Parallel solution of the subset‐sum problem: an empirical study

Bokhari, Shahid H.

doi:10.1002/cpe.2800

Cited by 15 publications

(9 citation statements)

References 16 publications

(23 reference statements)

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“…In [5] authors have introduced a dynamic programming algorithm and solve the problem in pseudo-polynomial time. However, they use a programming table and perform backtracking on the table which differs from our approach.…”

Section: Introductionmentioning

confidence: 99%

Parallel implementation of the modified subset sum problem in CUDA

Ristovski

Mishkovski

Gramatikov

et al. 2014

2014 22nd Telecommunications Forum Telfor (TELFOR)

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In the recent years, computing is shifting from "central processing" on the CPU to "co-processing" on the CPU and GPU. This computing paradigm shift is due to the development of CUDA (Compute Unified Device Architecture) parallel computing architecture. CUDA is a programming model for parallel computing in Graphics Processing Units (GPUs). In this work, we have implemented parallel solution of the NP-complete modified subset sum algorithm using CUDA. With our implementation, for a certain problem size, we have obtained speedup of 20 times, compared to the CPU version.

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Section: Introductionmentioning

confidence: 99%

Parallel implementation of the modified subset sum problem in CUDA

Ristovski

Mishkovski

Gramatikov

et al. 2014

2014 22nd Telecommunications Forum Telfor (TELFOR)

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“…This is but one example of how the architecture of the XMT family of machines permits fine-grained parallel computing in graph problems. Other examples appear in [37] and [40][41][42].…”

Section: Fine-grained Synchronizationmentioning

confidence: 99%

Massively multithreaded maxflow for image segmentation on the Cray XMT‐2

Bokhari¹,

Çatalyürek

Gürcan

2013

Concurrency and Computation

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SUMMARY Image segmentation is a very important step in the computerized analysis of digital images. The maxflow mincut approach has been successfully used to obtain minimum energy segmentations of images in many fields. Classical algorithms for maxflow in networks do not directly lend themselves to efficient parallel implementations on contemporary parallel processors. We present the results of an implementation of Goldberg-Tarjan preflow-push algorithm on the Cray XMT-2 massively multithreaded supercomputer. This machine has hardware support for 128 threads in each physical processor, a uniformly accessible shared memory of up to 4 TB and hardware synchronization for each 64 bit word. It is thus well-suited to the parallelization of graph theoretic algorithms, such as preflow-push. We describe the implementation of the preflow-push code on the XMT-2 and present the results of timing experiments on a series of synthetically generated as well as real images. Our results indicate very good performance on large images and pave the way for practical applications of this machine architecture for image analysis in a production setting. The largest images we have run are 320002 pixels in size, which are well beyond the largest previously reported in the literature.

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“…Figure depicts the main loop of this code where the filling and updating of the words is being carried out. Details of this code are available in and .…”

Section: Performance Of Three Dynamic Programming Codesmentioning

confidence: 99%

A comparison of the Cray XMT and XMT‐2

Bokhari

2012

Concurrency and Computation

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SUMMARYWe explore the comparative performance of the Cray XMT and XMT‐2 massively multithreaded supercomputers. We use benchmarks to evaluate memory accesses for various types of loops. We also compare the performance of these machines on matrix multiply and on three previously implemented dynamic programming algorithms. It is shown that the relative performance of these machines is dependent on the size (number of processors) of the configuration, as well as the size of the problem being evaluated. In particular, small configurations of the original XMT can sometimes show slightly better performance than larger configurations of the XMT‐2, for the same problem size. We note that, under heavy memory load, performance of loops can saturate well before the maximum number of processors available. This suggests that it may not always be useful to use the maximum number of processors for a specific run. We also show that manual restructuring of nested loops, including decreasing the parallelism, can result in major improvements in performance. The results in this paper indicate that careful exploration of the space of problem sizes, number of processors used, and choices of loop parallelization can yield substantial improvements in performance. These improvements can be very significant for production codes that run for extended periods of time. Copyright © 2012 John Wiley & Sons, Ltd.

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Parallel solution of the subset‐sum problem: an empirical study

Cited by 15 publications

References 16 publications

Parallel implementation of the modified subset sum problem in CUDA

Parallel implementation of the modified subset sum problem in CUDA

Massively multithreaded maxflow for image segmentation on the Cray XMT‐2

A comparison of the Cray XMT and XMT‐2

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