parallel computing research papers 2012







Adaptive Sequential Posterior Simulators for Massively Parallel Computing Environments
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ABSTRACT Massively parallel desktop computing capabilities now well within the reach of individual academics modify the environment for posterior simulation in fundamental and potentially quite advantageous ways. But to fully exploit these benefits algorithms that

MiniGhost: a miniapp for exploring boundary exchange strategies using stencil computations in scientific parallel computing
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ABSTRACT A broad range of scientific computation involves the use of difference stencils. In a parallel computing environment, this computation is typically implemented by decomposing the spacial domain, inducing a halo exchange of process-owned boundary data. This

Massively-Parallel Computing on Cog ex Machina
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ABSTRACT Cog ex Machina is a software framework for building massively-parallel applications on commodity, multicore hardware. Complex models may be expressed in a simple,Abstract programming model, while hiding the complexities (threads, locks,

Re-Optimizing data-parallel computing
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Abstract Performant execution of data-parallel jobs needs good execution plans. Certain properties of the code, the data, and the interaction between them are crucial to generate these plans. Yet, these properties are difcult to estimate due to the highly distributed

Point to point processing of digital images using parallel computing
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ABSTRACT This paper presents an approach the point to point processing of digital images using parallel computing, particularly for grayscale, brightening, darkening, thresholding and contrast change. The point to point technique applies a transformation to each pixel on

The Assessment of Bandwidth Requirements for Meteorological Code VARSHA on a Parallel Computing System
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ABSTRACT Complex scientific problems like weather forecasting, computational fluid and combustion dynamics, computational drug design etc. essentially require large scale computational resources in order to obtain solution to the equations governing them.

Parallel Computing System for Image Intelligent Processing
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ABSTRACT In this paper, a parallel computing system for image intelligent processing is described. This parallel computing system includes two main parts: A parallel computer and a set of software tools. The parallel computer is constructed by a host processor, a SII\/ID

The boat hull model: adapting the roofline model to enable performance prediction forparallel computing
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Abstract Multi-core and many-core were already major trends for the past six years, and are expected to continue for the next decades. With these trends of parallel computing, it becomes increasingly difficult to decide on which architecture to run a given application. In

PC-based real-time video watermark embedding system independent of platform for parallel computing
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An improved system is described for embedding watermarks into video frames in real time using software running on an ordinary personal computer and that is independent of the parallel-computing platform. It uses standard video I/O and is separate from the encoding

Parallel and Distributed Computing and Systems
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Purpose Exploring parallelism and concurrency is an efficient way of sharing interconnected processing and data resources to speed up time critical computation, lower costs of design and production, increase profit, create a better business environment, and improve

Computing A Matrix Transpose of Multithreading for Queueing Parallel in Matlab Programming
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ABSTRACT This paper describes a A matrix operation (vector and transpose) can be performed in queueing parallel model by using multithreading software are showing. Multithreading is useful in reducing the latency by switching among a set of threads in

Vortex particle method and parallel computing
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In this paper, it was presented numerical results related to three dimensional simulation of motion of a vortex ring. For the simulation it was chosen the Vortex In Cell method. The method was shortly described in the paper. The numerical results were obtained on the

Towards user transparent parallel multimedia computing on GPU-clusters
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The research area of Multimedia Content Analysis (MMCA) considers all aspects of the automated extraction of knowledge from multimedia archives and data streams. To satisfy the increasing computational demands of MMCA problems, the use of High Performance

The Parallel Expectation Maximization Algorithm: Novel Ideas in Distributed Iterative StatisticalComputing
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ABSTRACT Modern distributed computing architectures that facilitate storage of massive data (distributed data) on multiple machines are becoming increasingly prevalent. In these distributed computing environments, the statistical algorithms and procedures can access

Scalable Parallel Scientific Computing Using Twister4Azure
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ABSTRACT Recent advances in data intensive computing for science discovery are fueling a dramatic growth in use of data-intensive iterative computations. The utility computing model introduced by cloud computing combined with the rich set of cloud infrastructure and

Design of a Parallel Robotic Manipulator using Evolutionary Computing
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ABSTRACT In this paper the kinematic design of a 6-dof parallel robotic manipulator is analysed. Firstly, the condition number of the inverse kinematic jacobian is considered as the objective function, measuring the manipulator s dexterity and a genetic algorithm is

A Pragmatic Study and Analysis of Load Balancing Techniques In Parallel Computing
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MAS Manekar, MMD Poundekar, H Gupta, M Nagle ABSTRACT Allocation of the work load in to small processes is known as Load Balancing. Parallel programming is based on four phases finding Concurrency (by understanding the available concurrency and expose in algorithm design), Algorithm structure (programmer

Parallel computing with CUDA



Parallel computing with CUDA
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Page 1. Mark Harris NVIDIA Developer Technology ParallelComputingwithCUDA Page 2. CUDAParallelComputing ArchitectureISA and hardware compute engine in NVIDIA GPUsExposes the computational horsepower of NVIDIA GPUs

Parallel Computing with CUDA
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Abstract This thesis shows the differences between parallel and serial computing through the use of a complex test case and a more simplistic test case. Also researched was how parallel computing with CUDA, by looking for the different type of commands and

Massively Parallel Computing with Cuda
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Page 1. Parallel08 – Memory Access Hendrik Lensch and Robert Strzodka Massively Parallel ComputingwithCuda Memory Access Hendrik Lensch Robert Strzodka Page 2. Parallel08 – Memory Access Hendrik Lensch and Robert Strzodka TodayMemory – Latency

cuda programing and java programing

JCUDA: A programmer-friendly interface for accelerating Java programs with CUDA
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It also allows CUDA kernel code to be embedded as a string in a Python programRapidMind provides a multicore programming framework based on the C++ programming language [16A programmer can embed a kernel intended to run on the GPU as a delimited piece of code

Rootbeer: Seamlessly using GPUs from java
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Similarly, arrays of object types store references to the actual objects in each array element. VI. CUDA CODE GENERATION Java Bytecode is converted to the CUDA programming language by directly analyzing Jimple statements from Soot.

Jcudamp: Openmp/java on cuda
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Instead, the programmer will ex- pect that for such modern, dynamic, object-oriented languages, theTo circumvent the low-level parallel programming model of Java and to focus on SPMDJava class loader detects the presence and different ver- sions of CUDA (each version

Habanero-Java: the new adventures of old X10
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benefit of the seq clause is that it removes the burden on the programmer toThis is consistent with other runtime sys- tems, such as OpenMP, CUDA and MPI, thatPolyglot [26] is a highly extensible source-to-source translator framework for the Java programming language.

CnC-CUDA: Declarative Programming for GPUs.
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In CnC- CUDA, all of the complexity of inter-language function-calls and device memory management is hidden from the CnC programmer and autoHabanero Java also offers the extern keyword similar to native in Javawhich greatly simplifies programming with native

Returning control to the programmer: Simd intrinsics for virtual machines
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SSE, VIS), general-purpose computing on graphics cards (for example, Nvidia CUDA, ATI STREAMapproach works only when the processor architecture is known to the programmer, and itare faster application runtime, lower cost, smaller code size, fewer coding errors, and a

Automated dynamic analysis of CUDA programs
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no programmer input, race conditions and inefficient shared memory access patterns.these specific problems, helping them increase both the correct- ness and efficiency of their Cuda programs ACM Transactions on Programming Languages and Systems, 28(2):207–255, 2006

Par4all: From convex array regions to heterogeneous computing
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The gpu programming model and the host accelera- tor paradigm greatly restrict the potential ofRecent work [6, 9] adds extensions to OpenMP that account for cuda specificitystill responsible for designing and writ- ing communications code, and usually the programmer have to

Solving classification problems using genetic programming algorithms on GPUs
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Experiments carried out compare the performance of three different GP algo- rithms in single and multithreaded Java, C and CUDA GPU code.JCLEC is a software system for Evolutionary Com- putation (EC) research, developed in Java programming language.

A survey of parallel programming models and tools in the multi and many-core era
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Like CUDA, OpenCL is well suited for implementing the SPMD parallel design pattern [10].the other hand, with respect to data distribution, coarrays allow the programmer to specifyDIAZ ET AL.: A SURVEY OF PARALLEL PROGRAMMING MODELS AND TOOLS IN THE MULTI

Hierarchical place trees: A portable abstraction for task parallelism and data movement
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Note that the programmer, compiler, runtime or hardware may choose to create a cached clone of the object closer to4.2 Programming Using the HPT Interface for Implicit Data Accesscondition of the branch evaluates to true if the cur- rent topology is a CUDA grid configuration [HTML]

Accelerating epistasis analysis in human genetics with consumer graphics hardware
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The MDR software package uses the Java programming language and features a powerful GUI and a variety of preprocessing, expert knowledgecapable of running on graphics processing units (GPUs) using the NVIDIA Compute Unified Device Architecture (CUDA) framework

Parallel Java 2 Library
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programming, MPI for message passing parallel programming on clusters, Open CL or Nvidia's CUDA for GPU parallel programming, Apa che's Hadoop for mapreduce big data programming. As for languages, many computer science students are familiar with Java.

Java in the High Performance Computing arena: Research, practice and experience
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20th Annual ACM SIGPLAN Conference on Object-Oriented Programming, Systems, Languages, and Applications, OOPSLA'05, San Diego41]; Y. Yan, M. Grossman, V. Sarkar, JCUDA: a programmer-friendly interface for accelerating Java programs with CUDA, in: Proc

Habanero-Java: the new adventures of old X10
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The main benefit of the seq clause is that it removes the burden on the programmer toThis is consistent with other runtime sys- tems, such as OpenMP, CUDA and MPI, that require theThe Polyglot- based front-end parses HJ source code to create an abstract syntax tree (AST).

Jcudamp: Openmp/java on cuda
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For example, the programmer could have added the clause tiled(src: {x:-size,size; y:-size5. PERFORMANCE To test performance we use a simple matrix multiplication code as a micro benchmarkThe software envi- ronment is Linux kernel 2.6.24 with CUDA 2.2, GCC version 4.2

CnC-CUDA: Declarative Programming for GPUs.
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In this way, the programmer has the choice of writing CPU Steps in Java or C (since C code can be invoked form Java) and GPU steps in CUDA, and can leave all the remaining details of creating and managing parallel tasks and data transfers to the CnC-CUDA

Solving classification problems using genetic programming algorithms on GPUs
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Next the NVIDIA GPU code-named Fermi doubles the number of cores available tomW: Optimization principles and application performance evaluation of a multithreaded GPU using CUDA.13th ACM SIGPLAN Symposium on Principles and practice of parallel programming, pp

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