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Using JIT compilation and configurable runtime systems for efficient deployment of java programs on ubiquitous devices

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International Conference on Ubiquitous computing, Ubicomp 2001

作者： Teodorescu, Radu Pandey, Raju Parallel and Distributed Computing Laboratory Computer Science Department University of California Davis United States

ISBN: (纸本)3540426140

As the proliferation of ubiquitous devices moves computation away from the conventional desktop computer boundaries, distributed systems design is being exposed to new challenges. A distributed system supporting a ubiquitous computing application must deal with a wider spectrum of hardware architectures featuring structural and functional differences, and resources limitations. Due to its architecture independent infrastructure and object-oriented programming model, the Java programming environment can support flexible solutions for addressing the diversity among these devices. Unfortunately, Java solutions are often associated with high costs in terms of resource consumption, which limits the range of devices that can benefit from this approach. In this paper, we present an architecture that deals with the cost and complexity of running Java programs by partitioning the process of Java program execution between system nodes and remote devices. The system nodes prepare a Java application for execution on a remote device by generating device-specific native code and application-specific runtime system on the fly. The resulting infrastructure provides the flexibility of a high-level programming model and the architecture independence specific to Java. At the same time the amount of resources consumed by an application on the targeted device are comparable to that of a native implementation. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Object oriented programming

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Powerlyra: Differentiated graph computation and partitioning on skewed graphs 15

Powerlyra: Differentiated graph computation and partitioning...

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10th European Conference on Computer Systems, EuroSys 2015

作者： Chen, Rong Shi, Jiaxin Chen, Yanzhe Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China

ISBN: (纸本)9781450332385

Natural graphs with skewed distribution raise unique challenges to graph computation and partitioning. Existing graph-parallel systems usually use a "one size fits all" design that uniformly processes all vertices, which either suffer from notable load imbalance and high contention for high-degree vertices (e.g., Pregel and GraphLab), or incur high communication cost and memory consumption even for low-degree vertices (e.g., PowerGraph and GraphX). In this paper, we argue that skewed distribution in natural graphs also calls for differentiated processing on highdegree and low-degree vertices. We then introduce Power-Lyra, a new graph computation engine that embraces the best of both worlds of existing graph-parallel systems, by dynamically applying different computation and partitioning strategies for different vertices. PowerLyra further provides an efficient hybrid graph partitioning algorithm (hybrid-cut) that combines edge-cut and vertex-cut with heuristics. Based on PowerLyra, we design locality-conscious data layout optimization to improve cache locality of graph accesses during communication. PowerLyra is implemented as a separate computation engine of PowerGraph, and can seamlessly support various graph algorithms. A detailed evaluation on two clusters using graph-analytics and MLDM (machine learning and data mining) applications show that PowerLyra outperforms PowerGraph by up to 5.53X (from 1.24X) and 3.26X (from 1.49X) for real-world and synthetic graphs accordingly, and is much faster than other systems like GraphX and Giraph, yet with much less memory consumption. A porting of hybrid-cut to GraphX further confirms the efficiency and generality of PowerLyra. Copyright © 2015 ACM 978-1-4503-3238-5/15/04 $15.00.

关键词： Data mining

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A comparative study of star graphs and rotator graphs

A comparative study of star graphs and rotator graphs

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23rd International Conference on parallel Processing, ICPP 1994

作者： Ponnuswamy, Subburajan Chaudhary, Vipin Parallel and Distributed Computing Laboratory Dept. of ECE Wayne State University Detroit MI 48202 United States

ISBN: (纸本)0849324939

Star graph is an extensively studied Cayley graph, considered to be an attractive alternative to the popular binary cube. The rotator graphs are a set of directed Cayley graphs introduced recently. In this paper we compare the structural and algorithmic aspects of star graphs with that of rotator graphs. In the process we present some new results for star graphs and rotator graphs. We present a formula for the number of nodes at any distance from the identity permutation in star graphs. The minimum bisection width of star and rotator graphs is obtained. Partitioning and fault tolerant parameters for both star and rotator graphs are analyzed. The node disjoint parallel paths and hence the upper bound on the fault diameter of rotator graphs are presented. We compare the minimal path routing in star and rotator graphs using simulation results. © 1994 IEEE.

关键词： Directed graphs

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Mbapp: Efficient Memory-Balanced Pipeline parallelism for Large Model Fine-Tuning on Commodity GPU Servers 24

Mbapp: Efficient Memory-Balanced Pipeline Parallelism for La...

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5th International Conference on Computer Information and Big Data Applications, CIBDA 2024

作者： Liu, Yujie Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha410000 China

ISBN: (纸本)9798400718106

Large-scale models have demonstrated outstanding performance across various downstream tasks. Pipeline parallelism is essential for fine-tuning large models on commodity GPU servers, as it plays a crucial role in making their exceptional performance more accessible and widespread. Existing approaches have encountered challenges in attaining effective memory-balanced pipeline parallelism. This paper presents a novel memory load-balanced pipeline parallel solution that aims to distribute memory usage evenly across stages on commodity GPU servers by leveraging NVLink bridges. The solution presents a novel approach for transferring data from GPUs to CPUs through the PCI-e link between adjacent GPUs interconnected by the NVLink bridge. Moreover, our methodology orchestrates data transfer operations to reduce offloading latency during the fine-tuning of large models. The evaluation demonstrates that our approach enhances switching efficiency by 1.46x to 1.76x and improves throughput by 2.04x to 3.59x compared to the PyTorch offloading technique. © 2024 ACM.

关键词： Deep neural networks

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Rethinking benchmarking for NVM-based file systems 16

Rethinking benchmarking for NVM-based file systems

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7th ACM SIGOPS Asia-Pacific Workshop on Systems, APSys 2016

作者： Dong, Mingkai Yu, Qianqian Zhou, Xiaozhou Hong, Yang Chen, Haibo Zang, Binyu Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China

ISBN: (纸本)9781450342650

Fast, byte-addressable NVM promises near-cache latency and near memory bus throughput for file systems, which is evident by recent development of a number of NVM file systems (NVMFS). However, a key approach to measuring their performance, file system benchmarks, remains unchanged as those for disk-based file systems (DFS). In this paper, we analyze the pitfalls of using DFS benchmarks to evaluate NVMFS and propose several changes to existing DFS benchmarks to better characterize the performance of NVMFS. © 2016 ACM.

关键词： Benchmarking

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NUMA-aware graph-structured analytics 2015

NUMA-aware graph-structured analytics

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20th ACM SIGPLAN Symposium on Principles and Practice of parallel Programming, PPoPP 2015

作者： Zhang, Kaiyuan Chen, Rong Chen, Haibo Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University China

ISBN: (纸本)9781450332057

Graph-structured analytics has been widely adopted in a number of big data applications such as social computation, web-search and recommendation systems. Though much prior research focuses on scaling graph-analytics on distributed environments, the strong desire on performance per core, dollar and joule has generated considerable interests of processing large-scale graphs on a single server-class machine, which may have several terabytes of RAM and 80 or more cores. However, prior graph-analytics systems are largely neutral to NUMA characteristics and thus have suboptimal performance. This paper presents a detailed study of NUMA characteristics and their impact on the efficiency of graph-analytics. Our study uncovers two insights: 1) either random or interleaved allocation of graph data will significantly hamper data locality and parallelism;2) sequential inter-node (i.e., remote) memory accesses have much higher bandwidth than both intra- and inter-node random ones. Based on them, this paper describes Polymer, a NUMA-aware graph-analytics system on multicore with two key design decisions. First, Polymer differentially allocates and places topology data, application-defined data and mutable runtime states of a graph system according to their access patterns to minimize remote accesses. Second, for some remaining random accesses, Polymer carefully converts random remote accesses into sequential remote accesses, by using lightweight replication of vertices across NUMA nodes. To improve load balance and vertex convergence, Polymer is further built with a hierarchical barrier to boost parallelism and locality, an edge-oriented balanced partitioning for skewed graphs, and adaptive data structures according to the proportion of active vertices. A detailed evaluation on an 80-core machine shows that Polymer often outperforms the state-of-the-art single-machine graph-analytics systems, including Ligra, X-Stream and Galois, for a set of popular real-world and synthetic grap

关键词： Random access storage

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High Performance Interconnect Network for Tianhe System

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Journal of Computer Science & Technology 2015年第2期30卷 259-272页

作者：廖湘科庞征王克非卢宇彤谢旻夏军董德尊所光 College of Computer National University of Defense Technology Changsha 410073 Science and Technology on Parallel and Distributed Processing Laboratory National Changsha 410073 China China University of Defense Technology State Key Laboratory of High Performance Computing National University of Defense Technology Changsha 410073 China

In this paper, we present the Tianhe-2 interconnect network and message passing services. We describe the architecture of the router and network interface chips, and highlight a set of hardware and software features effectively supporting high performance communications, ranging over remote direct memory access, collective optimization, hardwareenable reliable end-to-end communication, user-level message passing services, etc. Measured hardware performance results are also presented.

关键词： Tianhe-2 supercomputer interconnect network router architecture network interface architecture user-level message passing

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Performance problems online detection in cloud computing systems via analyzing request execution paths

Performance problems online detection in cloud computing sys...

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Workshop on Proactive Failure Avoidance, Recovery, and Maintenance

作者： Mi, Haibo Wang, Huaimin Yin, Gang Cai, Hua Zhou, Qi Sun, Tingtao National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Computing Platform Alibaba Cloud Computing Company Hangzhou China

ISBN: (纸本)9781457703751

It is quite a headache for developers to online detect performance problems in large-scale cloud computing systems. The behavior and the hidden connections among the huge amount of runtime request execution paths in cloud computing systems usually contain useful information for performance problem detection. In this paper, we propose an approach to rapidly diagnose the source of performance degradation in large-scale non-stop cloud computing systems. The approach first groups the user requests into categories with a fast clustering algorithm;then applies the principal components analysis to extract the primary methods;finally compares the normal and abnormal behaviors of the primary methods to localize the main cause of performance problems. We conduct extensive experiments over a real-world enterprise system providing services for the public. The results show that our approach can locate the prime causes of performance problems accurately and efficiently.1 © 2011 IEEE.

关键词： Cloud computing

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Identifying faults in large-scale distributed systems by filtering noisy error logs

Identifying faults in large-scale distributed systems by fil...

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Workshop on Proactive Failure Avoidance, Recovery, and Maintenance

作者： Rao, Xiang Wang, Huaimin Shi, Dianxi Chen, Zhenbang Cai, Hua Zhou, Qi Sun, Tingtao National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410073 China Computing Platform Alibaba Cloud Computing Corporation Hangzhou China

ISBN: (纸本)9781457703751

Extracting fault features with the error logs of fault injection tests has been widely studied in the area of large scale distributed systems for decades. However, the process of extracting features is severely affected by a large amount of noisy logs. While the existing work tries to solve the problem by compressing logs in temporal and spatial views or removing the semantic redundancy between logs, they fail to consider the co-existence of other noisy faults that generate error logs instead of injected faults, for example, random hardware faults, unexpected bugs of softwares, system configuration faults or the error rank of a log severity. During a fault feature extraction process, those noisy faults generate error logs that are not related to a target fault, and will strongly mislead the resulted fault features. We call an error log that is not related to a target fault a noisy error log. To filter out noisy error logs, we present a similarity-based error log filtering method SBF, which consists of three integrated steps: (1) model error logs into time series and use haar wavelet transform to get the approximate time series;(2) divide the approximate time series into sub time series by valleys;(3) identify noisy error logs by comparing the similarity between the sub time series of target error logs and the template of noisy error logs. We apply our log filtering method in an enterprise cloud system and show its effectiveness. Compared with the existing work, we successfully filter out noisy error logs and increase the precision and the recall rate of fault feature extraction.1 © 2011 IEEE.

关键词： Errors

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Network and service management and diagnostics solution of a remote patient monitoring system

Network and service management and diagnostics solution of a...

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3rd IEEE International Symposium on Logistics and Industrial Informatics, LINDI 2011

作者： Kozlovszky, Miklos Meixner, Zsolt Windisch, Gergely Márton, Judit Ács, Sándor Bogdanov, Pál Boruzs, Anikó Kotcauer, Péter Ferenczi, János Kozlovszky, Viktor MTA SZTAKI Laboratory of Parallel and Distributed Computing Budapest Hungary Óbuda University John von Neumann Faculty of Informatics Budapest Hungary

ISBN: (纸本)9781457718410

We have developed a combined network and service management and diagnostics solution for our in-house developed remote patient monitoring system. The developed system has included into the ALPHA eHealth/remote patient monitoring system and was successfully used in our large Living Lab infrastructure operating in three different Hungarian regions with 40 patients. In this paper we will identify the key elements of the combined Network and Service Management solution of the remote patient monitoring system. © 2011 IEEE.

关键词： Telemedicine

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