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Petascale Application of a Coupled CPU-GPU Algorithm for Simulation and Analysis of Multiphase Flow Solutions in Porous Medium Systems

Petascale Application of a Coupled CPU-GPU Algorithm for Sim...

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IEEE 28th international parallel & distributed Processing symposium (IPDPS)

作者： McClure, James E. Wang, Hao Prins, Jan F. Miller, Cass T. Feng, Wu-Chun Virginia Tech Adv Res Comp Blacksburg VA 24061 USA Univ N Carolina Dept Comp Sci Chapel Hill NC 27514 USA Univ N Carolina Dept Environm Sci & Engn Chapel Hill NC 27515 USA Virginia Tech Dept Comp Sci Blacksburg VA USA

ISBN: (纸本)9780769552071

Large-scale simulation can provide a wide range of information needed to develop and validate theoretical models for multiphase flow in porous medium systems. In this paper, we consider a coupled solution in which a multiphase flow simulator is coupled to an analysis approach used to extract the interfacial geometries as the flow evolves. this has been implemented using MPI to target heterogeneous nodes equipped with GPUs. the GPUs evolve the multiphase flow solution using the lattice Boltzmann method while the CPUs compute upscaled measures of the morphology and topology of the phase distributions and their rate of evolution. Our approach is demonstrated to scale to 4,096 GPUs and 65,536 CPU cores to achieve a maximum performance of 244,754 million-lattice-node updates per second (MLUPS) in double precision execution on Titan. In turn, this approach increases the size of systems that can be considered by an order of magnitude compared with previous work and enables detailed in situ tracking of averaged flow quantities at temporal resolutions that were previously impossible. Furthermore, it virtually eliminates the need for post-processing and intensive I/O and mitigates the potential loss of data associated with node failures.

关键词： CUDA Heterogeneous computing parallel computing GPGPU Lattice Boltzmann Method

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MC-SkyNet: Mobile-cloud dynamic partitioning for mobile cloud applications 19

MC-SkyNet: Mobile-cloud dynamic partitioning for mobile clou...

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ISCC 2014 Workshop - 5th IEEE international Workshop on Performance Evaluation of Communications in distributed Systems and Web based Service Architectures, PEDISWESA 2014

作者： Bayat, Navid Lutfiyya, Hanan Department of Computer Science University of Western Ontario Canada

ISBN: (纸本)9781479942770

Mobile devices have limited resources including short battery life, storage capacity and processor performance. this limits the applications that can run on it. Mobile applications can be partitioned so that some of the application runs on a cloud. this works well for applications with relatively little data to be transferred and that do not have a high level of interactiveness with the user. Challenges with applications that have large amounts of data to be transferred and have a high level interactiveness is the high latency incurred by the wired network and packet loss of the wireless network. A cloudlet is a resource-rich computer or cluster of computers that is connected to the Internet and is available for use by nearby mobile devices. A mobile application can be partitioned so that part of it runs on the cloudlet. In this paper, we present the MC-Skynet framework which introduces fine-grained offloading approach and support for runtime and dynamic partitioning of an application. this is different from previous approaches, in that MC-Skynet not only provides dynamic partitioning and offloading, but is also adaptive to the changes of the state of a cloudlet by introducing a cloudlet mesh network and self-learningg decision making module to estimate the offloading cost. © 2014 IEEE.

关键词： Mobile computing

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High-level dataflow programming for reconfigurable computing 26

High-level dataflow programming for reconfigurable computing

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26th IEEE international symposium on Computer Architecture and High Performance computing Workshops, SBAC-PADW 2014

作者： Sérot, J. Berry, F. Institut Pascal Université Blaise Pascal / CNRS Clermont-Ferrand France

ISBN: (纸本)9781479970148

In many application domains, FPGAS are now promoted as a way of getting round the restrictions of specific CPU designs on system scalability. However, in the current state-of-the art, programming FPGAS remains essentially a hardware-oriented activity, relying on dedicated hardware description languages such as VHDL or Verilog. Using these languages requires expertise in digital design and in practice this limits the applicability of FPGA-based solutions. this is particulary true for stream-processing applications, in which some processing must be carried out "on the fly" on digital data streams. In this context, the dataflow programming model offers a very effective way to reduce the gap between high-level formulations and low-level implementations. To support this claim, the authors have recently introduced CAPH, a domain specific language, offering a fully-automated compilation path from high-level dataflow descriptions to FPGA configuration for stream-processing applications. this paper is a introduction to the CAPH language, giving its motivations and main design principles and exposing the basic features of its syntax, semantics and compilation. It also points to experimental results showing that, at least for stream-processing applications, the dataflow model of computation, used jointly as a programming model and an execution model, can offer a very effective way to conciliate abstraction and efficiency when programming FPGAS. © 2014 IEEE.

关键词： Computer hardware description languages

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Stack-tagged dataflow 26

Stack-tagged dataflow

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26th IEEE international symposium on Computer Architecture and High Performance computing Workshops, SBAC-PADW 2014

作者： Santiago, Leandro Marzulo, Leandro A.J. Goldstein, Brunno F. Alves, Tiago A.O. França, Felipe M.G. Dep. de Informática e Ciência da Computação Instituto de Matemática e Estatística - IMe Universidade Do Estado Do Rio de Janeiro - UERJ Rio de Janeiro Brazil Programa de Engenharia de Sistemas e Computação COPPE Universidade Federal Do Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9781479970148

Dynamic Dataflow allows simultaneous execution of instructions in different iterations of a loop, boosting parallelism exploitation. In this model, operands are tagged with their associated instance number, incremented as they go through the loop. Instruction execution is triggered when all input operand with the same tag become available. However, this traditional tagging mechanism often requires the generation of several control instructions to manipulate tags and guarantee the correct matching. To address this problem, this work presents Stack- Tagged Dataflow, a tagging mechanism that uses stacks of tags to reduce control overheads in dataflow, while isolating context from different nested loops. Push instructions at the beginning of loops will create a new context, while Pop instructions, at the end of the loop, will restore the original context tags. Experimental results show that Stack-Tagged Dataflow is a viable solution, suggesting that a hybrid compiling approach can be used to enable Stack- Tags only when needed. © 2014 IEEE.

关键词： data flow computing program control structures context isolation dataflow control overhead reduction nested loops pop instructions push instructions stack-tagged dataflow tagging mechanism Assembly Computer architecture Context Libraries parallel processing Tagging Virtual machining Dataflow parallel Programming Stack Tag data flow TAGGING program control structures virtual machines parallel programming Computer Architecture Furnace conveyors parallel PROCESSING (COMPUTERS) Loops Libraries assemblies NESTED LOOPS virtual machining instruction

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A Framework for Lattice QCD Calculations on GPUs

A Framework for Lattice QCD Calculations on GPUs

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IEEE 28th international parallel & distributed Processing symposium (IPDPS)

作者： Winter, F. T. Clark, M. A. Edwards, R. G. Joo, B. Thomas Jefferson Natl Accelerator Facil Newport News VA 23606 USA NVIDIA Corp Santa Clara CA 95050 USA

ISBN: (纸本)9780769552071

computing platforms equipped with accelerators like GPUs have proven to provide great computational power. However, exploiting such platforms for existing scientific applications is not a trivial task. Current GPU programming frameworks such as CUDA C/C++ require low-level programming from the developer in order to achieve high performance code. As a result porting of applications to GPUs is typically limited to time-dominant algorithms and routines, leaving the remainder not accelerated which can open a serious Amdahl's law issue. the Lattice QCD application Chroma allows us to explore a different porting strategy. the layered structure of the software architecture logically separates the data-parallel from the application layer. the QCD Data-parallel software layer provides data types and expressions with stencil-like operations suitable for lattice field theory. Chroma implements algorithms in terms of this high-level interface. thus by porting the low-level layer one effectively ports the whole application layer in one swing. the QDP-JIT/PTX library, our reimplementation of the low-level layer, provides a framework for Lattice QCD calculations for the CUDA architecture. the complete software interface is supported and thus applications can be run unaltered on GPU-based parallel computers. this reimplementation was possible due to the availability of a JIT compiler which translates an assembly language (PTX) to GPU code. the existing expression templates enabled us to employ compile-time computations in order to build code generators and to automate the memory management for CUDA. Our implementation has allowed us to deploy the full Chroma gauge-generation program on large scale GPU-based machines such as Titan and Blue Waters and accelerate the calculation by more than an order of magnitude.

关键词： Application framework C++ CUDA GPU JIT Lattice QCD PTX Lattice QCD GRAPPER PICK UP Graphics Processing Unit Application Framework just in time compiler data parallel application layer Store management LOW LEVEL Code generation Chroma Frameworks software interface

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distributed Virtual Network Embedding with Bloom Filter

Distributed Virtual Network Embedding with Bloom Filter

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IEEE international Conference on Smart City

作者： Sude Qing Min Feng Hua Jin China Academy of Telecommunication Research State Key Lab of Networking and Switching Technology Beijing University of Posts and Telecommunications

ISBN: (纸本)9781509018949

As a disruptive technology, Network Virtualization plays an increasingly important role in Cloud computing by allowing multiple heterogeneous virtual networks to run on a shared infrastructure as independent slices. However, it is a big challenge to map multiple virtual networks onto specific nodes and links in the shared substrate network, known as the Virtual Network Embedding (VNE) problem. Most existing solutions are provided in a centralized way, which may have hot spot problem. In this paper, by leveraging the learning and inference technology, a novel VNE algorithm is proposed to achieve the embedding in a distributed way. Instead of flooding, Bloom Filter is introduced to synchronize the mapping information, without generating massive communication overhead. the results of comparison between existing algorithms and the proposed algorithm show that our distributed algorithm has acceptable, even better performance in terms of long-term average revenue and acceptance ratio.

关键词： Network Virtualization Cloud Comuputing Virtual Network Embedding Learning & Inference Bloom Filter

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Bursting the Cloud Data Bubble: Towards Transparent Storage Elasticity in IaaS Clouds

Bursting the Cloud Data Bubble: Towards Transparent Storage ...

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IEEE 28th international parallel & distributed Processing symposium (IPDPS)

作者： Nicolae, Bogdan Riteau, Pierre Keahey, Kate IBM Res Dublin Ireland Univ Chicago Chicago IL 60637 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9780769552071

Storage elasticity on IaaS clouds is an important feature for data-intensive workloads: storage requirements can vary greatly during application runtime, making worst-case over-provisioning a poor choice that leads to unnecessarily tied-up storage and extra costs for the user. While the ability to adapt dynamically to storage requirements is thus attractive, how to implement it is not well understood. Current approaches simply rely on users to attach and detach virtual disks to the virtual machine (VM) instances and then manage them manually, thus greatly increasing application complexity while reducing cost efficiency. Unlike such approaches, this paper aims to provide a transparent solution that presents a unified storage space to the VM in the form of a regular POSIX file system that hides the details of attaching and detaching virtual disks by handling those actions transparently based on dynamic application requirements. the main difficulty in this context is to understand the intent of the application and regulate the available storage in order to avoid running out of space while minimizing the performance overhead of doing so. To this end, we propose a storage space prediction scheme that analyzes multiple system parameters and dynamically adapts monitoring based on the intensity of the I/O in order to get as close as possible to the real usage. We show the value of our proposal over static worst-case over-provisioning and simpler elastic schemes that rely on a reactive model to attach and detach virtual disks, using both synthetic benchmarks and real-life data-intensive applications. Our experiments demonstrate that we can reduce storage waste/cost by 30-40% with only 2-5% performance overhead.

关键词： cloud computing elastic storage adaptive resizing I/O access pattern prediction

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Exploratory analysis of raw data files through dataflows 26

Exploratory analysis of raw data files through dataflows

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26th IEEE international symposium on Computer Architecture and High Performance computing Workshops, SBAC-PADW 2014

作者： Silva, Vitor De Oliveira, Daniel Mattoso, Marta Computer Science - COPPE Federal University of Rio de Janeiro Rio de Janeiro Brazil Institute of Computing Fluminense Federal University Niteroi Brazil

ISBN: (纸本)9781479970148

Scientific applications generate raw data files in very large scale. Most of these files follow a standard format established by the domain area application, like HDF5, NetCDF and FITS. these formats are supported by a variety of programming languages, libraries and programs. Since they are in large scale, analyzing these files require writing a specific program. Generic data analysis systems like database management systems (DBMS) are not suited because of data loading and data transformation in large scale. Recently there have been several proposals for indexing and querying raw data files without the overhead of using a DBMS, such as noDB, RAW and FastBit. their goal is to offer query support to the raw data file after a scientific program has generated it. However, these solutions are focused on the analysis of one single large file. When a large number of files are all related and required to the evaluation of one scientific hypothesis, the relationships must be managed manually or by writing specific programs. the proposed approach takes advantage of existing provenance data support from Scientific Workflow Management Systems (SWfMS). When scientific applications are managed by SWfMS, the data is registered along the provenance database at runtime. therefore, this provenance data may act as a description of theses files. When the SWfMS is dataflow aware, it registers domain data all in the same database. this resulting database becomes an important access method to the large number of files that are generated by the scientific workflow execution. this becomes a complementary approach to the single raw data file analysis support. In this work, we present our dataflow approach for analyzing data from several raw data files and evaluate it with the Montage application from the astronomy domain. © 2014 IEEE.

关键词： Data handling

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Multi-threading Based Map Reduce Tasks Scheduling

Multi-Threading Based Map Reduce Tasks Scheduling

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5th international Conference on Information and Communication Systems (ICICS)

作者： Althebyan, Qutaibah Alqudah, Omar Jararweh, Yaser Yaseen, Qussai Jordan Univ Sci & Technol Software Engn Dept Irbid Jordan Jordan Univ Sci & Technol Dept Comp Sci Irbid Jordan Yarmouk Univ Dept Comp Sci Irbid Jordan

ISBN: (纸本)9781479930234

Map Reduce is a parallel and a distributed computing framework used to process datasets that have large scale nature on a cluster. Due to the nature of data that needs to be handled in the Map Reduce problem which involves huge amount of data, many problems came up that are of great importance. Scheduling tasks is considered one of these major problems that face Map Reduce frameworks. In this paper, we tackled this problem and proposed a new scheduling algorithm that is based on a multi-threading principle. In our proposed algorithm, we divided the cluster into multi blocks where each one of them is scheduled by a special thread. Two major factors are used to test our algorithm;the simulation time and the energy consumption. Our proposed scheduler is then compared with existing schedulers and the results showed the superiority and the preference of our proposed scheduler over the existing schedulers.

关键词： Map Reduce Schedulers Cloud computing Clustering Hadoop Scalability

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Agent-Based Fault-Tolerance Mechanism for distributed Key-Value Database

Agent-Based Fault-Tolerance Mechanism for Distributed Key-Va...

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5th international Conference on Digital Home (ICDH)

作者： Wu Hui-Jun Lu Kai Li Gen Jiang Jin-Fei Wang Shuang-Xi Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Hunan Peoples R China

ISBN: (纸本)9781479942848

distributed key-value database is widely used in Web 2.0 applications and cloud computing environments. It overcomes the weak performance and bad scalability of traditional relational database. But fault in distributed system will lead to errors, then the high performance is useless. So we should build a fault tolerance mechanism. On the other hand, in many application scenarios, transactional operations are inevitable. Some existing key-value databases utilize two-phase commit protocol or optimistic concurrency control in transaction processing. But the problems are sing-node failure and high overhead in protocol processing. Meanwhile, users' programming becomes more error-prone. this paper designs a fault tolerance and recovery mechanism on DStageDB, which is a distributed key-value database. We design an agent-based transaction processing mechanism. the transaction processing speed is improved and less user intervention is needed.

关键词： fault-tolerance key-value database agent transaction

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