CGMBE: a model-based tool for the design and implementation of real-time image processing applications on CPU–GPU platforms

Abstract

Processing large images in real time requires effective image processing algorithms as well as efficient software design and implementation to take full advantage of all CPU cores and GPU resources on state of the art CPU/GPU platforms. Efficiently coordinating computations on both the host (CPU) and devices (GPUs), along with host–device data transfers is critical to achieving real-time performance. However, such coordination is challenging for system designers given the complexity of modern image processing applications and the targeted processing platforms. In this paper, we present a novel model-based design tool that automates and optimizes these critical design decisions for real-time image processing implementation. The proposed tool consists of a compile-time static analyzer and a run-time dynamic scheduler. The tool automates the process of scheduling dataflow tasks (actors) and coordinating CPU–GPU data transfers in an integrated manner. The approach uses an unfolded dataflow graph representation of the application along with thread-pool-based executors, which are optimized for efficient operation on the targeted CPU–GPU platform. This approach automates the most complicated aspects of the design and implementation process for image processing system designers, while maximizing the utilization of computational power, reducing the memory footprint for both the CPU and GPU, and facilitating experimentation for tuning performance-oriented designs.