Results 1 - 10 of 774

Parallelizing RSA Algorithm on Multicore CPU and GPU

by Heba Mohammed Fadhil, Mohammed Issam Younis, Ph. D
"... Public key algorithms are extensively known to be slower than symmetric key alternatives in the a r e a of cryptographic algorithms for the reason of their basis in modular arithmetic. The most public key algorithm widely used is the RSA. Therefore, how to enhance the speed of RSA algorithm has been ..."
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(SIMT) style of computing. This paper proposes a hybrid system to parallelize the RSA for multicore CPU and many cores GPUs with variable key size. In doing so, three variants implementation for the RSA algorithm are done to facilitate the performance comparison against Crypto++ library and sequential

High Performance Color Image Processing in Multicore CPU using MFC Multithreading

by Anandhanarayanan Kamalakannan, Govindaraj Rajamanickam
"... Abstract—Image processing is an engineering field where stored image data is readily available for parallel processing. Basically data processing algorithms developed in sequential approach are not capable of harnessing the computing power of individual cores present in a single-chip multicore proce ..."
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of the algorithm in a multicore CPU, the entire image data is partitioned into equal blocks and copy of the algorithm is applied on each block using separate worker thread. In this paper, multithreaded color image processing algorithms namely contrast enhancement using fuzzy technique and edge detection were

HSPA+/LTE-A Turbo Decoder on GPU and Multicore CPU

by Michael Wu, Guohui Wang, Bei Yin, Christoph Studer, Joseph R. Cavallaro
"... Abstract—This paper compares two implementations of re-configurable and high-throughput turbo decoders. The first implementation is optimized for an NVIDIA Kepler graphics processing unit (GPU), whereas the second implementation is for an Intel Ivy Bridge processor. Both implementations support max- ..."
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-rate performance. Our results show that the Intel Ivy Bridge processor implementation achieves up to 2 × higher decoding throughput than our GPU implementation. In addition our CPU implementation requires roughly 4 × fewer codewords to be processed in parallel to achieve its peak throughput. I.

DYNAMIC AUTOTUNING OF ADAPTIVE FAST MULTIPOLE METHODS ON HYBRID MULTICORE CPU & GPU SYSTEMS

by Marcus Holm, Stefan Engblom, Anders Goude, Sverker Holmgren
"... Abstract. We discuss an implementation of adaptive fast multipole meth-ods targeting hybrid multicore CPU- and GPU-systems. From previous ex-periences with the computational profile of our version of the fast multipole algorithm, suitable parts are off-loaded to the GPU, while the remaining parts ar ..."
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Abstract. We discuss an implementation of adaptive fast multipole meth-ods targeting hybrid multicore CPU- and GPU-systems. From previous ex-periences with the computational profile of our version of the fast multipole algorithm, suitable parts are off-loaded to the GPU, while the remaining parts

A cost-optimal parallel algorithm for the 0-1 knapsack problem and its performance on multicore CPU and GPU implementations

by Kenli Li , Jing Liu , Lanjun Wan , Shu Yin , Keqin Li
"... a b s t r a c t The 0-1 knapsack problem has been extensively studied in the past years due to its immediate applications in industry and financial management, such as cargo loading, stock cutting, and budget control. Many algorithms have been proposed to solve this problem, most of which are heuri ..."
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and yields optimal solutions consuming less computational time. Furthermore, this paper implements COPA on two scenarios -multicore CPU based architectures using Open MP and GPU based configurations using CUDA. A series of experiments are conducted to examine the performance of COPA under two different test

Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks

by Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, Dennis Fetterly - In EuroSys , 2007
"... Dryad is a general-purpose distributed execution engine for coarse-grain data-parallel applications. A Dryad applica-tion combines computational “vertices ” with communica-tion “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set of availa ..."
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simultaneously on multi-ple computers, or on multiple CPU cores within a computer. The application can discover the size and placement of data at run time, and modify the graph as the computation pro-gresses to make efficient use of the available resources. Dryad is designed to scale from powerful multi-core sin

CPU Accounting for Multicore Processors

by Carlos Luque, Miquel Moreto, Francisco J. Cazorla, Roberto Gioiosa, Alper Buyuktosunoglu, Senior Member, Mateo Valero
"... Abstract—In single-threaded processors and Symmetric Multiprocessors the execution time of a task depends on the other tasks it runs with (the workload), since the Operating System (OS) time shares the CPU(s) between tasks in the workload. However, the time accounted to a task is roughly the same re ..."
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Abstract—In single-threaded processors and Symmetric Multiprocessors the execution time of a task depends on the other tasks it runs with (the workload), since the Operating System (OS) time shares the CPU(s) between tasks in the workload. However, the time accounted to a task is roughly the same

Multicore bundle adjustment

by Changchang Wu, Sameer Agarwal, Brian Curless, Steven M. Seitz - In IEEE Conference on Computer Vision and Pattern Recognition (CVPR , 2011
"... We present the design and implementation of new inexact Newton type Bundle Adjustment algorithms that exploit hardware parallelism for efficiently solving large scale 3D scene reconstruction problems. We explore the use of multicore CPU as well as multicore GPUs for this purpose. We show that overco ..."
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We present the design and implementation of new inexact Newton type Bundle Adjustment algorithms that exploit hardware parallelism for efficiently solving large scale 3D scene reconstruction problems. We explore the use of multicore CPU as well as multicore GPUs for this purpose. We show

Methods for Emulation of Multi-Core CPU Performance

by Tomasz Buchert, Lucas Nussbaum, Jens Gustedt , 2011
"... Abstract—When validating or evaluating real distributed applications, it is useful to be able to test the application in a large range of environments. In that context, emulation of CPU performance enables researchers to investigate how the performance of the application is affected by the performan ..."
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by the performance of the participating CPUs. Using a homogeneous cluster of fast multi-core nodes, it is therefore possible to evaluate how an application would behave on a heterogeneous set of nodes, with varying performance and number of cores. In this paper, three new methods for the emulation of CPU performance

Accurate CPU Power Modeling for Multicore Smartphones

by Yifan Zhang , Yunxin Liu , Li Zhuang , Xuanzhe Liu , Feng Zhao , Qun Li
"... ABSTRACT CPU is a major source of power consumption in smartphones. Power modeling is a key technology to understand CPU power consumption and also an important tool for power management on smartphones. However, we have found that existing CPU power models on smartphones are ill-suited for modern m ..."
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multicore CPUs: they can give high estimation errors (up to 34%) and high estimation accuracy variation (more than 30%) for different types of workloads on mainstream multicore smartphones. The root cause is that those models estimate the power consumption of a CPU based on only frequency and utilization
Results 1 - 10 of 774