high performance computing on graphics processing units: hgpu.org

Graphics processing units (GPUs) have been growing in popularity due to their impressive processing capabilities, and with general purpose programming languages such as NVIDIA’s CUDA interface, are becoming the platform of choice in the scientific computing community. Today the research community successfully uses GPU to solve a broad range of computationally demanding, complex problems. This effort in general purpose computing on the GPU, also known as GPU computing, has positioned the GPU as a compelling alternative to traditional microprocessors in high-performance computer systems and DSP applications. GPUs have evolved a generalpurpose programmable architecture and supporting ecosystem that make it possible for their use in a wide range of non-graphics tasks, including many applications in signal processing. Commercial, high-performance signal processing applications that use GPUs as accelerators for general purpose tasks are still emerging, but many aspects of the architecture of GPUs and their wide availability make them interesting options for implementing and deploying such applications even though there are memory bottleneck challenges that have to be overcome in real time processing. This report describes the brief history, hardware architecture, and programming model for GPU computing as well as the modern tools and methods used recently. The application space of the GPGPU is explored with examples of signal processing applications and with results of recently conducted evaluations and benchmarks.