Proc. VLDB Endow.最新文献_第5页

The FastLanes Compression Layout: Decoding >100 Billion Integers per Second with Scalar Code FastLanes压缩布局:用标量码每秒解码> 1000亿个整数

Proc. VLDB Endow. Pub Date : 2023-05-01 DOI: 10.14778/3598581.3598587

Azim Afroozeh, P. Boncz

{"title":"The FastLanes Compression Layout: Decoding >100 Billion Integers per Second with Scalar Code","authors":"Azim Afroozeh, P. Boncz","doi":"10.14778/3598581.3598587","DOIUrl":"https://doi.org/10.14778/3598581.3598587","url":null,"abstract":"\u0000 The open-source FastLanes project aims to improve big data formats, such as Parquet, ORC and columnar database formats, in multiple ways. In this paper, we significantly accelerate decoding of all common Light-Weight Compression (LWC) schemes: DICT, FOR, DELTA and RLE through better data-parallelism. We do so by re-designing the compression layout using two main ideas: (i) generalizing the\u0000 value interleaving\u0000 technique in the basic operation of bit-(un)packing by targeting a virtual 1024-bits SIMD register, (ii) reordering the tuples in all columns of a table in the same Unified Transposed Layout that puts tuple chunks in a common \"04261537\" order (explained in the paper); allowing for maximum independent work for all possible basic SIMD lane widths: 8, 16, 32, and 64 bits.\u0000 \u0000 We address the software development, maintenance and future-proofness challenges of increasing hardware diversity, by defining a virtual 1024-bits instruction set that consists of simple operators supported by all SIMD dialects; and also, importantly, by scalar code. The interleaved and tuple-reordered layout actually makes scalar decoding faster, extracting more data-parallelism from today's wide-issue CPUs. Importantly, the scalar version can be fully auto-vectorized by modern compilers, eliminating technical debt in software caused by platform-specific SIMD intrinsics.\u0000 Micro-benchmarks on Intel, AMD, Apple and AWS CPUs show that FastLanes accelerates decoding by factors (decoding >40 values per CPU cycle). FastLanes can make queries faster, as compressing the data reduces bandwidth needs, while decoding is almost free.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87752745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

TiQuE: Improving the Transactional Performance of Analytical Systems for True Hybrid Workloads TiQuE:为真正的混合工作负载提高分析系统的事务性能

Proc. VLDB Endow. Pub Date : 2023-05-01 DOI: 10.14778/3598581.3598598

Nuno Faria, J. Pereira, A. Alonso, R. Vilaça, Yunus Koning, N. Nes

引用次数: 0

SDPipe: A Semi-Decentralized Framework for Heterogeneity-aware Pipeline-parallel Training SDPipe:一个半分散的异构感知管道并行训练框架

Proc. VLDB Endow. Pub Date : 2023-05-01 DOI: 10.14778/3598581.3598604

Xupeng Miao, Yining Shi, Zhi Yang, Bin Cui, Zhihao Jia

{"title":"SDPipe: A Semi-Decentralized Framework for Heterogeneity-aware Pipeline-parallel Training","authors":"Xupeng Miao, Yining Shi, Zhi Yang, Bin Cui, Zhihao Jia","doi":"10.14778/3598581.3598604","DOIUrl":"https://doi.org/10.14778/3598581.3598604","url":null,"abstract":"\u0000 The increasing size of both deep learning models and training data necessitates the ability to scale out model training through pipeline-parallel training, which combines pipelined model parallelism and data parallelism. However, most of them assume an ideal homogeneous dedicated cluster. As for real cloud clusters, these approaches suffer from the intensive model synchronization overheads due to the dynamic environment heterogeneity. Such a huge challenge leaves the design in a dilemma: either the performance bottleneck of the central parameter server (PS) or severe performance degradation caused by stragglers for decentralized synchronization (like All-Reduce). This approach presents SDPipe, a new\u0000 semi-decentralized\u0000 framework to get the best of both worlds, achieving both high heterogeneity tolerance and convergence efficiency in pipeline-parallel training. To provide high performance, we decentralize the communication model synchronization, which accounts for the largest proportion of synchronization overhead. In contrast, we centralize the process of group scheduling, which is lightweight but needs a global view for better performance and convergence speed against heterogeneity. We show via a prototype implementation the significant advantage of SDPipe on performance and scalability, facing different environments.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76849676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

LRU-C: Parallelizing Database I/Os for Flash SSDs LRU-C:为Flash ssd并行化数据库I/ o

Proc. VLDB Endow. Pub Date : 2023-05-01 DOI: 10.14778/3598581.3598605

Bo-Hyun Lee, Mijin An, Sang-Won Lee

{"title":"LRU-C: Parallelizing Database I/Os for Flash SSDs","authors":"Bo-Hyun Lee, Mijin An, Sang-Won Lee","doi":"10.14778/3598581.3598605","DOIUrl":"https://doi.org/10.14778/3598581.3598605","url":null,"abstract":"\u0000 The conventional database buffer managers have two inherent sources of I/O serialization: read stall and mutex conflict. The serialized I/O makes storage and CPU under-utilized, limiting transaction throughput and latency. Such harm stands out on flash SSDs with asymmetric read-write speed and abundant I/O parallelism. To make database I/Os parallel and thus leverage the parallelism in flash SSDs, we propose a novel approach to database buffering, the\u0000 LRU-C\u0000 method. It introduces the LRU-C pointer that points to the\u0000 least-recently-used-clean\u0000 page in the LRU list. Upon a page miss, LRU-C selects the current LRU-clean page as a victim and adjusts the pointer to the next LRU-clean one in the LRU list. This way, LRU-C can avoid the I/O serialization of read stalls. The LRU-C pointer enables two further optimizations for higher I/O throughput:\u0000 dynamic-batch-write\u0000 and\u0000 parallel LRU-list manipulation.\u0000 The former allows the background flusher to write more dirty pages at a time, while the latter mitigates mutex-induced I/O serializations. Experiment results from running OLTP workloads using MySQL-based LRU-C prototype on flash SSDs show that it improves transaction throughput compared to the Vanilla MySQL and the state-of-the-art WAR solution by 3x and 1.52x, respectively, and also cuts the tail latency drastically. Though LRU-C might compromise the hit ratio slightly, its increased I/O throughput far offsets the reduced hit ratio.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90857233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Designing and Learning Piecewise Space-Filling Curves 分段空间填充曲线的设计与学习

Proc. VLDB Endow. Pub Date : 2023-05-01 DOI: 10.14778/3598581.3598589

Jiangneng Li, Zheng Wang, Gao Cong, Cheng Long, H. M. Kiah, Bin Cui

引用次数: 2

What Modern NVMe Storage Can Do, And How To Exploit It: High-Performance I/O for High-Performance Storage Engines 现代NVMe存储可以做什么，以及如何利用它:高性能存储引擎的高性能I/O

Proc. VLDB Endow. Pub Date : 2023-05-01 DOI: 10.14778/3598581.3598584

Gabriel Haas, Viktor Leis

引用次数: 1

Decoupled Graph Neural Networks for Large Dynamic Graphs 大型动态图解耦图神经网络

Proc. VLDB Endow. Pub Date : 2023-05-01 DOI: 10.48550/arXiv.2305.08273

Y. Zheng, Zhewei Wei, Jiajun Liu

{"title":"Decoupled Graph Neural Networks for Large Dynamic Graphs","authors":"Y. Zheng, Zhewei Wei, Jiajun Liu","doi":"10.48550/arXiv.2305.08273","DOIUrl":"https://doi.org/10.48550/arXiv.2305.08273","url":null,"abstract":"Real-world graphs, such as social networks, financial transactions, and recommendation systems, often demonstrate dynamic behavior. This phenomenon, known as graph stream, involves the dynamic changes of nodes and the emergence and disappearance of edges. To effectively capture both the structural and temporal aspects of these dynamic graphs, dynamic graph neural networks have been developed. However, existing methods are usually tailored to process either continuous-time or discrete-time dynamic graphs, and cannot be generalized from one to the other. In this paper, we propose a decoupled graph neural network for large dynamic graphs, including a unified dynamic propagation that supports efficient computation for both continuous and discrete dynamic graphs. Since graph structure-related computations are only performed during the propagation process, the prediction process for the downstream task can be trained separately without expensive graph computations, and therefore any sequence model can be plugged-in and used. As a result, our algorithm achieves exceptional scalability and expressiveness. We evaluate our algorithm on seven real-world datasets of both continuous-time and discrete-time dynamic graphs. The experimental results demonstrate that our algorithm achieves state-of-the-art performance in both kinds of dynamic graphs. Most notably, the scalability of our algorithm is well illustrated by its successful application to large graphs with up to over a billion temporal edges and over a hundred million nodes.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85071720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MiniGraph: Querying Big Graphs with a Single Machine MiniGraph:用单个机器查询大图

Proc. VLDB Endow. Pub Date : 2023-05-01 DOI: 10.14778/3598581.3598590

Xiaoke Zhu, Yang Liu, Shuhao Liu, W. Fan

引用次数: 0

Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs 时间图SIR-GN:时间图的高效结构表示学习

Proc. VLDB Endow. Pub Date : 2023-05-01 DOI: 10.14778/3598581.3598583

Janet Layne, Justin Carpenter, Edoardo Serra, Francesco Gullo

{"title":"Temporal SIR-GN: Efficient and Effective Structural Representation Learning for Temporal Graphs","authors":"Janet Layne, Justin Carpenter, Edoardo Serra, Francesco Gullo","doi":"10.14778/3598581.3598583","DOIUrl":"https://doi.org/10.14778/3598581.3598583","url":null,"abstract":"Node representation learning (NRL) generates numerical vectors (embeddings) for the nodes of a graph. Structural NRL specifically assigns similar node embeddings for those nodes that exhibit similar structural roles. This is in contrast with its proximity-based counterpart, wherein similarity between embeddings reflects spatial proximity among nodes. Structural NRL is useful for tasks such as node classification where nodes of the same class share structural roles, though there may exist a distant, or no path between them.\u0000 Athough structural NRL has been well-studied in static graphs, it has received limited attention in the temporal setting. Here, the embeddings are required to represent the evolution of nodes' structural roles over time. The existing methods are limited in terms of efficiency and effectiveness: they scale poorly to even moderate number of timestamps, or capture structural role only tangentially.\u0000 \u0000 In this work, we present a novel unsupervised approach to structural representation learning for temporal graphs that overcomes these limitations. For each node, our approach clusters then aggregates the embedding of a node's neighbors for each timestamp, followed by a further temporal aggregation of all timestamps. This is repeated for (at most)\u0000 d\u0000 iterations, so as to acquire information from the\u0000 d\u0000 -hop neighborhood of a node. Our approach takes linear time in the number of overall temporal edges, and possesses important theoretical properties that formally demonstrate its effectiveness.\u0000 \u0000 Extensive experiments on synthetic and real datasets show superior performance in node classification and regression tasks, and superior scalability of our approach to large graphs.","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74748120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Extract-Transform-Load for Video Streams 提取-转换-加载视频流

Proc. VLDB Endow. Pub Date : 2023-05-01 DOI: 10.14778/3598581.3598600

Ferdinand Kossmann, Ziniu Wu, Eugenie Lai, Nesime Tatbul, Lei Cao, Tim Kraska, S. Madden

{"title":"Extract-Transform-Load for Video Streams","authors":"Ferdinand Kossmann, Ziniu Wu, Eugenie Lai, Nesime Tatbul, Lei Cao, Tim Kraska, S. Madden","doi":"10.14778/3598581.3598600","DOIUrl":"https://doi.org/10.14778/3598581.3598600","url":null,"abstract":"\u0000 Social media, self-driving cars, and traffic cameras produce video streams at large scales and cheap cost. However, storing and querying video at such scales is prohibitively expensive. We propose to treat large-scale video analytics as a data warehousing problem: Video is a format that is easy to produce but needs to be transformed into an application-specific format that is easy to query. Analogously, we define the problem of Video Extract-Transform-Load (\u0000 V-ETL\u0000 ).\u0000 V-ETL\u0000 systems need to reduce the cost of running a user-defined\u0000 V-ETL\u0000 job while also giving throughput guarantees to keep up with the rate at which data is produced. We find that no current system sufficiently fulfills both needs and therefore propose\u0000 Skyscraper\u0000 , a system tailored to\u0000 V-ETL. Skyscraper\u0000 can execute arbitrary video ingestion pipelines and adaptively tunes them to reduce cost at minimal or no quality degradation, e.g., by adjusting sampling rates and resolutions to the ingested content.\u0000 Skyscraper\u0000 can hereby be provisioned with cheap on-premises compute and uses a combination of buffering and cloud bursting to deal with peaks in workload caused by expensive processing configurations. In our experiments, we find that\u0000 Skyscraper\u0000 significantly reduces the cost of\u0000 V-ETL\u0000 ingestion compared to adaptions of current SOTA systems, while at the same time giving robustness guarantees that these systems are lacking.\u0000","PeriodicalId":20467,"journal":{"name":"Proc. VLDB Endow.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77218679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0