A Highly Optimized GPU Batched Elasticnet Solver (BENS) with Application to Real- Time Keypoint Detection for Image Retrieval

Zheng Guo, Thanh Hong-Phuoc, N. Khan, L. Guan
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Abstract

In this paper, we present a highly optimized GPU batched elastic-net solver (BENS) with application to real-time key-point detection for image retrieval. BENS was optimized to perform hundreds of thousands of small elastic-net fits by batching each fit from specific steps in the elastic-net computation into a large matrix multiplication which can be computed efficiently using the CUBLAS library. The main motivation for BENS was a real-time implementation of the Sparse-Coding Key-point detector (SCK) algorithm which has reaching applications in science, engineering, social science and medicine. When BENS was applied to accelerate SCK, we have achieved a 232x speed up compared to the original CPU implementation of SCK. To demonstrate the newly accelerated SCK algorithm, we conducted an Bo Vw based image retrieval experiment using SCK as the key-point detector.
一种高度优化的GPU批处理弹性网络求解器(BENS)及其在图像检索中的实时关键点检测中的应用
在本文中,我们提出了一个高度优化的GPU批量弹性网络求解器(BENS),并应用于图像检索的实时关键点检测。通过将弹性网计算中特定步骤的每个拟合批量处理成一个大型矩阵乘法,BENS可以执行数十万个小型弹性网拟合,并且可以使用CUBLAS库进行高效计算。BENS的主要动机是实时实现稀疏编码关键点检测器(SCK)算法,该算法已在科学,工程,社会科学和医学中得到广泛应用。当使用BENS来加速SCK时,我们获得了比原来的CPU实现SCK快232x的速度。为了验证新的加速SCK算法,我们使用SCK作为关键点检测器进行了基于Bo Vw的图像检索实验。
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