Performance Improvement of CUDA Applications by Reducing CPU-GPU Data Transfer Overhead

被引：0

作者：

Sunitha, N., V ^{[1
]}

Raju, K. ^{[1
]}

Chiplunkar, Niranjan N. ^{[1
]}

机构：

[1] NMAMIT, Dept CSE, Nitte, India

来源：

PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON INVENTIVE COMMUNICATION AND COMPUTATIONAL TECHNOLOGIES (ICICCT) | 2017年

关键词：

Heterogeneous system; CUDA; Kernel; Stream;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In a CPU-GPU based heterogeneous computing system, the input data to be processed by the kernel resides in the host memory. The host and the device memory address spaces are different. Therefore, the device can not directly access the host memory. In CUDA programming model, the data is moved between the host memory and the device memory. This data transfer is a time consuming task. The communication overhead can be hidden by overlapping the data transfer and the kernel execution. CUDA streams provide a means for overlapping data transfer and the kernel execution. In this paper we explore the effects of overlapping data transfer and the kernel execution on overall execution time of some CUDA applications. The results show that the usage of the different levels of concurrency supported by the streams enhances the performance of the CUDA applications.

引用

页码：211 / 215

页数：5

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