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检索条件"任意字段=13th IEEE International Symposium on Parallel and Distributed Processing with Applications"

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A simulation study of the effects of multi-path approaches in e-commerce applications 06

A simulation study of the effects of multi-path approaches i...

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international symposium on parallel and distributed processing (IPDPS)

作者： P. Romano F. Quaglia B. Ciciani Dipartimento di Informatica e Sistemistica Università di Roma La Sapienza Rome Italy

ISBN: (纸本)9781424400546

Response time is a key factor of any e-commerce application, and a set of solutions have been proposed to provide low response time despite network congestions or failures. Being them mostly based on caching of Web objects and replication of DBMS managed data at the edges, or at intermediate points, of the Web infrastructure, they reveal effective when handling client requests only performing read access to application data. However, any update request typically needs to be redirected to the origin DBMSs, hence not taking advantage from data replication and related client proximity. In order to alleviate the effects of network congestions or failures, we have proposed a multi-path protocol that increases the likelihood for the update request to be processed along a responsive (e.g. failure free) network path in between the client location and the origin DBMS sites. In this paper we present an extensive simulation study of the effects of such a multi-path approach on the client perceived response time. the study relies on both Brite generated network topologies and the NLANR graph. Also, well known realistic TCP models are used to capture the effects of network delays during both normal and anomalous (i.e. packet loss affected) operation mode

关键词： Remuneration Access protocols Network topology Delay effects Electronic commerce Reliability engineering Added delay Employment Degradation Logic

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Replication-Based Load Balancing in distributed Content-Based Publish/Subscribe

Replication-Based Load Balancing in Distributed Content-Base...

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international symposium on parallel and distributed processing (IPDPS)

作者： Weixiong Rao Chao Chen Pan Hui Sasu Tarkoma School of Software Engineering Tongji University Shanghai China Department of Computer Science University of Helsinki Finland Hong Kong University of Science and Technology Kowloon HK

ISBN: (纸本)9781467360661

In recent years, content-based publish/subscribe (pub/sub) has become a popular paradigm to decouple content producers and consumers for Internet-scale content services. Many real applications show that the content workloads frequently exhibit very skewed distribution, and incur unbalanced workloads. To balance the workloads, the literature of content-based pub/sub adopted a migration scheme (Mis) to move (a subset of) subscription filters from overloaded brokers to underloaded brokers. In this way, the publications that successfully match the moved filters are then overloaded, leading to balanced workloads. Unfortunately, the scheme cannot reduce the overall matching workloads. In the worst case, suppose that all brokers suffer from heavy workloads. cannot find available brokers to offload the heavy workloads of those overloaded brokers, and fails to balance the workloads. To overcome the issue, the contribution of this paper is to develop a set of novel load balancing algorithms, namely a similarity-based replication scheme (Sir). the novelty of is that it not only balances the workloads of brokers but also reduces the overall workloads. Based on both simulation and emulation results, the extensive experiments verify that can achieve much better performance than, in terms of 43.10% higher entropy value (i.e., more balanced workloads) and 46.39% lower workloads.

关键词： Subscriptions Indexes Measurement distributed processing Social network services Registers Semantics

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Enabling In-situ Execution of Coupled Scientific Workflow on Multi-core Platform

Enabling In-situ Execution of Coupled Scientific Workflow on...

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international symposium on parallel and distributed processing (IPDPS)

作者： Fan Zhang Ciprian Docan Manish Parashar Scott Klasky Norbert Podhorszki Hasan Abbasi Center for Autonomic Computing Rutgers University Piscataway NJ USA Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (纸本)9781467309752

Emerging scientific application workflows are composed of heterogeneous coupled component applications that simulate different aspects of the physical phenomena being modeled, and that interact and exchange significant volumes of data at runtime. With the increasing performance gap between on-chip data sharing and off-chip data transfers in current systems based on multicore processors, moving large volumes of data using communication network fabric can significantly impact performance. As a result, minimizing the amount of inter-application data exchanges that are across compute nodes and use the network is critical to achieving overall application performance and system efficiency. In this paper, we investigate the in-situ execution of the coupled components of a scientific application workflow so as to maximize on-chip exchange of data. Specifically, we present a distributed data sharing and task execution framework that (1) employs data-centric task placement to map computations from the coupled applications onto processor cores so that a large portion of the data exchanges can be performed using the intra-node shared memory, (2) provides a shared space programming abstraction that supplements existing parallel programming models (e.g., message passing) with specialized one-sided asynchronous data access operators and can be used to express coordination and data exchanges between the coupled components. We also present the implementation of the framework and its experimental evaluation on the Jaguar Cray XT5 at Oak Ridge National Laboratory.

关键词： Data models Atmospheric modeling Couplings distributed databases Computational modeling Program processors Servers

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A Utility Based Power-Aware Autonomic Approach for Running Scientific applications

A Utility Based Power-Aware Autonomic Approach for Running S...

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ieee international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Rajat Mehrotra Ioana Banicescu Srishti Srivastava Department of Electrical and Computer Engineering Mississippi State University USA Department of Computer Science and Engineering NSF Center for Autonomic Computing Mississippi State University USA

ISBN: (纸本)9781467309745

Traditionally, computational power and performance of the high performance computing (HPC) systems are assumed to depend upon the number of processors and speed of supercomputers. Supercomputers run at their peak performance to efficiently execute scientific applications. therefore, these supercomputers consume enormous amount of power that results in increased operational cost. the high power consumption translates into high temperature of the physical HPC systems, which in turn results in high failure rate and decreased reliability. Employing an aggressive cooling system does not improve the situation, because it involves an additional operational and infrastructure cost. Slowing down these HPC systems results in loss of performance that is also not recommended. Moreover, the execution of scientific applications is affected adversely through the variation in available computational resources. this variation is due to the computational resource utilization by other applications running at the computing nodes. therefore, the execution of a scientific application should be monitored with respect to its performance objectives (deadline etc.). Failure in meeting the deadlines may result in high cost in terms of revenue loss to the service providers. these issues raise the motivation towards the designing of an autonomic approach for managing power consumption in HPC systems without adversely affecting to the performance of the system. In this paper, we present a utility based power-aware approach that uses a model-based control theoretic framework for executing scientific applications. the approach and related simulations indicate that the performance and the power requirements of the system can dynamically be adjusted, while maintaining the predefined quality of service (QoS) goals in terms of deadline of execution and power consumption of the HPC system, even in the presence of computational resource related perturbations. this approach is autonomic, performance dire

关键词： Program processors Power demand Processor scheduling Dynamic scheduling Quality of service Availability Job shop scheduling

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Meteor Shower: A Reliable Stream processing System for Commodity Data Centers

Meteor Shower: A Reliable Stream Processing System for Commo...

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international symposium on parallel and distributed processing (IPDPS)

作者： Huayong Wang Li-Shiuan Peh Emmanouil Koukoumidis Shao Tao Mun Choon Chan Computer Science and Artificial Intelligence Laboratory MIT USA School of Computing National University of Singapore Singapore

ISBN: (纸本)9781467309752

Large-scale failures are commonplace in commodity data centers, the major platforms for distributed Stream processing Systems (DSPSs). Yet, most DSPSs can only handle single-node failures. Here, we propose Meteor Shower, a new fault-tolerant DSPS that overcomes large-scale burst failures while improving overall performance. Meteor Shower is based on checkpoints. Unlike previous schemes, Meteor Shower orchestrates operators' check pointing activities through tokens. the tokens originate from source operators, trickle down the stream graph, triggering each operator that receives these tokens to checkpoint its own state. Meteor Shower is a suite of three new techniques: 1) source preservation, 2) parallel, asynchronous check pointing, and 3) application-aware check pointing. Source preservation allows Meteor Shower to avoid the overhead of redundant tuple saving in prior schemes, parallel, asynchronous check pointing enables Meter Shower operators to continue processing streams during a checkpoint, while application-aware check pointing lets Meteor Shower learn the changing pattern of operators' state size and initiate checkpoints only when the state size is minimal. All three techniques together enable Meteor Shower to improve throughput by 226% and lower latency by 57% vs prior state-of-the-art. Our results were measured on a prototype implementation running three real world applications in the Amazon EC2 Cloud.

关键词： Checkpointing Cameras Digital signal processing Fault tolerance Fault tolerant systems throughput

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Warp-Based Load/Store Reordering to Improve GPU Data Cache Time Predictability and Performance

Warp-Based Load/Store Reordering to Improve GPU Data Cache T...

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international symposium on Object-Oriented Real-Time distributed Computing

作者： Yijie Huangfu Wei Zhang Virginia Commonwealth University

ISBN: (纸本)9781467390330

Graphics processing Units (GPUs) have great potential to improve the performance and energy efficiency for data-parallel real-time applications. However, it is very difficult to compute worst-case execution time (WCET) for current GPUs that are design for improving the average-case throughput, not for time predictability. In this paper, we propose a warp-based load/store reordering mechanism to improve the time predictability of GPU data caching without incurring much performance overhead. this mechanism can be used in conjunction with dynamic warp scheduling to achieve better performance than a pure round-robin based scheduling while enabling accurate static timing analysis to bound the worst-case GPU L1 data cache misses.

关键词： Graphics processing units Kernel Dynamic scheduling Real-time systems Timing Out of order

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Efficient data access for parallel BLAST

Efficient data access for parallel BLAST

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international symposium on parallel and distributed processing (IPDPS)

作者： Heshan Lin Xiaosong Ma P. Chandramohan A. Geist N. Samatova Department of Computer Science North Carolina State University USA Computer Science and Mathematics Division Oak Ridge National Laboratory USA Computer Science and Mathematics Division Oak Ridge National Laboratory

Searching biological sequence databases is one of the most routine tasks in computational biology. this task is significantly hampered by the exponential growth in sequence database sizes. Recent advances in parallelization of biological sequence search applications have enabled bioinformatics researchers to utilize high-performance computing platforms and, as a result, greatly reduce the execution time of their sequence database searches. However, existing parallel sequence search tools have been focusing mostly on parallelizing the sequence alignment engine. While the computation-intensive alignment tasks become cheaper with larger machines, data-intensive initial preparation and result merging tasks become more expensive. Inefficient handling of input and output data can easily create performance bottlenecks even on supercomputers. It also causes a considerable data management overhead. In this paper, we present a set of techniques for efficient and flexible data handling in parallel sequence search applications. We demonstrate our optimizations through improving mpiBLAST, an open-source parallel BLAST tool rapidly gaining popularity. these optimization techniques aim at enabling flexible database partitioning, reducing I/O by caching small auxiliary files and results, enabling parallel I/O on shared files, and performing scalable result processing protocols. As a result, we reduce mpiBLAST users' operational overhead by removing the requirement of pre-partitioning databases. Meanwhile, our experiments show that these techniques can bring by an order of magnitude improvement to both the overall performance and scalability of mpiBLAST.

关键词： Sequences Databases Biology computing Computational biology Bioinformatics Concurrent computing Engines Merging Supercomputers Data handling

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Reuse and Refactoring of GPU Kernels to Design Complex applications

Reuse and Refactoring of GPU Kernels to Design Complex Appli...

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international symposium on parallel and distributed processing with applications, ISPA

作者： Santonu Sarkar Sayantan Mitra Ashok Srinivasan Infosys Laboratories Infosys Limited Bangalore India Department of Computer Science Florida State University Tallahassee FL USA

Developers of GPU kernels, such as FFT, linear solvers, etc, tune their code extensively in order to obtain optimal performance, making efficient use of different resources available on the GPU. Complex applications are composed of several such kernel components. the software engineering community has performed extensive research on component based design to build generic and flexible components, such that a component can be reused across diverse applications, rather than optimizing its performance. Since a GPU is used primarily to improve performance, application performance becomes a key design issue. the contribution of our work lies in extending component based design research in a new direction, dealing with the performance impact of refactoring an application consisting of the composition of highly tuned kernels. Such refactoring can make the composition more effective with respect to GPU resource usage especially when combined with suitable scheduling. Here we propose a methodology where developers of highly tuned kernels can enable application designers to optimize performance of the composition. Kernel developers characterize the performance of a kernel through its "performance signature". the application designer combines these kernels such that the performance of the refactored kernel is better than the sum of the performances of the individual kernels. this is partly based on the observation that different kernels may make unbalanced use of different GPU resources like different types of memory. Kernels may also have the potential to share data. Refactoring the kernels, combining them, and scheduling them suitably can improve performance. We study different types of potential design optimizations and evaluate their effectiveness on different types of kernels. this may even involve choosing non-optimal parameters for an individual kernel. We analyze how the performance signature of the composition changes from that of the individual kernels through our te

关键词： Kernel Graphics processing unit Instruction sets Merging Hardware Registers Design methodology

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Optimizing and Auto-Tuning Iterative Stencil Loops for GPUs with the In-Plane Method

Optimizing and Auto-Tuning Iterative Stencil Loops for GPUs ...

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international symposium on parallel and distributed processing (IPDPS)

作者： Wai Teng Tang Wen Jun Tan Ratna Krishnamoorthy Yi Wen Wong Shyh-Hao Kuo Rick Siow Mong Goh Stephen John Turner Weng-Fai Wong School of Computer Engineering Nanyang Technological University Singapore Department of Computer Science School of Computing National University of Singapore Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore

ISBN: (纸本)9781467360661

Stencils represent an important class of computations that are used in many scientific disciplines. Increasingly, many of the stencil computations in scientific applications are being offloaded to GPUs to improve running times. Since a large part of the simulation time is spent inside the stencil kernels, optimizing the kernel is therefore important in the context of achieving greater computation efficiencies and reducing simulation time. In this work, we proposed a novel in-plane method for stencil computations on GPUs and compared its performance with the conventional method implemented in the Nvidia SDK. We also implemented an auto-tuning framework for our method to select the optimal parameters for different GPU architectures. A performance model was developed for our proposed method, and is used to speed up the auto-tuning process. Our results show that a speedup of nearly 2× can be achieved compared to Nvidia's implementation.

关键词： Kernel Instruction sets Loading Graphics processing units Computer architecture Bandwidth Registers

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An Efficient Framework for Multi-dimensional Tuning of High Performance Computing applications

An Efficient Framework for Multi-dimensional Tuning of High ...

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international symposium on parallel and distributed processing (IPDPS)

作者： Guojing Cong Huifang Wen I-hsin Chung David Klepacki Hiroki Murata Yasushi Negishi IBM Thomas J. Watson Research Center Yorktown Heights NY USA IBM Tokyo Research Laboratory Yamato Japan

ISBN: (纸本)9781467309752

Deploying an application onto a target platform for high performance oftentimes demands manual tuning by experts. As machine architecture gets increasingly complex, tuning becomes even more challenging and calls for systematic approaches. In our earlier work we presented a prototype that combines efficiently expert knowledge, static analysis, and runtime observation for bottleneck detection, and employs refactoring and compiler feedback for mitigation. In this study, we develop a software tool that facilitates \emph{fast} searching of bottlenecks and effective mitigation of problems from major dimensions of computing (e.g., computation, communication, and I/O). the impact of our approach is demonstrated by the tuning of the LBMHD code and a Poisson solver code, representing traditional scientific codes, and a graph analysis code in UPC, representing emerging programming paradigms. In the experiments, our framework detects with a single run of the application intricate bottlenecks of memory access, I/O, and communication. Moreover, the automated solution implementation yields significant overall performance improvement on the target platforms. the improvement for LBMHD is up to 45\%, and the speedup for the UPC code is up to 5. these results suggest that our approach is a concrete step towards systematic tuning of high performance computing applications.

关键词： Measurement Tuning Runtime Probes Software Libraries

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