practice

Doi: 10.1145/1400181.1400197

As the line between GPUs and CPUs
begins to blur, it’s important to understand
what makes GPUs tick.
By kayVon fatahaLian anD mike houston
a closer
Look at
GPus

A GAMER WANDERS through a virtual world rendered in near-cinematic detail. Seconds later, the screen fills with a 3D explosion, the result of unseen enemies hiding in physically accurate shadows. Disappointed, the user exits the game and returns to a computer desktop that exhibits the stylish 3D look-and-feel of a modern window manager. Both of these visual experiences require hundreds of gigaflops of computing performance, a demand met by the GPU (graphics processing unit) present in every consumer PC.

The modern GPU is a versatile processor that constitutes an extreme but compelling point in the growing space of multicore parallel computing architectures. These platforms, which include GPUs, the STI Cell Broadband Engine, the Sun UltraSPARC

T2, and increasingly multicore x86 systems from Intel and AMD, differentiate themselves from traditional CPU designs by prioritizing high-throughput processing of many parallel operations over the low-latency execution of a single task.

GPUs assemble a large collection of fixed-function and software-program-mable processing resources. Impressive statistics, such as ALU (arithmetic logic unit) counts and peak floating-point rates often emerge during discussions of GPU design. Despite the inherently parallel nature of graphics, however, efficiently mapping common rendering algorithms onto GPU resources is extremely challenging.

The key to high performance lies in strategies that hardware components and their corresponding software interfaces use to keep GPU processing resources busy. GPU designs go to great lengths to obtain high efficiency and conveniently reduce the difficulty programmers face when programming graphics applications. As a result, GPUs deliver high performance and expose an expressive but simple programming interface. This interface remains largely devoid of explicit parallelism or asynchronous execution and has proven to be portable across vendor implementations and generations of GPU designs.

At a time when the shift toward throughput-oriented CPU platforms is prompting alarm about the complexity of parallel programming, understanding key ideas behind the success of GPU computing is valuable not only for developers targeting software for GPU execution, but also for informing the design of new architectures and programming systems for other domains. In this article, we dive under the hood of a modern GPU to look at why interactive rendering is challenging and to explore the solutions GPU architects have devised to meet these challenges.

 

the Graphics Pipeline A graphics system generates images that represent views of a virtual scene. This scene is defined by the geometry,

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