Basics of GPU Computing for Data Scientists
GPU’s have become the new core for image analytics. More and more data scientists are looking into using GPU for image processing. In this article I review the basics of GPU’s that are needed for a data scientist and list a framework from literature for selecting suitability of GPU for an algorithm.
Lets start with what is a GPU?
Graphics Processing Unit (GPU) were originally created for rendering graphics. However due to their high performance and low cost they have become the new standard of image processing. Their application areas include image restoration, segmentation (labeling), de-noising, filtering, interpolation and reconstruction. A web search on what is a GPU would result to : “A graphics processing unit (GPU) is a computer chip that performs rapid mathematical calculations, primarily for the purpose of rendering images.”
What is GPU Computing?
Nvidia’s blog defines GPU computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate scientific, analytics, engineering, consumer, and enterprise applications. They also say if CPU is the brain then GPU is Soul of the computer.
GPU’s used for general-purpose computations have a highly data parallel architecture. They are composed of a number of cores. Each of these cores have a number of functional units, such as arithmetic and logic units (ALUs) etc. One or more of these functional units are used to process each thread of execution. These group of functional units that help the thread are called“thread processors”. All thread processors in a core of GPU perform the same instructions, as they share the same control unit. This means that GPUs can perform the same instruction on each pixel of an image in parallel. GPU architectures are complex and differ from manufacture to manufacture. The two main players in GPU market are Nvidia and AMD. Nvidia calls thread processors as CUDA (Compute Unified Device Architecture) cores, AMD calls them as Stream Processors (SP) .
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Note: Taposh Dutta Roy is a Data Geek at Kaiser Permanente. The views expressed in this article are only those of Mr. Dutta Roy. These are not the views of Kaiser Permanente, and they are not responsible for the content.