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A performance prediction model for the CUDA GPGPU platform

A performance prediction model for the CUDA GPGPU platform

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International conference on High Performance Computing

作者： Kishore Kothapalli Rishabh Mukherjee M. Suhail Rehman Suryakant Patidar P. J. Narayanan Kannan Srinathan International Institute of Information Technology Hyderabad Hyderabad India

ISBN: (纸本)9781424449224;9781424449217

The significant growth in computational power of modern Graphics Processing Units (GPUs) coupled with the advent of general purpose programming environments like NVIDIA's CUDA, has seen GPUs emerging as a very popular parallel computing platform. Till recently, there has not been a performance model for GPGPUs. The absence of such a model makes it difficult to definitively assess the suitability of the GPU for solving a particular problem and is a significant impediment to the mainstream adoption of GPUs as a massively parallel (super)computing platform. In this paper we present a performance prediction model for the CUDA GPGPU platform. This model encompasses the various facets of the GPU architecture like scheduling, memory hierarchy, and pipelining among others. We also perform experiments that demonstrate the effects of various memory access strategies. The proposed model can be used to analyze pseudo code for a CUDA kernel to obtain a performance estimate, in a way that is similar to performing asymptotic analysis. We illustrate the usage of our model and its accuracy with three case studies: matrix multiplication, list ranking, and histogram generation.

关键词： Predictive models Performance analysis Concurrent computing Graphics programming environments parallel processing Impedance Memory architecture Pipeline processing Kernel

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massively parallel feature extraction framework application in predicting dangerous seismic events

Massively parallel feature extraction framework application ...

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Federated conference on Computer Science and Information Systems (FedCSIS)

作者： Marek Grzegorowski Faculty of Mathematics Informatics and Mechanics University of Warsaw Warsaw Poland

ISBN: (纸本)9781509000463

In this paper we introduce an automated mechanism for knowledge discovery from data streams. As a part of this work, we also present a new approach to the creation of classifiers ensemble based on a wide variety of models. Furthermore, we describe an innovative, highly scalable feature extraction and selection framework designed to work with the MapReduce programming model and the application of designed framework to build an ensemble of classifiers which takes into account both the quality and the diversity of individual models. The effectiveness of the solution has been verified through a participation in an open data mining competition which concerned the problem of predicting periods of increased seismic activity causing life-threatening accidents in coal mines. The submitted solution obtained the highest AUC score of all the solutions uploaded by 106 participating research teams.

关键词： Feature extraction Data mining Time series analysis Coal mining Training Sensors Correlation

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Rekindling parallelism

Rekindling Parallelism

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IEEE International conference on Self-Adaptive and Self-Organizing Systems Workshops, SASOW

作者： Frederic Gruau Fabien Michel Laboratoire de Recherche en Informatique Université de Paris-Sud 11 France Laboratoire dInformatique de robotique et de Microélectronique de Montpellier CNRS Université Montpelher II France

Computing in parallel means performing computation simultaneously, this generates two distinct views: (1) Performance view A mean to accelerate computation using coarse grain parallelism. (2) Decentralization view A new way of programming by decentralizing massive fine grain parallelism. Researchers on massive parallel models study the programming expressiveness, i.e. new bio-inspired ways of computing such as artificial neural network or multi agent systems solving new kinds of problems, but are usually not directly concerned about high performance. In contrast, researchers on high performance tend to narrow the scope of parallel expressiveness by preserving the sequential model of computation and defining specific language constructs that can lead to parallel run-time performance for more classical parallel algorithms. We argue that parallelism will really fully blossom only when both views get unified through the achievement of a new generic computing model that, while enabling decentralized computation, also supports classical way of programming and incorporates the hardware constraints to provide parallel performance. We are working on such a generic model called self developing self mapping network. This paper first justifies the motivation for such a model, and then sketches the fundamental principles of this model.

关键词： Automata parallel processing programming Computational modeling Hardware Computer architecture Neurons

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TIDeFlow: A parallel Execution Model for High Performance Computing Programs

TIDeFlow: A Parallel Execution Model for High Performance Co...

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International conference on parallel Architecture and Compilation Techniques (PACT)

作者： Daniel Orozco University of Delaware USA

Summary form only given. The popularity of serial execution paradigms in the High Performance Computing (HPC) field greatly hinders the ability of computational scientists to develop and support massively parallel programs. Programmers are left with languages that are inadequate to express parallel constructs, being forced to take decisions that are not directly related to the programs they write. Computer architects are forced to support sequential memory semantics only because serial languages require them and operating system designers are forced to support slow synchronization operations. This poster addresses the development and execution of HPC programs in many-core architectures by introducing the Time Iterated Dependency Flow (TIDeFlow) execution model. In TIDeFlow, programmers specify the precedence relations between computations without dealing with implementation details related to synchronization or scheduling. TIDeFlow is a graph-based model inspired by dataflow: Computations in a program are expressed as actors whose dependencies are represented by arcs. TIDeFlow departs from other dataflow models (1) in that actors represent parallel loops as the basic building block of HPC programs, (2) in that arcs between actors represent dependencies of any kind (data, control or other) and (3) in that arc weights allow delaying tokens to provide support for pipelining. TIDeFlow is related to other dataflow models (an excellent survey can be found in [1]) in the idea of executing several computations into a single actor as in Macro Dataflow[2] and in allowing multiple, concurrent executions of the same actor through coloring of tokens as in Dynamic Dataflow[3]. The resulting TIDeFlow model expresses HPC programs as directed graphs with weighted nodes and weighted arcs that represent parallel loops and loop carried dependencies respectively. The model is useful to support task pipelining, task migration, and distributed control. An implementation of TIDeFlow was de

关键词： Computational modeling Runtime Scalability Computer architecture Synchronization Time domain analysis programming

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GPU Delegation: Toward a Generic Approach for Developping MABS using GPU programming 16

GPU Delegation: Toward a Generic Approach for Developping MA...

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International conference on Autonomous Agents and Multiagent Systems

作者： Emmanuel Hermellin Fabien Michel LIRMM - CNRS - University of Montpellier

ISBN: (纸本)9781510855083

Using Multi-Agent Based Simulation (MABS), computing resources requirements often limit the extent to which a model could be experimented. As the number of agents and the size of the environment are constantly growing in these simulations, using General-Purpose Computing on Graphics Units (GPGPU) appears to be very promising as it allows to use the massively parallel architecture of the GPU (Graphics Processing Unit) to do High Performance Computing (HPC). Considering the use of GPGPU for developing MABS, the conclusions of Perumalla and Aaby's work [25] in 2008 was twofold: (1) data parallel execution capabilities of GPU can be used effectively in MABS and afford excellent speedup on models and (2) effective use of data parallel execution requires resolution of modeling and execution challenges at the cost of a decrease in modularity, ease of programmability and reusability. In this paper, we propose to study through experiments if the conclusions and issues outlined by Perumalla and Aaby are still true despite the evolution of GPGPU and MABS. To this end, we use the GPU environmental delegation principle on four models in order to compare CPU and GPU implementations. Then, we discuss and analyze the results from both a conceptual and a performance point of view.

关键词： MABS GPGPU CUDA Hybrid architecture Graphics Processing Unit composite structures GRAPPER PICK UP Legal Executions delegation maintenance data data parallel programmability

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Application Modeling for Scalable Simulation of massively parallel Systems

Application Modeling for Scalable Simulation of Massively Pa...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Eric Anger Damian Dechev Gilbert Hendry Jeremiah Wilke Sudhakar Yalamanchili Georgia Institute of Technology Atlanta Georgia University of Central Florida Orlando Florida Sandia National Laboratories Livermore California

Macro-scale simulation has been advanced as one tool for application -- architecture co-design to express operation of exascale systems. These simulations approximate the behavior of system components, trading off accuracy for increased evaluation speed. Application skeletons serve as the vehicle for these simulations, but they require accurately capturing the execution behavior of computation. The complexity of application codes, the heterogeneity of the platforms, and the increasing importance of simulating multiple performance metrics (e.g., execution time, energy) require new modeling techniques. We propose flexible statistical models to increase the fidelity of application simulation at scale. We present performance model validation for several exascale mini-applications that leverage a variety of parallel programming frameworks targeting heterogeneous architectures for both time and energy performance metrics. When paired with these statistical models, application skeletons were simulated on average 12.5 times faster than the original application incurring only 6.08% error, which is 12.5% faster and 33.7% more accurate than baseline models.

关键词： Computational modeling Skeleton Analytical models Load modeling Hardware Predictive models Data models

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Multi-science applications with single codebase — GAMER — For massively parallel architectures

Multi-science applications with single codebase — GAMER — ...

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Supercomputing conference

作者： Hemant Shukla Tak-Pong Woo Hsi-Yu Schive Tzihong Chiueh Lawrence Berkeley National laboratory Berkeley USA Department of Physics National Taiwan University Taipei Taiwan Department of Physics Soochow University Taipei Taiwan

The growing need for power efficient extreme-scale high-performance computing (HPC) coupled with plateauing clock-speeds is driving the emergence of massively parallel compute architectures. Tens to many hundreds of cores are increasingly made available as compute units, either as the integral part of the main processor or as coprocessors designed for handling massively parallel workloads. In the case of many-core graphics processing units (GPUs) hundreds of SIMD cores primarily designed for image and video rendering are used for high-performance scientific computations. The new architectures typically offer ANSI standard programming models such as CUDA (NVIDIA) and OpenCL. However, the wide-ranging adoption of these parallel architectures is steeped in difficult learning curve and requires reengineering of existing applications that mostly leads to expensive and error prone code rewrites without prior guarantee and knowledge of any speedups. Broad range of complex scientific applications across many domains use common algorithms and techniques, such as adaptive mesh refinements (AMR), advanced hydrodynamics partial differential equation (PDE) solvers, Poisson-Gravity solvers etc, that have demonstrably performed highly efficiently on GPU based systems. Taking advantage of the commonalities, we use GPU-aware AMR code, GAMER [1], to examine the unique approach of solving multi-science problems in astrophysics, hydrodynamics and particle physics with single codebase. We demonstrate significant speedups in disparate class of scientific applications on 3 separate clusters, viz., Dirac, Laohu and Mole 8.5. By extensively reusing the extendable single codebase we mitigate the impediments of significant code rewrites. We also collect performance and energy consumption benchmark metrics on 50-nodes NVIDIA C2050 GPU and Intel 8-core Nehalem CPU on Dirac cluster at the National Energy Research Supercomputing Center (NERSC). In addition, we propose a strategy and framework for

关键词： Graphics processing unit Kernel Computational modeling Instruction sets Mathematical model Memory management

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XTest: A parallel Multilingual Corpus with Test Cases for Code Translation and Its Evaluation*

XTest: A Parallel Multilingual Corpus with Test Cases for Co...

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International conference on Computer and Information Technology (ICCIT)

作者： Israt Jahan Rithy Hasib Hossain Shakil Niloy Mondal Fatema Sultana Faisal Muhammad Shah Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

The advancement of artificial intelligence has significantly improved the automated generation, summarization and translation of source code. Researchers are working together to improve the automated coding tasks to bring the next generation of programming systems. Currently there are few corpora available for automated code generation, summarization and translation tasks but these corpora and models are limited within common languages like C, C++, Python, Java, etc. Also none of the existing corpus contains appropriate test cases to evaluate a program properly. This paper introduces XTest: A parallel Multilingual Corpus with Test Cases for Code Translation. Our dataset contains parallel programs in 9 languages, Problem statement and test cases. Also in this work, we have built 30 systems to translate code between some high-resourced(C++, Python, etc) and low-resourced (Go, Ruby, etc) programming languages.

关键词： Java Codes Source coding Computational modeling programming Software Task analysis

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Periodic steady state determination of power systems using graphics processing units

Periodic steady state determination of power systems using g...

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International conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： Ernesto Magaña-Lemus Aurelio Medina-Ríos Antonio Ramos-Paz Víctor H. Montesinos-González Facultad de Ingeniería Eléctrica Universidad Michoacana de San Nicolás de Hidalgo (UMSNH) Morelia Michoacán México

Modern advances in computer technology will definitely have a great impact on the methodologies used in the expansion and operational planning of power systems. parallel and distributed systems are the new tendencies in technologies that present a great potential for application in these areas. Graphics processing units (GPUs) have recently become popular in several fields of science due to their capability of performing massively large computational tasks. The use of a GPU for efficiently obtaining the periodic steady state solution in the time domain of large-scale electric power systems with nonlinear components is presented in this paper. Two programming models of the numerical differentiation process are proposed and implemented on a single GPU. The computation time of the simulation performed by co-processing GPU-CPU has been compared with the simulation time required when using a conventional CPU. Several test cases have been analyzed to demonstrate the acceleration of the GPU-CPU simulation and the speed-up achieved.

关键词： Graphics processing units Instruction sets parallel processing Steady-state Power systems programming Computational modeling

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SCGPSim: A fast SystemC simulator on GPUs 10

SCGPSim: A fast SystemC simulator on GPUs

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Asia and South Pacific Design Automation conference

作者： Mahesh Nanjundappa Hiren D. Patel Bijoy A. Jose Sandeep K. Shukla FERMAT Lab Virginia Polytechnic Institute and State University Blacksburg VA USA Department of ECE University of Waterloo Waterloo ONT Canada

ISBN: (纸本)9781605588377

The main objective of this paper is to speed up the simulation performance of SystemC designs at the RTL abstraction level by exploiting the high degree of parallelism afforded by today's general purpose graphics processors (GPGPUs). Our approach parallelizes SystemC's discrete-event simulation (DES) on GPGPUs by transforming the model of computation of DES into a model of concurrent threads that synchronize as and when necessary. Unlike the cooperative threading model employed in the SystemC reference implementation, our threading model is capable of executing in parallel on the large number of simple processing units available on GPUs. Our simulation infrastructure is called SCGPSim and it includes a source-to-source (S2S) translator to transform synthesizable SystemC models into parallelly executable programs targeting an NVIDIA GPU. The translator retains the simulation semantics of the original designs by applying semantics preserving transformations. The resulting transformed models mapped onto the massively parallel architecture of GPUs improve simulation efficiency quite substantially. Preliminary experiments with varying-sized examples such as AES, ALU, and FIR have shown simulation speed-ups ranging from 30× to 100×. Considering that our transformations are not yet optimized, we believe that optimizing them will improve the simulation performance even further.

关键词： Computational modeling Discrete event simulation Graphics processing unit parallel architectures Scheduling parallel processing Concurrent computing Finite impulse response filter parallel programming

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