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Efficient. Scalable and Robust Data Shuffle Service for Distributed MapReduce computing on Cloud

Efficient. Scalable and Robust Data Shuffle Service for Dist...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Rong Gu Xu Huang Haipeng Dai Xiaoyu Geng Xiaofei Chen Yihua Huang Fu Xiao Guihai Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Nanjing University of Posts and Telecommunications Nanjing China

Distributed Map Reduce computing frameworks, such as Hadoop, Spark, and Flink, are widely used in various domains which face big data challenges. Inside Map Reduce, Shuffle is a critical stage that bridges up the Map stage and Reduce stage through data partition and network transmission under the hood. With the rapidly growing scale of data and clusters, the all-to-all data transfer in Shuffle becomes inefficient and frequently fails, making Shuffle the bottleneck of many big data processing jobs. To improve the Shuffle efficiency, we propose a hybrid Shuffle model based on file pre-merging and partition reorganization mechanisms. To address the Shuf-fle stability issue, we design a novel data transmission model based on the replication mechanism. Finally, to support scalable Shuffle with high performance, we present a Shuffle parallelism degree tuning approach based on machine learning and searching techniques. We have implemented the proposed Shuffle models and strategies into the widely-used MapReduce-based big data platform Spark for evaluation. Experimental evaluation shows that, compared with the state-of-the-art systems, our proposed methods outperform them by 23% on average. As a practical application case, the proposed Shuffle service has been running in the real-world environment of ByteDance. Inc over one year with significant performance improvement.

关键词： Machine learning parallel processing Big Data Data models Stability analysis Sparks Task analysis

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8th ifip international conference on network and parallel computing, NPC 2011

8th IFIP International Conference on Network and Parallel Co...

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8th ifip international conference on network and parallel computing, NPC 2011

ISBN: (纸本)9783642244025

the proceedings contain 27 papers. the special focus in this conference is on Filesystems and Data. the topics include: Malleable mapreduce for shared-memory systems;a load-aware data placement policy on cluster file system;a new heuristic method for fuzzing data generation;improved compact routing schemes for power-law networks;accelerating the requirement space exploration through coarse-grained parallel execution;a global benefit maximization task-bundle allocation;service quality assurance mechanisms for P2P SIP VoIP;a virtual server consolidation manager for cluster;informed live migration strategies of virtual machines for cluster load balancing;dynamic advance reservation for grid system using resource pools;a way of key management in cloud storage based on trusted computing;a trust-based defensive system model for cloud computing;a new method for authentication based on covert channel;a layered detection method for malware identification;pedestrian detection and tracking using HOG and oriented-LBP features;an efficient checkpointing scheme using price history of spot instances in cloud computing environment;towards a universal sketch for origin-destination network measurements;a power adjusting algorithm on mobility control in mobile ad hoc networks;power-aware run-time incremental mapping for 3-D networks-on-chip;a SSD-based RAID for system endurance and performance;reorder the write sequence by virtual write buffer to extend SSD’s lifespan;memory-side acceleration for XML parsing;hardware performance monitoring for the rest of us and an efficient architectural design of hardware interface for heterogeneous multi-core system.

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LogTrans: Providing Efficient Local-Global Fusion with Transformer and CNN parallel network for Biomedical Image Segmentation

LogTrans: Providing Efficient Local-Global Fusion with Trans...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Xingqing Nie Xiaogen Zhou Zhiqiang Li Luoyan Wang Xingtao Lin Tong Tong College of Physics and Information Engineering Fuzhou University Fuzhou China Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology Fuzhou University Fuzhou China Imperial Vision Technology Fuzhou China

Accurate biomedical image segmentation is a prerequisite for excellent computer-aided diagnosis (CAD) systems. A series of researches have shown that convolutional neural networks (CNNs) have made impressive progress in segmentation tasks. Nevertheless, owing to the finite receptive field of CNN-based algorithms, such networks are focused too much on local area features rather than global context. While the Transformer architecture can encode global dependencies information through the self-attention mechanism, this mechanism typically ignores the local pixel-level structural information within each divided patch. therefore, a better solution is still needed for how to integrate CNN architecture with Transformer architecture efficiently. In this essay, we propose an originative parallel segmentation algorithm called LogTrans. First, in the encoder path, the local details and global contour dependencies on the entire image are captured by the CNN branch and the Transformer branch, respectively. then these two branches complement each other by a novel separate-combiner (SeCo) module, leading to better fused features. Moreover, we attempt to further enhance the segmentation properties by using a residual stackable dilated (ReS $D$ ) block, which applies residual shortcut connections to resolve dimension alterations in the target region and stacks dilated convolutions to capture more spatial information. the proposed LogTrans framework was evaluated on two biomedical datasets, including ISIC-2017 and UITNS-2022 datasets. Collectively, multiple results have indicated that our LogTrans performs superior with other state-of-the-art architectures in both visual comparison and quantitative appraisal.

关键词： Image segmentation Visualization Fuses Transformers Decoding Image restoration Convolutional neural networks

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parallel Asynchronous Memetic Optimization for Freeform Optical Design 10th

Parallel Asynchronous Memetic Optimization for Freeform Opti...

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10th international conference on the theory and Practice of Natural computing (TPNC)

作者： Sakharov, Maxim Houllier, thomas Lepine, thierry Bauman Moscow State Tech Univ 5-1 2 Ya Baumanskaya Moscow 105005 Russia Univ Lyon Lab Hubert Curien UMR CNRS 5516 18 Rue Benoit Lauras F-42000 St Etienne France Sophia Engn 5 Rue Soutrane F-06560 Sophia Antipolis France Inst Opt Grad Sch 18 Rue Benoit Lauras F-42000 St Etienne France

ISBN: (纸本)9783030904258;9783030904241

Freeform optical systems include lens or mirror surfaces with shapes that have no axis of revolution in order to reach better performance in off-axis designs such as Head-Mounted Displays (HMD). they have an increased number of design parameters compared to rotationally symmetrical optical systems which makes their optimization more difficult. In this paper we presented results of several studies where various optimization techniques were used to optimize a freeform HMD prism with two mirrors. It was demonstrated that algorithms which performed well on traditional systems were not capable of obtaining high-quality solutions for the system under investigation. this paper presents a new parallel asynchronous memetic algorithm designed for optimizing freeform optical systems. the algorithm incorporates local search techniques into an asynchronous parallel computing procedure thus helping to speed-up the convergence to a high-quality solution. the results of a comparative study, presented in this paper along with the algorithm description, demonstrate that the proposed method is not only capable of obtaining a good solution but is also comparable with high-end commercial optical software packages.

关键词： Global optimization Freeform optics Memetic algorithms parallel computing

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the Entry-Extensible Cuckoo Filter 17th

The Entry-Extensible Cuckoo Filter

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17th ifip WG 10.3 international conference on network and parallel computing, NPC 2020

作者： Yu, Shuiying Wu, Sijie Chen, Hanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783030794774

the emergence of large-scale dynamic sets in real applications brings severe challenges in approximate set representation structures. A dynamic set with changing cardinality requires an elastic capacity of the approximate set representation structure, while traditional static structures, e.g., bloom filter, cuckoo filter, and their variants cannot satisfy the requirements. Existing dynamic approximate set representation structures only provide filter-level extensions, which require a single membership query to probe all discrete filters one by one. the large number of small discrete memory accesses takes up the vast majority of query time and results in unsatisfied query performance. To address the problem, in this work we propose the entry-extensible cuckoo filter (E2CF) to reduce memory access overhead for dynamic set representation and accelerate the membership query. E2CF utilizes adjacent buckets with continuous physical addresses in a cuckoo filter to extend bucket entries, which avoids many discrete memory accesses in a query. To further make E2CF space and time efficient, we adopt asynchronous extension and fine-grained splitting methods. Experiment results show that compared to state-of-the-art designs, E2CF reduces the query and insertion time by 82% and 28%, respectively. © 2021, ifip international Federation for Information Processing.

关键词： Data structures

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Rethinking Breast Lesion Segmentation in Ultrasound: A New Video Dataset and A Baseline network 25th

Rethinking Breast Lesion Segmentation in Ultrasound: A New V...

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25th international conference on Medical Image computing and Computer Assisted Intervention (MICCAI)

作者： Li, Jialu Zheng, Qingqing Li, Mingshuang Liu, Ping Wang, Qiong Sun, Litao Zhu, Lei Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Guangdong Prov Key Lab Comp Vis & Virtual Real Shenzhen Peoples R China Zhejiang Prov Peoples Hosp Hangzhou Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9783031164408;9783031164392

Automatic breast lesion segmentation in ultrasound (US) videos is an essential prerequisite for early diagnosis and treatment. this challenging task remains under-explored due to the lack of availability of annotated US video dataset. though recent works have achieved better performance in natural video object segmentation by introducing promising Transformer architectures, they still suffer from spatial inconsistency as well as huge computational costs. therefore, in this paper, we first present a new benchmark dataset designed for US video segmentation. then, we propose a dynamic parallel spatial-temporal Transformer (DPSTT) to improve the performance of lesion segmentation in US videos with higher computational efficiency. Specifically, the proposed DPSTT disentangles the non-local Transformer along the temporal and spatial dimensions, respectively. the temporal Transformer attends temporal lesion movement on different frames at the same regions, and the spatial Transformer focuses on similar context information between the previous and the current frames. Furthermore, we propose a dynamic selection scheme to effectively sample the most relevant frames from all the past frames, and thus prevent out of memory during inference. Finally, we conduct extensive experiments to evaluate the efficacy of the proposed DPSTT on the new US video benchmark dataset.

关键词： Computational efficiency

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Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering 8

Consensus Variational and Monte Carlo Algorithms for Bayesia...

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8th IEEE international conference on Big Data (Big Data)

作者： Ni, Yang Jones, David Wang, Zeya Texas A&M Univ Dept Stat College Stn TX 77843 USA Petuum Inc Pittsburgh PA USA

ISBN: (纸本)9781728162515

We propose both the first embarrassingly parallel consensus variational inference algorithm and a new consensus Monte Carlo algorithm for efficient implementation of Bayesian nonparametric mixture models. the proposed algorithms are based on a group clustering approach, and they substantially accelerate inference and reduce memory costs compared with standard Markov chain Monte Carlo and variational inference algorithms for clustering. We demonstrate that our proposed algorithms are significantly faster than competing methods while maintaining the same clustering accuracy. Due to their simplicity and embarrassingly parallel nature, our proposed algorithms are straightforward to implement and widely applicable beyond the models and applications considered in this paper.

关键词： big data embarrassingly parallel computing unsupervised learning image clustering

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Supervised Learning Based Resource Allocation with network Slicing 8

Supervised Learning Based Resource Allocation with Network S...

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8th international conference on Advanced Cloud and Big Data (CBD)

作者： Zhang, Tianxiang Bian, Yuxin Lu, Qianchun Qi, Jin Zhang, Kai Ji, Hong Wang, Wanyuan Wu, Weiwei zte Corp Nanjing Jiangsu Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781665423137

With the fast growth of wireless network technologies(e.g., 5G, data center networks) and increasing demand for services with high Quality of Services(QoS), efficient management of network resources becomes more and more important. network slicing is a effective method for reducing computing time through parallel computing and improve QoS of services. However, determining in which slice a request should be deployed on the premise of ensuring the success rate of transmission is difficult. To this end, we propose a slicing model and a resource allocation scheme in network slices. We make several contributions: i) a slice model that help us reduce the time it takes to calculate routes by parallel computing, and the formulation of resource allocation problem in network slices which aims at maximizing the amount of data transmitted successfully, and ii) a supervised-learning based model to quickly determine in which slices requests should be deployed, that can improve the success rate of transmission. Experimental results show that our proposed approach can achieve a good performance in network slicing environment.

关键词： network Slicing Resource Allocation Supervised Learning

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FEB3D : An Efficient FPGA-Accelerated Compression Framework for Microscopy Images 17th

FEB3D : An Efficient FPGA-Accelerated Compression Framework ...

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17th ifip WG 10.3 international conference on network and parallel computing, NPC 2020

作者： Liu, Wanqi Li, Yewen Zang, Dawei Tan, Guangming Institute of Computing Technology Chinese Academy of Sciences Beijing100095 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783030794774

With the rapid development of fluorescence microscope technologies, high-content screening and light-sheet microscopy are producing ever-larger datasets that pose great challenges in data storing and data sharing. As a popular compression tool, B3D introduces a noise dependent compression algorithm for microscopy images to preserve the numerical intensities of all pixel within their uncertainties by exploiting the natural variability of each pixel value. Nevertheless, the high complexity of the processing flow restricts the deployment of the tool since the throughput and power consumption cannot satisfy the increasing demand. In this paper, we propose an efficient FPGA-accelerated data compression framework based on B3D. Following the co-design methodology, the compression processing flows are partitioned into different blocks to deploy on CPU or FPGA according to their computation characteristics. Also, we design a custom accelerator core that consists of multiple full on-chip pipelines using the channel function of the Intel OpenCL toolkit to implement data-flow driven computation. Our experiments show that the proposed framework achieves up to 32 × throughput for a single pipeline compared with Intel Xeon E3-1220 v5 operating at 3.00 GHz, and 6 × energy-efficiency compared with GPU implementation. © 2021, ifip international Federation for Information Processing.

关键词： Field programmable gate arrays (FPGA)

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Pseudo-Reconfigurable Heterogeneous Solution for Accelerating Spectral Clustering 8

Pseudo-Reconfigurable Heterogeneous Solution for Acceleratin...

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8th IEEE international conference on Big Data (Big Data)

作者： Malita, Mihaela Popescu, George Vladut Stefan, Gheorghe M. Smith Coll Comp Sci Dept Northampton MA 01063 USA Univ Politehn Bucuresti Elect Devices Circ & Arch Dept Bucharest Romania Univ Politehn Bucuresti Elect Devices Circuits & Arch Dept Bucharest Romania

ISBN: (纸本)9781728162515

Spectral clustering is a Machine Learning technique intensively used in Big Data applications. It makes extensive use of linear algebra. this article introduces the concept of MapReduce Accelerator (MRA) as the reconfigurable part of a heterogeneous computing system. Although the accelerator we propose is a general purpose one, it has some specific features related to the targeted application. this is possible due to the pseudo-reconfigurable environment which deploys in FPGA a parameterizable programmable accelerator. the main specific characteristics of the accelerator are proposed as a result of the analysis performed on the spectral clustering algorithms. the architecture is described and the spectral clustering algorithms are evaluated. the proposed solution is compared, in terms of computing performance and energy consumption, with other solutions published in the literature. the increase in computing performance is accompanied by a 3-5 times reduction in energy consumed. the accelerator is a linear array of cells controlled by a sequencer loop closed through a reduction network. Each cell is a simple, accumulator-based execution unit with a big two-port register file. the reduction network is a log-depth pipelined circuit performing few reduction functions such as add, min, max. the experimental system is a PYNQ-Z2 board equipped with Zinq 7020 SoC;it is used to implement and evaluate the acceleration provided by an 128-cell MRA.

关键词： Spectral clustering parallel algorithm parallel computing accelerator heterogeneous computing pseudo-reconfigurable computing

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