The Graphics Card as a Streaming Computer

TL;DR

This paper explores how graphics cards function as highly optimized stream processors, enabling fast parallel computations and serving as general-purpose co-processors for large data processing tasks.

Contribution

It highlights the role of GPUs as efficient stream processing engines and their growing use as general-purpose co-processors in various applications.

Findings

GPUs perform computations much faster than CPUs for stream tasks.

Graphics cards are increasingly used as general-purpose stream processing engines.

The stream processing paradigm is central to both large data algorithms and GPU computations.

Abstract

Massive data sets have radically changed our understanding of how to design efficient algorithms; the streaming paradigm, whether it in terms of number of passes of an external memory algorithm, or the single pass and limited memory of a stream algorithm, appears to be the dominant method for coping with large data. A very different kind of massive computation has had the same effect at the level of the CPU. The most prominent example is that of the computations performed by a graphics card. The operations themselves are very simple, and require very little memory, but require the ability to perform many computations extremely fast and in parallel to whatever degree possible. What has resulted is a stream processor that is highly optimized for stream computations. An intriguing side effect of this is the growing use of a graphics card as a general purpose stream processing engine. In an ever-increasing array of applications, researchers are discovering that performing a computation on a graphics card is far faster than performing it on a CPU, and so are using a GPU as a stream co-processor.