GPU evolution: Will graphics morph into compute?

Norman Rubin
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引用次数: 2

Abstract

In the last several years GPU devices have started to evolve into supercomputers. New, non-graphics, features are rapidly appearing along with new more general programming languages. One reason for the quick pace of change is that, games and hardware evolve together: Hardware vendors review the most popular games, looking for places to add hardware while game developers review new hardware, looking for places to add more realism. Today, we see both GPU devices and games moving from a model of looks real to one of acts real. One consequence of acts real is that evaluating physics, simulations, and artificial intelligence on a GPU is becoming an element of future game programs. We will review the difference between a CPU and a GPU. Then we will describe hardware changes added to the current generation of AMD graphics processors, including the introduction of traditional compute operations such as double precision, scatter/gather and local memory. Along with new features, we have added new metrics like performance/watt and performance/dollar. The current AMD GPU processor delivers 9 gigaflops/watt and 5 gigaflops/dollar. For the last two generations, each AMD GPU has provided double the performance/watt of the prior machine. We believe the software community needs to become more aware and appreciate these metrics. Because this has been a kind of co-evolution and not a process of radical change, current GPU devices have retained a number of odd sounding transitional features, including fixed functions like memory systems that can do filtering, depth buffers, a rasterizer and the like. Today, each of these remain because they are important for graphics performance. Software on GPU devices also shows transitional features. As AI/physics virtual reality starts to become important, development frameworks have started to shift. Graphics APIs have added compute shaders. Finally, there has been a set of transitional programs implemented by graphics programmers but whose only real connection with graphics is that the result is rendered. One early example is toy shop which contains a weak physical simulation of rain on window (it looks great but the random number generator would not pass any kind of test). A more recent and better acting program is March of the Froblins an AI program related to robotic path calculations. This program both simulates large crowds of independent creatures and shows how massively parallel compute can benefit character-centric entertainment.
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GPU的进化:图形会变成计算吗?
在过去的几年里,GPU设备已经开始向超级计算机发展。随着新的更通用的编程语言的出现,新的非图形特性也在迅速出现。快速变化的一个原因是,游戏和硬件一起发展:硬件供应商审查最受欢迎的游戏,寻找添加硬件的地方,而游戏开发商审查新硬件,寻找添加更多现实性的地方。今天,我们看到GPU设备和游戏都从一个看起来真实的模型转变为一个真实的模型。真实行为的一个后果是,在GPU上评估物理、模拟和人工智能将成为未来游戏程序的一个元素。我们将回顾CPU和GPU之间的区别。然后,我们将描述当前一代AMD图形处理器的硬件变化,包括引入传统的计算操作,如双精度,分散/收集和本地内存。除了新功能,我们还添加了性能/瓦特和性能/美元等新指标。目前AMD的GPU处理器可以提供9千兆次/瓦特和5千兆次/美元的速度。在过去的两代中,每个AMD GPU的性能/瓦特都是之前机器的两倍。我们相信软件社区需要更加了解并欣赏这些度量标准。因为这是一种共同进化,而不是一个彻底改变的过程,所以当前的GPU设备保留了许多奇怪的过渡功能,包括可以进行过滤的内存系统、深度缓冲、光栅化等固定功能。今天,这些都保留了下来,因为它们对图形性能很重要。GPU设备上的软件也显示出过渡特性。随着AI/物理虚拟现实开始变得重要,开发框架也开始发生转变。图形api增加了计算着色器。最后,有一组由图形程序员实现的过渡程序,但它们与图形的唯一真正联系是呈现结果。一个早期的例子是《玩具店》,它包含了一个弱的物理模拟(游戏邦注:它看起来很棒,但随机数生成器无法通过任何类型的测试)。最近的一个更好的表演节目是Froblins的三月,这是一个与机器人路径计算相关的人工智能程序。这个程序既模拟了大量独立生物,又展示了大规模并行计算如何有利于以角色为中心的娱乐。
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