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Parameterized Verification of GPU Kernel Programs

Parameterized Verification of GPU Kernel Programs

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Guodong Li Ganesh Gopalakrishnan Fujitsu Laboratory of America Inc. Sunnyvale CA USA School of Computing University of Utah Salt Lake UT USA

ISBN: (纸本)9781467309745

We present an automated symbolic verifier for checking the functional correctness of GPGPU kernels parametrically, for an arbitrary number of threads. Our tool checks the functional equivalence of a kernel and its optimized versions, helping debug errors introduced during memory coalescing and bank conflict elimination related optimizations. Key features of our work include: (1) a symbolic method to encode a comparative assertion across two kernel versions, and (2) techniques to overcome SMT solver restrictions through over-approximations, yielding an efficient bug-hunting method.

关键词： Kernel Instruction sets Graphics processing unit Arrays Optimization Computer bugs Schedules

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Research of Context Situation Awareness Technology

Research of Context Situation Awareness Technology

引用

IEEE international symposium on Object/Component/Service-Oriented Real-Time distributed computing Workshops (ISORCW)

作者： Dianxi Shi Zhendong Wu Bo Ding Huining Yan National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

Context situation, which means a snapshot of the status of the real world, is formed by integrating a large amount of contexts collected from various resources. How to get the context situation and use the situation to provide better services is a challenging issue. In this paper, we focused on this challenge on the basis of the mobile cloud computing architecture. An abstract model is proposed in this paper to uniformly collect the context and send them to cloud. A rule-based large-scale context aggregation algorithm is also proposed which utilizes the MapReduce computing paradigm. Finally, a large-scale context management framework based on the abstract model and the context aggregation algorithm is proposed, and a real-time traffic demo is implemented to verify the validity of the framework.

关键词： Context Mobile handsets Real time systems Sensors Servers Mobile communication Context modeling

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Improving the Performance of Dynamical Simulations Via Multiple Right-Hand Sides

Improving the Performance of Dynamical Simulations Via Multi...

引用

international symposium on parallel and distributed Processing (IPDPS)

作者： Xing Liu Edmond Chow Karthikeyan Vaidyanathan Mikhail Smelyanskiy School of Computational Science and Engineering College of Computing Georgia Institute of Technology Atlanta GA USA Parallel Computing Laboratory Intel Corporation Santa Clara CA USA

This paper presents an algorithmic approach for improving the performance of many types of stochastic dynamical simulations. The approach is to redesign existing algorithms that use sparse matrix-vector products (SPMV) with single vectors to instead use a more efficient kernel, the generalized SPMV (GSPMV), which computes with multiple vectors simultaneously. In this paper, we show how to redesign a dynamical simulation to exploit GSPMV in way that is not initially obvious because only one vector is available at a time. We study the performance of GSPMV as a function of the number of vectors, and demonstrate the use of GSPMV in the Stokesian dynamics method for the simulation of the motion of macromolecules in the cell. Specifically, for our application, we find that with modern multicore Intel microprocessors in clusters of up to 64 nodes, we can typically multiply by 8 to 16 vectors in only twice the time required to multiply by a single vector. After redesigning the Stokesian dynamics algorithm to exploit GSPMV, we measure a 30 percent speedup in performance in single-node, data parallel simulations.

关键词： Vectors Sparse matrices Heuristic algorithms Computational modeling Mathematical model Equations Kernel

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Middleware Support for RDMA-based Data Transfer in Cloud computing

Middleware Support for RDMA-based Data Transfer in Cloud Com...

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Yufei Ren Tan Li Dantong Yu Shudong Jin Thomas Robertazzi Department of Electrical and Computer Engineering Stony Brook University Stony Brook NY USA

ISBN: (纸本)9781467309745

Providing high-speed data transfer is vital to various data-intensive applications in cloud computing systems. We design a middleware layer of high-speed communication based on Remote Direct Memory Access (RDMA) that serves as the common substrate to accelerate various data transfer applications in cloud computing, such as FTP, HTTP, file copy, sync and remote file I/O. This middleware offers higher end-to-end bandwidth than the traditional TCP-based alternatives, while it hides the heterogeneity of the underlying high-speed architecture. In this paper, we describe the design of this middleware, including resource abstraction, and task synchronization and scheduling. We provide a reference implementation of the file-transfer protocol over this RDMA based middleware. Our experimental results show that it outperforms several TCP-based FTP tools, such as GridFTP, while maintaining very low CPU consumption on a variety of platforms. Furthermore, these results confirm that our middleware achieves near line-speed performance in both LAN and MAN, and scales consistently from 10Gbps Ethernet to 40Gbps Ethernet and InfiniBand environments.

关键词： Middleware Protocols Servers Semantics Message systems Computer architecture

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Autonomous and Energy-Aware Management of Large-Scale Cloud Infrastructures

Autonomous and Energy-Aware Management of Large-Scale Cloud ...

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Eugen Feller Christine Morin INRIA Centre Rennes-Bretagne Atlantique Rennes France

ISBN: (纸本)9781467309745

With the advent of cloud computing and the need for increasing amount of computing power, cloud infrastructure providers are now facilitating the deployment of large-scale data centers. In order to efficiently manage such environments three important properties have to be fulfilled by their resource management frameworks: (1) scalability, (2) autonomy (i.e. self-organization and healing), (3) energy-awareness. However, existing open-source cloud management stacks (e.g. Eucalyptus, Nimbus, Open Nebula, Open Stack) have a high degree of centralization and limited power management support. In this context, this PhD thesis focuses on more scalable, autonomic, and energy-aware resource management frameworks for large-scale cloud infrastructures. Particularly, a novel virtual machine (VM) management system based on a self-organizing hierarchical architecture called Snooze is proposed. In order to conserve energy, Snooze automatically transitions idle servers into a low-power mode (e.g. suspend). To favor idle times the system integrates a nature-inspired VM consolidation algorithm based on the Ant Colony Optimization (ACO).

关键词： Cloud computing Heart beat Scalability Fault tolerance Fault tolerant systems Scheduling Monitoring

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Online Scheduling for Cloud computing and Different Service Levels

Online Scheduling for Cloud Computing and Different Service ...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Uwe Schwiegelshohn Andrei Tchernykh Robotics Research Institute Technical University Dortmund Dortmund Germany Computer Science Department CICESE Research Center Ensenada Baja California Mexico

ISBN: (纸本)9781467309745

In this paper, we address scheduling problems for infrastructure as a service (IaaS). In a typical IaaS scenario, an infrastructure provider offers his resources on demand and with different service levels to his customers. These service levels are mainly distinguished by the amount of computing power a customer is guaranteed to receive within a time frame. In our a model, each service level is described by a slack factor and a price for a processing time unit. If the provider accepts a job it is guaranteed to complete by its deadline, that is its submission time plus its processing time times the slack factor of assigned service level. After a job has been submitted, the provider must decide immediately and irrevocably whether he accepts or rejects the job. We suggest various algorithms and use competitive analysis to discuss different scenarios for this model. These scenarios combine fixed services levels with the single machine model or the parallel identical machines model. Particularly, we demonstrate the benefit of parallelism by showing that we can achieve better competitive factor in a parallel machine scenario than in the corresponding single machine scenario.

关键词： Silicon Scheduling Processor scheduling Real time systems Upper bound Computational modeling Schedules

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A Combined Dual-stage Framework for Robust Scheduling of Scientific Applications in Heterogeneous Environments with Uncertain Availability

A Combined Dual-stage Framework for Robust Scheduling of Sci...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Florina M. Ciorba Timothy Hansen Srishti Srivastava Ioana Banicescu Anthony A. Maciejewski Howard Jay Siegel Center for Information Services and High Performance Computing Technische Universität Dresden Dresden Germany Department of Electrical and Computer Engineering Colorado State University Fort Collins CO USA Department of Computer Science and Engineering Mississippi State University Mississippi MS USA Department of Electrical and Computer Engineering Department of Computer Science Colorado State University Fort Collins CO USA

Scheduling parallel applications on existing or emerging computing platforms is challenging, and, among other attributes, must be efficient and robust. A dual-stage framework is proposed in this paper to evaluate the robustness of efficient resource allocation and dynamic load balancing of scientific applications in heterogeneous computing environments with uncertain availability. The first stage employs robust resource allocation heuristics, while the second stage incorporates robust dynamic loop scheduling techniques. The combined dual-stage framework constitutes a comprehensive framework that enables and provides guarantees for the robust execution of scientific applications in computing systems where uncertainty is caused by various unpredictable perturbations. The paper reports on studies for determining the best techniques to be used for each stage that: (a) maximize the probability that the system make span satisfies a deadline, and (b) minimize the system make span for every given availability level in the system. The usefulness and benefits of the proposed framework are demonstrated via a small scale example.

关键词： Availability Robustness Resource management Uncertainty Runtime Dynamic scheduling Program processors

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Dynamic Load Scheduling on CPU-GPU for Iterative Tomographic Reconstruction

Dynamic Load Scheduling on CPU-GPU for Iterative Tomographic...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： J.I. Agulleiro F. V'zquez E.M. Garzón J.J. Fern'ndez Supercomputing and Algorithms Group Associated Unit CSIC-UAL Agrifood Campus of International Excellence (ceiA3) University of Almería Almeria Spain Supercomputing and Algorithms Group Agrifood Campus of International Excellence (ceiA3) Almería Spain

This work presents a hybrid computing approach which combines GPUs and multicore processors to fully take advantage of the computing power latent in modern computers. It also presents its application to the problem of tomographic reconstruction. One inherent characteristic of these modern platforms is their heterogeneity, which raises the issue of workload distribution among the different processing elements. Adaptive load balancing techniques are thus necessary to properly adjust the amount of work to be done by each computing element. Here, we have chosen the 'on-demand' strategy, a well-known technique in the HPC field by which the different elements asynchronously request a piece of work when they become idle, thereby keeping the system fairly well balanced. The results show that our scheme accommodates to the heterogeneous platform where it runs as it assigns more work to the faster processing elements automatically, which allows to correctly exploit all the resources available and to get complete reconstructions in less time than pure CPU or GPU approaches.

关键词： Graphics processing unit Multicore processing Central Processing Unit Slabs Computers Instruction sets

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A FAULT TOLERANCE SCHEDULING ALGORITHM FOR parallel TERRAIN ANALYSIS

A FAULT TOLERANCE SCHEDULING ALGORITHM FOR PARALLEL TERRAIN ...

引用

The Eleventh international symposium on distributed computing and Applications to Business,Engineering and Science(DCABES2012)(第十一届分布式计算及其应用国际学术研讨会)(原名:电子商务、工程及科学领域的分布式计算和应用国际学术研讨会)

作者： Xiaodong Song Kejian Qian Guoan Tang Wanfeng Dou Kun Yang Key Laboratory of Virtual Geographic Environment of Ministry of Education Nanjing Normal University School of Computer Science and Technology Nanjing Normal UniversityNanjingChina

In recent years,due to the increasing calculation demands for the massive spatial data analysis,the parallel computing based on high-performance computers has become an inevitable trend of Digital Terrain Analysis(DTA in short).A key problem is to design fault-tolerant software to enhance the stability and robustness of scientific *** paper presents an approach of failure recovery solution in distributed parallel computing *** approach to schedule the failing task is built by dividing all the failing data into several partitions according the calculating scale of *** means of the Fault-Tolerant Granularity Model,the scheduling algorithm can assign the failing task ***,two experiments focusing data size and failure size are discussed.

关键词： DEM Digital Terrain Analysis parallel computing Fault Tolerance

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Towards the Scalability of Dynamic Loop Scheduling Techniques via Discrete Event Simulation

Towards the Scalability of Dynamic Loop Scheduling Technique...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Mahadevan Balasubramaniam Nitin Sukhija Florina M. Ciorba Ioana Banicescu Srishti Srivastava Dept. of Computer Science & Engineering Mississippi State University Mississippi USA Center for Information Services and High Performance Computing (ZIH) Technische Universität Dresden Germany

ISBN: (纸本)9781467309745

To improve their performance, scientific applications often use loop scheduling algorithms as techniques for load balancing data parallel computations. Over the years, a number of dynamic loop scheduling (DLS) techniques have been developed. These techniques are based on probabilistic analyses, and are effective in addressing unpredictable load imbalances in the system arising from various sources, such as, variations in application, algorithmic, and systemic characteristics. Modern, high-end computing facilities can now offer petascale performance (10 15 flops), and several initiatives have already begun with the goal of achieving exascale performance (10 18 flops) towards the end of the current decade. Efficient and scalable algorithms are therefore required to utilize the petascale and exascale resources. In this paper, a study of the scalability of DLS techniques via discrete event simulation is presented, both in terms of number of processors, and problem size. To facilitate the scalability study, a dynamic loop scheduler was designed and was implemented using the SimGrid simulation framework. The results of the study demonstrate the scalability of the DLS techniques and their effectiveness in addressing load imbalance in large scale computing systems.

关键词： Program processors Scalability Dynamic scheduling Availability Computational modeling Scheduling algorithms

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