Operating Systems Review (ACM)最新文献_第8页

Taking the Pulse of Financial Activities with Online Graph Processing 用在线图形处理把握金融活动的脉搏

Operating Systems Review (ACM) Pub Date : 2021-06-04 DOI: 10.1145/3469379.3469389

Xiaowei Zhu, Zhisong Fu, Zhenxuan Pan, Jin Jiang, Chuntao Hong, Yongchao Liu, Yang Fang, Wenguang Chen, Changhua He

引用次数: 3

A Deeper Dive into Pattern-Aware Subgraph Exploration with PEREGRINE 使用PEREGRINE深入研究模式感知子图探索

Operating Systems Review (ACM) Pub Date : 2021-06-04 DOI: 10.1145/3469379.3469381

Kasra Jamshidi, Keval Vora

{"title":"A Deeper Dive into Pattern-Aware Subgraph Exploration with PEREGRINE","authors":"Kasra Jamshidi, Keval Vora","doi":"10.1145/3469379.3469381","DOIUrl":"https://doi.org/10.1145/3469379.3469381","url":null,"abstract":"Graph mining workloads aim to extract structural properties of a graph by exploring its subgraph structures. PEREGRINE is a general-purpose graph mining system that provides a generic runtime to efficiently explore subgraph structures of interest and perform various graph mining analyses. It takes a 'pattern-aware' approach by incorporating a pattern-based programming model along with efficient pattern matching strategies. The programming model enables easier expression of complex graph mining use cases and enables PEREGRINE to extract the semantics of patterns. By analyzing the patterns, PEREGRINE generates efficient exploration plans which it uses to guide its subgraph exploration. In this paper, we present an in-depth view of the patternanalysis techniques powering the matching engine of PEREGRINE. Beyond the theoretical foundations from prior research, we expose opportunities based on how the exploration plans are evaluated, and develop key techniques for computation reuse, enumeration depth reduction, and branch elimination. Our experiments show the importance of patternawareness for scalable and performant graph mining where the presented new techniques speed up the performance by up to two orders of magnitude on top of the benefits achieved from the prior theoretical foundations that generate the initial exploration plans.","PeriodicalId":38935,"journal":{"name":"Operating Systems Review (ACM)","volume":"55 1","pages":"1 - 10"},"PeriodicalIF":0.0,"publicationDate":"2021-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3469379.3469381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43971497","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: 6

Towards Next-Generation Cybersecurity with Graph AI 用图形人工智能实现下一代网络安全

Operating Systems Review (ACM) Pub Date : 2021-06-04 DOI: 10.1145/3469379.3469386

Benjamin Bowman, H. H. Huang

引用次数: 9

GraphZero GraphZero

Operating Systems Review (ACM) Pub Date : 2021-06-02 DOI: 10.1145/3469379.3469383

Daniel Mawhirter, Sam Reinehr, Connor Holmes, Tongping Liu, Bo Wu

{"title":"GraphZero","authors":"Daniel Mawhirter, Sam Reinehr, Connor Holmes, Tongping Liu, Bo Wu","doi":"10.1145/3469379.3469383","DOIUrl":"https://doi.org/10.1145/3469379.3469383","url":null,"abstract":"Subgraph matching is a fundamental task in many applications which identifies all the embeddings of a query pattern in an input graph. Compilation-based subgraph matching systems generate specialized implementations for the provided patterns and often substantially outperform other systems. However, the generated code causes significant computation redundancy and the compilation process incurs too much overhead to be used online, both due to the inherent symmetry in the structure of the query pattern. In this paper, we propose an optimizing query compiler, named GraphZero, to completely address these limitations through symmetry breaking based on group theory. GraphZero implements three novel techniques. First, its schedule explorer efficiently prunes the schedule space without missing any high-performance schedule. Second, it automatically generates and enforces a set of restrictions to eliminate computation redundancy. Third, it generalizes orientation, a surprisingly effective optimization that was only used for clique patterns, to apply to arbitrary patterns. Evaluation on multiple query patterns shows that GraphZero outperforms two state-of-the-art compilation and non-compilation based systems by up to 40X and 2654X, respectively.","PeriodicalId":38935,"journal":{"name":"Operating Systems Review (ACM)","volume":"55 1","pages":"21 - 37"},"PeriodicalIF":0.0,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3469379.3469383","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49370241","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: 13

Predicting file lifetimes for data placement in multi-tiered storage systems for HPC 预测HPC多层存储系统中数据放置的文件生命周期

Operating Systems Review (ACM) Pub Date : 2021-06-02 DOI: 10.1145/3469379.3469392

Luis Thomas, Sebastien Gougeaud, S. Rubini, Philippe Deniel, Jalil Boukhobza

{"title":"Predicting file lifetimes for data placement in multi-tiered storage systems for HPC","authors":"Luis Thomas, Sebastien Gougeaud, S. Rubini, Philippe Deniel, Jalil Boukhobza","doi":"10.1145/3469379.3469392","DOIUrl":"https://doi.org/10.1145/3469379.3469392","url":null,"abstract":"The emergence of Exascale machines in HPC will have the foreseen consequence of putting more pressure on the storage systems in place, not only in terms of capacity but also bandwidth and latency. With limited budget we cannot imagine using only storage class memory, which leads to the use of a heterogeneous tiered storage hierarchy. In order to make the most efficient use of the high performance tier in this storage hierarchy, we need to be able to place user data on the right tier and at the right time. In this paper, we assume a 2-tier storage hierarchy with a high performance tier and a high capacity archival tier. Files are placed on the high performance tier at creation time and moved to capacity tier once their lifetime expires (that is once they are no more accessed). The main contribution of this paper lies in the design of a file lifetime prediction model solely based on its path based on the use of Convolutional Neural Network. Results show that our solution strikes a good trade-off between accuracy and under-estimation. Compared to previous work, our model made it possible to reach an accuracy close to previous work (around 98.60% compared to 98.84%) while reducing the underestimations by almost 10x to reach 2.21% (compared to 21.86%). The reduction in underestimations is crucial as it avoids misplacing files in the capacity tier while they are still in use.","PeriodicalId":38935,"journal":{"name":"Operating Systems Review (ACM)","volume":"55 1","pages":"99 - 107"},"PeriodicalIF":0.0,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3469379.3469392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64044506","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

Scalable Graph Neural Network Training 可伸缩图神经网络训练

Operating Systems Review (ACM) Pub Date : 2021-05-05 DOI: 10.1145/3469379.3469387

M. Serafini, Hui Guan

引用次数: 19

EZIOTracer EZIOTracer

Operating Systems Review (ACM) Pub Date : 2021-04-26 DOI: 10.1145/3469379.3469391

Mohammed Islam Naas, François Trahay, Alexis Colin, Pierre Olivier, S. Rubini, Frank Singhoff, Jalil Boukhobza

{"title":"EZIOTracer","authors":"Mohammed Islam Naas, François Trahay, Alexis Colin, Pierre Olivier, S. Rubini, Frank Singhoff, Jalil Boukhobza","doi":"10.1145/3469379.3469391","DOIUrl":"https://doi.org/10.1145/3469379.3469391","url":null,"abstract":"Tracing is a popular method for evaluating, investigating, and modeling the performance of today's storage systems. Tracing has become crucial with the increase in complexity of modern storage applications/systems, that are manipulating an ever-increasing amount of data and are subject to extreme performance requirements. There exists many tracing tools focusing either on the user-level or the kernel-level, however we observe the lack of a unified tracer targeting both levels: this prevents a comprehensive understanding of modern applications' storage performance profiles. In this paper, we present EZIOTracer, a unified I/O tracer for both (Linux) kernel and user spaces, targeting data intensive applications. EZIOTracer is composed of a userland as well as a kernel space tracer, complemented with a trace analysis framework able to merge the output of the two tracers, and in particular to relate user-level events to kernel-level ones, and vice-versa. On the kernel side, EZIOTracer relies on eBPF to offer safe, low-overhead, low memory footprint, and flexible tracing capabilities. We demonstrate using FIO benchmark the ability of EZIOTracer to track down I/O performance issues by relating events recorded at both the kernel and user levels. We show that this can be achieved with a relatively low overhead that ranges from 2% to 26% depending on the I/O intensity.","PeriodicalId":38935,"journal":{"name":"Operating Systems Review (ACM)","volume":"55 1","pages":"88 - 98"},"PeriodicalIF":0.0,"publicationDate":"2021-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3469379.3469391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48149376","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: 6

GeoGraph: A Framework for Graph Processing on Geometric Data 地理:几何数据的图形处理框架

Operating Systems Review (ACM) Pub Date : 2021-01-01 DOI: 10.1145/3469379.3469384

Yiqiu Wang, Shangdi Yu, Laxman Dhulipala, Yan Gu, Julian Shun

{"title":"GeoGraph: A Framework for Graph Processing on Geometric Data","authors":"Yiqiu Wang, Shangdi Yu, Laxman Dhulipala, Yan Gu, Julian Shun","doi":"10.1145/3469379.3469384","DOIUrl":"https://doi.org/10.1145/3469379.3469384","url":null,"abstract":"In many applications of graph processing, the input data is often generated from an underlying geometric point data set. However, existing high-performance graph processing frameworks assume that the input data is given as a graph. Therefore, to use these frameworks, the user must write or use external programs based on computational geometry algorithms to convert their point data set to a graph, which requires more programming effort and can also lead to performance degradation. In this paper, we present our ongoing work on the GeoGraph framework for shared-memory multicore machines, which seamlessly supports routines for parallel geometric graph construction and parallel graph processing within the same environment. GeoGraph supports graph construction based on k-nearest neighbors, Delaunay triangulation, and β-skeleton graphs. It can then pass these generated graphs to over 25 graph algorithms. GeoGraph contains highperformance parallel primitives and algorithms implemented in C++, and includes a Python interface. We present four examples of using GeoGraph, and some experimental results showing good parallel speedups and improvements over the Higra library. We conclude with a vision of future directions for research in bridging graph and geometric data processing.","PeriodicalId":38935,"journal":{"name":"Operating Systems Review (ACM)","volume":"55 1","pages":"38-46"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3469379.3469384","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64044488","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: 7

Wukong: A Distributed Framework for Fast and Concurrent Graph Querying Wukong:一个用于快速并发图查询的分布式框架

Operating Systems Review (ACM) Pub Date : 2021-01-01 DOI: 10.1145/3469379.3469388

Rong Chen, Haibo Chen

引用次数: 3

VRGQ: Evaluating a Stream of Iterative Graph Queries via Value Reuse VRGQ:通过值重用评估迭代图查询流

Operating Systems Review (ACM) Pub Date : 2021-01-01 DOI: 10.1145/3469379.3469382

Xiaolin Jiang, Chengshuo Xu, Rajiv Gupta

引用次数: 2