The convolution computation is widely used in many fields, especially in CNNs. Because of the rapid growth of the training data in CNNs, GPUs have been used for the acceleration, and memory-efficient algorithms are focused because of thier high performance. In this paper, we propose two convolution kernels for single-channel convolution and multi-channel convolution respectively. Our two methods achieve high performance by hiding the access delay of the global memory efficiently, and achieving high ratio of floating point Fused Multiply-Add operations per fetched data from the global memory. In comparison to the latest Cudnn library developed by Nvidia aimed to accelerate the deep-learning computation, the average performance improvement by our research is 2.6X for the single-channel, and 1.4X for the multi-channel.
翻译:由于CNN培训数据迅速增长,GPU被用于加速,而记忆高效算法也因性能高而集中。在本文中,我们分别为单频道革命和多频道革命提出了两个革命内核。我们的两个方法通过隐藏全球记忆存取的高效存取延迟和每个从全球记忆中提取的数据实现浮动点Fuse Dubly-Add操作的高比率而取得了高性能。 与Nvidia为加速深层学习计算而开发的最新库登图书馆相比,我们研究的平均性能改进是单频道2.6X,多频道1.4X。