the absolute training time of one DeePMD model from hours to several minutes, making it one step toward online training.

关键词： parallel training Molecular dynamics First principle ab initio GPU

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