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Accelerating Traditional File systems on Non-volatile Main Memory

Accelerating Traditional File Systems on Non-volatile Main M...

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international conference on Parallel and distributed systems (ICPADS)

作者： Weitong Jin Yanmin Zhu Linpeng Huang Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai Key Lab of Scalable Computing and Systems

ISBN: (纸本)9781538632086

New non-volatile memory (NVM) technologies, e.g., Phase Change Memory (PCM), are emerging with main features such as byte addressability, cheapness and large size, and will be deployed to serve as main memory. For fast processing of big data, block device drivers have been developed for NVM such that traditional file systems (e.g., EXT4) operated on non-volatile main memory. However, the existing block driver blindly caches pages, which incurs unnecessary access delays. To overcome this drawback, this paper develops a novel block device driver, called NVMBD, for byte-addressable non-volatile main memory. It adopts two methods of processing block I/O requests, i.e., traditional block I/O and BMAP. On the one hand, traditional block I/O reads/writes 512B sectors with multiple load/store instructions. On the other hand, BMAP inserts NVM pages into the page cache tree when processing block I/O requests, which removes the page movement between the page cache and the NVM. BMAP involves in the management of page cache, but it is transparent to file systems. BMAP provides file systems with the DAX-like feature, improving the file system performance. Through comprehensive experiments, we demonstrate that the developed block device driver significantly improves the file access performance.

关键词： Nonvolatile memory Performance evaluation Random access memory Device drivers Kernel System performance

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An Efficient Label Routing on High-Radix Interconnection Networks

An Efficient Label Routing on High-Radix Interconnection Net...

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international conference on Parallel and distributed systems (ICPADS)

作者： Fei Lei Dezun Dong Xiangke Liao College of Computer National University of Defense Technology China National Laboratory for Parallel and Distributed Processing National University of Defense Technology China Collaborative Innovation Center of High Performance Computing National University of Defense Technology China

ISBN: (纸本)9781538632086

Cost-effective adaptive routing has a significant impact on overall performance for high-radix hierarchical topologies, such as Dragonfly, which achieve a lower network diameter than traditional topologies, Torus and Fat tree, but exhibit a lower degree of adaptiveness for shortest-path rout- ing. Existing adaptive routing methods for those hierarchical topologies improve the adaptiveness by increasing path length, i.e. local or global adaptive routing, and thus suffer from complex and costly deadlock avoidance. This work aims to maximize the routing adaptiveness at the minimum cost of deadlock avoidance. We propose a label routing method for high-radix hierarchical networks. This label routing utilizes a co-design methodology and coordinates the two pipelines, input queue and routing computation, in the router microarchitec- ture. Packets in the input buffer are labeled by our routing algorithm depending on network states. We reorganize the input buffer and develop a label routing algorithm, named Green-Red Routing, GRR. GRR relaxes the requirement of using virtual channels to eliminate routing deadlock, and mitigates buffer resources dedicated to deadlock avoidance. GRR manages the buffer resources and balance its utilization elaborately, and achieve fully adaptive routing efficiently. We conduct extensive experiments to evaluate the performance of GRR on Dragonfly and compare it with state-of-the-art works. The results show that GRR achieves 10%-35% higher performance than existing routing algorithms under most traffic patterns.

关键词： Routing System recovery Network topology Topology Resource management Adaptive systems Organizations

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Steal-A-GC: Framework to Trigger GC during Idle Periods in distributed systems 23

Steal-A-GC: Framework to Trigger GC during Idle Periods in D...

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23rd IEEE international conference on High Performance computing (HiPC)

作者： Saraswati, Sujoy Chatterjee, Soumitra Ramachandra, Ranganath Hewlett Packard Enterprise Bangalore Karnataka India

ISBN: (纸本)9781509054114

distributed applications for data analytics have emerged to be a significant growth domain both in terms of technology as well as business relevance. With the advent of Hadoop MapReduce, several distributed execution frameworks have been developed for data analytics and machine learning applications. Many of these frameworks are written in Java, Scala or Python and run on top of the Java Virtual Machine (JVM), leaving them vulnerable to the performance impact of garbage collection (GC) operations. Application pauses induced by the Java GC are notorious in being one of the most important contributors of runtime performance bottleneck for a single process execution. These pauses are present in the distributed scenario as well, though in this case, it is significantly more complex to measure the impact of a GC pause for the overall execution runtime. Typically, in a distributed workload, a GC can result in delayed tasks impacting the overall completion time of the application. We present herein a novel way to preemptively trigger Java GC, stealing idle cycles, so as to prevent future tasks from being impacted by a GC pause during peak execution periods, resulting in improved overall application throughput.

关键词： Performance Throughput Cycle Stealing distributed computing Garbage Collection Java Virtual Machine Big data Analytics GC JVM JMX

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A High Performance computing Framework for Data Mining 23

A High Performance Computing Framework for Data Mining

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23rd Annual IEEE international conference on High Performance computing, Data, and Analytics (HiPCW)

作者： Goyal, Navneet Balasubramaniam, Sundar Goyal, Poonam Islam, Saiyedul Sati, Mohit Birla Inst Technol & Sci Pilani Dept Comp Sci & Informat Syst Adv Data Analyt & Parallel Technol Lab Pilani Rajasthan India

ISBN: (纸本)9781509057733

Mining large data sets is no longer the prerogative of computer scientists - specialists in a wide variety of domains are performing analytics as a day-to-day activity. Often such analyses are specific to the domain and analysts are required to devise new algorithms or techniques. For such scenarios, providing a high-level programming environment that delivers high performance on clusters is a challenge. We propose a framework that supports high-level programming using domain abstractions in data mining while delivering scalable performance on commodity clusters i.e. clusters of multi-core workstations. This framework includes a domain specific programming language, DWARF, to enable data mining specialists to rapidly prototype algorithms. DWARF is supported by a compiler that automatically parallelizes code by identifying domain specific patterns and translating them to parallel code that exploits data parallelism and task parallelism. The compiler generates code for a hybrid virtual machine supporting distributed memory model at the top level and shared memory model nested within. The code generated by the compiler can be scheduled on commodity clusters. We compare the proposed framework with other frameworks commonly used for data mining on distributed platforms.

关键词： Big Data Analytics Data Mining Domain Specific Language High Performance computing Parallelizing Compiler

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Second Workshop on Cyber-Physical systems Security and PrivaCy (CPS-SPC'16) 16

Second Workshop on Cyber-Physical Systems Security and Priva...

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23rd ACM conference on Computer and Communications Security (CCS)

作者： Cardenas, Alvaro A. Bobba, Rakesh B. Univ Texas Dallas Richardson TX 75083 USA Oregon State Univ Corvallis OR 97331 USA

ISBN: (纸本)9781450341394

The Second international Workshop on Cyber-Physical systems Security and PrivaCy (CPS-SPC'16) is being held in conjunction with the 23rd ACM CCS conference. This second edition follows a successful workshop held with ACM CCS in 2015. The workshop was motivated by several observations. First, cyber-physical systems represent the new frontier for cyber risk. The attack surface imposed by the convergence of computing, communications and physical control represents unique challenges for security researchers and practitioners. Second, majority of the published literature addressing the security and privacy of CPS reflect a field still in its infancy. As such, the overall principles, models, and theories for securing CPS have not yet emerged. Third, the organizers of this workshop strongly felt that a premiere forum associated with a premiere conference was needed for rapidly publishing diverse, multidisciplinary in progress work on the security and privacy of CPS and galvanizing the research community. The set of accepted papers reflect this vision. We have organized an exciting program for this workshop and look forward to active participation in this and future workshops.

关键词： Cyber-Physical systems Security Privacy Safety Reliability

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Lambda Means Clustering: Automatic Parameter Search and distributed computing Implementation 23

Lambda Means Clustering: Automatic Parameter Search and Dist...

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23rd international conference on Pattern Recognition (ICPR)

作者： Comiter, Marcus Cha, Miriam Kung, H. T. Teerapittayanon, Surat Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781509048472

Recent advances in clustering have shown that ensuring a minimum separation between cluster centroids leads to higher quality clusters compared to those found by methods that explicitly set the number of clusters to be found, such as k-means. One such algorithm is DP-means, which sets a distance parameter lambda for the minimum separation. However, without knowing either the true number of clusters or the underlying true distribution, setting lambda itself can be difficult, and poor choices in setting lambda will negatively impact cluster quality. As a general solution for finding lambda, in this paper we present lambda-means, a clustering algorithm capable of deriving an optimal value for lambda automatically. We contribute both a theoretically-motivated cluster-based version of lambda-means, as well as a faster conflict-based version of lambda-means. We demonstrate that lambda-means discovers the true underlying value of lambda asymptotically when run on datasets generated by a Dirichlet Process, and achieves competitive performance on a real world test dataset. Further, we demonstrate that when run on both parallel multicore computers and distributed cluster computers in the cloud, cluster-based lambda-means achieves near perfect speedup, and while being a more efficient algorithm, conflict-based lambda-means achieves speedups only a factor of two away from the maximum-possible.

关键词： Clustering algorithms Elbow Partitioning algorithms Computers Measurement distributed computing Multicore processing

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Towards Understanding Optimal Load-Balancing of Heterogeneous Short-Range Molecular Dynamics 23

Towards Understanding Optimal Load-Balancing of Heterogeneou...

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23rd Annual IEEE international conference on High Performance computing, Data, and Analytics (HiPCW)

作者： Hirschmann, Steffen Pflueger, Dirk Glass, Colin W. Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany Univ Stuttgart High Performance Comp Ctr Stuttgart Stuttgart Germany

ISBN: (纸本)9781509057733

For heterogeneous dynamic short-range molecular dynamics simulations it is critical to employ suitable loadbalancing methods to minimize the time to solution. However, designing, selecting and parametrizing the optimal loadbalancing method is a complex task which depends on detailed properties of the simulation scenario. The main challenge in balancing the load of molecular dynamics simulations is the extreme difference in load for scenarios with a heterogeneous particle density, which can easily reach 4-6 orders of magnitude. Therefore, heterogeneity is deemed to be a relevant property. In this paper, we formulate a suitable metric to reliably quantify heterogeneity. We apply this metric, which is based on the binning of particles and the evaluation of statistical moments, to example scenarios, and we correlate the results to the performance of five load balancing methods. Furthermore, we identify the load dynamics as a second relevant property. It quantifies how rapidly the load varies over time, and we introduce corresponding metrics. We show that the load dynamics can be used to determine how long the benefits of a specific partitioning are expected to last. The results indicate that these metrics are useful to differentiate between scenarios, and that they facilitate reasoning over the complex relationship between particle simulation scenarios and optimal load balancing methods. This work is a first step towards understanding this relationship, and it introduces key concepts that we regard as crucial in this process.

关键词： short-range molecular dynamics parallelization spatial decomposition distributed memory linked-cell load-balancing heterogeneity load dynamics

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Quantitative Performance Evaluation of distributed Video Coding

Quantitative Performance Evaluation of Distributed Video Cod...

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international conference on Inventive computing and Informatics

作者： Vijay Kumar Kodavalla Semiconductors and Systems Division Product Engineering Services Wipro Technologies

ISBN: (纸本)9781538640326

distributed Video Coding (DVC) is evolving as next generation video coding method for certain emerging applications and services including mobile video, wireless camera, wireless surveillance networks etc. On the contrary to its predecessor standards such as H.264/5, MPEG4, VC1, VP6/7/8/9 and AVS1/2;DVC is designed to have low complex encoder, even at the expense of high complex decoder. Though concept was developed way back in 2002 by Stanford and Berkeley Universities and further research has been carried out, DVC has not yet reached a state, at which it can be practically deployed. This paper highlights quantitative performance evaluation of DVC codec and its major bottlenecks, which are potential show stoppers for practical deployment.

关键词： DVC DSC WZ GOP A VC MPEG H.264 VC1 VP AVS QP rd RALDPC DCT DISCUS PRISM DISCOVER VISNET II NoE PSNR

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Proceedings - 23rd IEEE international conference on High Performance computing, HiPC 2016

Proceedings - 23rd IEEE International Conference on High Per...

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23rd IEEE international conference on High Performance computing, HiPC 2016

ISBN: (纸本)9781509054114

The proceedings contain 40 papers. The topics discussed include: fault tolerant frequent pattern mining;parallel performance-energy predictive modeling of browsers: case study of servo;optimization of brain mobile interface applications using IoT;Mizan-RMA: accelerating Mizan graph processing framework with MPI RMA;CUDA M3: designing efficient CUDA managed memory-aware MPI by exploiting GDR and IPC;parallel implementation of lossy data compression for temporal data sets;scalable parallel algorithms for shared nearest neighbor clustering;DCRoute: speeding up inter-datacenter traffic allocation while guaranteeing deadlines;efficient data redistribution to speedup big data analytics in large systems;MEC: the memory elasticity controller;Phoenix: memory speed HPC I/O with NVM;dynamic data layout optimization for high performance parallel I/O;read consistency in distributed database based on DMVCC;data elevator: low-contention data movement in hierarchical storage system;CMT-Bone - A Proxy application for compressible multiphase turbulent flows;tensor contractions with extended BLAS kernels on CPU and GPU;memory-efficient parallel simulation of electron beam dynamics using GPUs;cache-friendly design for complex spatially-variable coefficient stencils on many-core architectures;using message logs and resource use data for cluster failure diagnosis;and PRESAGE: protecting structured address generation against soft errors.

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End-User Composition of Graphical User Interfaces for PalCom systems

End-User Composition of Graphical User Interfaces for PalCom...

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11th international conference on Future Networks and Communications (FNC) / 13th international conference on Mobile systems and Pervasive computing (MobiSPC)

作者： Johnsson, Bjoern A. Weibull, Gunnar Lund Univ Dept Comp Sci Ole Romers Vag 3 SE-22363 Lund Sweden

In ubiquitous computing, end-user composition allows users to combine multiple single-purpose devices into new, interesting constellations. In PalCom - a ubiquitous middleware - this is achieved without the need to write program code. In this paper we present a solution that in the same way allows users to create Graphical User Interfaces (GUIs) for such systems without coding. The approach is to focus on presenting functionality in a GUI rather than attaching functionality to manually added components. We see this as an inverted way of working with GUI development. The solution was realized in the form of a graphical editor for a PalCom specific User Interface Description Language. The tool produces platform independent GUI descriptions that can be interpreted on any platform. When compared to another common tool, the presented editor exhibited roughly 10x shorter development times. The learning time for new users was also evaluated with positive outcome, and a scalability evaluation showed that the solution can be used to create professional grade GUIs. (C) 2016 Published by Elsevier B.V.

关键词： end-user composition graphical user interfaces middleware development tools PalCom ubiquitous computing distributed systems

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