A Time Optimal Parallel Algorithm for the Dynamic Programming on the Hierarchical Memory Machine

Nakano, Koji

doi:10.1109/candar.2014.14

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…In this section, we show our previous GPU implementation of the optimal polygon triangulation on the GPU . This approach is used to efficiently compute the optimal polygon triangulation only for one instance using parallel threads on the GPU.…”

Section: Our Previous Gpu Implementation Of the Optimal Polygon Trianmentioning

confidence: 99%

“…In this section, we show our previous GPU implementation of the optimal polygon triangulation on the GPU. 19,25,26 This approach is used to efficiently compute the optimal polygon triangulation only for one instance using parallel threads on the GPU. Therefore, to distinguish this approach from the proposed methods shown in the following, we call it intra-parallel thread assignment.…”

Section: Our Previous Gpu Implementation Of the Optimal Polygon Trianmentioning

confidence: 99%

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Bulk execution of the dynamic programming for the optimal polygon triangulation problem on the GPU

Yamashita

Ito

Nakano

2018

Concurrency and Computation

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Summary The bulk execution is to execute some computation for many different inputs in turn or at the same time. The main contribution of this paper is to propose a parallel processing technique for the bulk execution of the dynamic programming using the GPU (Graphics Processing Unit). Especially, we focus on the optimal polygon triangulation problem for a lot of polygons. We consider programming issues of the GPU architecture such as coalesced memory access of the global memory, warp divergence avoidance, and reduction of CUDA kernel calls. In the GPU implementation, we propose two thread assignment methods that efficiently perform the parallel execution with a lot of threads on thousands of cores in the GPU. The experimental results show that our GPU implementation on NVIDIA TITAN V attains a speed‐up factor of up to 106.05 and 26.78 over the single‐thread and 8‐thread CPU implementations on Intel Core i7‐6700K CPU, respectively.

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Section: Our Previous Gpu Implementation Of the Optimal Polygon Trianmentioning

confidence: 99%

Section: Our Previous Gpu Implementation Of the Optimal Polygon Trianmentioning

confidence: 99%

Bulk execution of the dynamic programming for the optimal polygon triangulation problem on the GPU

Yamashita

Ito

Nakano

2018

Concurrency and Computation

Self Cite

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“…It has been studied well to speed up DP programs using GPU (e.g. [2], [3]), where they mainly focus on optimizing the order of accessing data by proposing novel techniques avoiding memory access conflicts. In this study, we consider adopting a pipeline technique and implementing the DP program on GPU in a pipeline fashion.…”

Section: Introductionmentioning

confidence: 99%

Solving Dynamic Programming Problem by Pipeline Implementation on GPU

Matsumae¹,

Miyazaki²

2018

ijacsa

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In this paper, we show the effectiveness of a pipeline implementation of Dynamic Programming (DP) on GPU. As an example, we explain how to solve a matrix-chain multiplication (MCM) problem by DP on GPU. This problem can be sequentially solved in O(n 3) steps by DP where n is the number of matrices, because its solution table is of size n × n and each element of the table can be computed in O(n) steps. A typical speedup strategy for this is to parallelize the O(n) step computation of each element, which can be easily achieved by parallel prefix computation, i.e., an O(log n) step computation with n threads in a tournament fashion. By such a standard parallelizing method, we can solve the MCM problem in O(n 2 log n) steps with n threads. In our approach, we solve the MCM problem on GPU in a pipeline fashion, i.e., we use GPU cores for supporting pipelinestages so that many elements of the solution table are partially computed in parallel at one time. Our implementation determines one output value per one computational step with n threads in a pipeline fashion and constructs the solution table totally in O(n 2) steps with n threads.

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“…It has been studied well to speed up DP programs using GPU (e.g. [1,2]), where they mainly focus on optimizing the order of accessing data by proposing novel techniques avoiding memory access conflicts. In our study, however, we consider adopting a pipeline technique and implementing the DP program on GPU in a pipeline fashion.…”

Section: Introductionmentioning

confidence: 99%

Accelerating pipeline implementation of dynamic programming on GPU

Matsumae¹

EPiC Series in Computing

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In this paper, we show the effectiveness of pipeline implementations of Dynamic Pro- gramming (DP) on Graphics Processing Unit (GPU). We deal with a simplified DP problem where each element of its solution table is calculated in order by semi-group operations among several of already computed elements in the table. We implement the DP program on GPU in a pipeline fashion, i.e., we use GPU cores for supporting pipeline-stages so that several elements of the solution tables are partially computed at one time. Further, to accelerate the pipeline implementation, we propose a p-fold pipeline technique, which enables larger parallelism more than the number of pipeline-stages.

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A Time Optimal Parallel Algorithm for the Dynamic Programming on the Hierarchical Memory Machine

Cited by 4 publications

References 20 publications

Bulk execution of the dynamic programming for the optimal polygon triangulation problem on the GPU

Bulk execution of the dynamic programming for the optimal polygon triangulation problem on the GPU

Solving Dynamic Programming Problem by Pipeline Implementation on GPU

Accelerating pipeline implementation of dynamic programming on GPU

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