Duhu Man

A GPU Implementation of Computing Euclidean Distance Map with Efficient Memory Access

Recent Graphics Processing Units (GPUs), which have many processing units, can be used for general purpose parallel computation. To utilize the powerful computing ability, GPUs are widely used for general purpose processing. Since GPUs have very high memory bandwidth, the performance of GPUs greatly depends on memory access. The main contribution of this paper is to present a GPU implementation of computing Euclidean Distance Map (EDM) with efficient memory access. Given a 2-D binary image, EDM is a 2-D array of the same size such that each element is storing the Euclidean distance to the nearest black pixel. In the proposed GPU implementation, we have considered many programming issues of the GPU system such as coalescing access of global memory, shared memory bank conflicts and partition camping. In practice, we have implemented our parallel algorithm in the following two modern GPU systems: Tesla C1060 and GTX 480, respectively. The experimental results have shown that, for an input binary image with size of

Implementations of Parallel Computation of Euclidean Distance Map in Multicore Processors and GPUs

9216\times 9216

Developing science gateways for drug discovery in a grid environment

, our implementation can achieve a speedup factor of 52 over the sequential algorithm implementation.

Accelerating computation of Euclidean distance map using the GPU with efficient memory access

Given a 2-D binary image of size

A Simple Parallel Convex Hulls Algorithm for Sorted Points and the Performance Evaluation on the Multicore Processors

n \times n

Implementations of a Parallel Algorithm for Computing Euclidean Distance Map in Multicore Processors and GPUs

, Euclidean Distance Map (EDM) is a 2-D array of the same size such that each element is storing the Euclidean distance to the nearest black pixel. It is known that a sequential algorithm can compute the EDM in

An Optimal Implementation of the Approximate String Matching on the Hierarchical Memory Machine, with Performance Evaluation on the GPU

O(n^2)

AN EFFICIENT PARALLEL SORTING COMPATIBLE WITH THE STANDARD QSORT

and thus this algorithm is optimal. Also, work-time optimal parallel algorithms for shared memory model have been presented. However, these algorithms are too complicated to implement in existing shared memory parallel machines. The main contribution of this paper is to develop a simple parallel algorithm for the EDM and implement it in two parallel platforms: multicore processors and a Graphics Processing Unit (GPU). More specifically, we have implemented our parallel algorithm in a Linux server with four Intel hexad-core processors (Intel Xeon X7460 2.66GHz). We have also implemented it in a modern GPU system, Tesla C1060, respectively. The experimental results have shown that, for an input binary image with size of

An Efficient Parallel Sorting Compatible with the Standard qsort

10000\times 10000

Developing science gateways for drug discovery in a grid environment.

, our implementation in the multi-core system achieves a speedup factor of 18 over the performance of a sequential algorithm using a single processor in the same system. Meanwhile, for the same input binary image, our implementation on the GPU achieves a speedup factor of 5 over the sequential algorithm implementation.