Fast Histograms using Adaptive CUDA Streams

Histograms are widely used in medical imaging, network intrusion detection, packet analysis and other streambased high throughput applications. However, while porting such software stacks to the GPU, the computation of the histogram is a typical bottleneck primarily due to the large impact on kernel speed by atomic operations. In this work, we propose a stream-based model implemented in CUDA, using a new adaptive kernel that can be optimized based on latency hidden CPU compute. We also explore the tradeoffs of using the new kernel vis-

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Sisir Koppaka, Dheevatsa Mudigere, Srihari Narasimhan, Babu Narayanan
• Keywords: CORR 2010, Education, Intelligent Kernel, Network Intrusion Detection, NVIDIA SDK Kernel