CUDA 可以帮助解决什么样的数据处理问题？
我研究过许多数据匹配问题，通常它们都归结为快速并行运行许多 CPU 密集型算法的实现，例如汉明/编辑距离。这是 CUDA 有用的事情吗？
您用它解决了哪些类型的数据处理问题？与标准四核英特尔台式机相比真的有提升吗？
克里斯
I've worked on many data matching problems and very often they boil down to quickly and in parallel running many implementations of CPU intensive algorithms such as Hamming / Edit distance. Is this the kind of thing that CUDA would be useful for?
What kinds of data processing problems have you solved with it? Is there really an uplift over the standard quad-core intel desktop?
Chris
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我想你已经回答了你自己的问题。一般来说，CUDA/OpenCL 可以加速大规模并行操作。我们使用 CUDA 执行各种 DSP 操作（FFT、FIR），并看到了数量级的加速。几百美元的数量级加速是很划算的。虽然像 MKL 和 OpenMP 这样的专用 CPU 库已经给我们带来了相当大的速度提升，但 CUDA/OpenCL 的速度要快得多。
检查此处了解 CUDA 使用示例
I think you've answered your own question. In general, CUDA/OpenCL accelerates massively parallel operations. We've used CUDA to perform various DSP operations (FFT, FIR) and seen order-of-magnitude speedups. Order of magnitude speedups with a couple hundred dollars is a steal. While specialized CPU libraries like MKL and OpenMP have given us quite a speed increase, CUDA/OpenCL is much faster.
Check here for examples of a CUDA usage
例如，在 SIGGRAPH '09 中，他们展示了Vray for Maya 的 CUDA 实现。使用 200 美元的卡实现 20 fps 的实时光线追踪和预览质量？我认为这有很大帮助。
For one, in SIGGRAPH '09 they showed a CUDA implementation of Vray for Maya. Real-time ray-tracing and preview quality at 20-fps with a $200 card? I think it helps greatly.
是的，它是 CUDA 的主要领域。如果满足以下条件，则效率最高：
当然，属于这种情况的任务确实很少。根据您距离它们多远，效率会降低。有时您需要完全重写算法以最大限度地利用。
yes, it is main domain of CUDA. It's efficiency is maximum if following conditions are true:
Of course there are really few tasks that fall into this conditions. Depending on how far you move from them the efficiency will get lower. Sometimes you need to completely rewrite your algorithm to maximize usage.
CUDA 已用于极大提高计算机断层扫描的速度，FASTRA 项目该实例的性能与超级计算机相当（不仅仅是四核台式机！），同时由消费级硬件组装而成，价格为数千欧元。
我知道的其他研究主题是群体优化和实时音频处理。
一般来说：该技术可用于每个领域，其中所有数据必须以相同方式处理，因为所有核心将执行相同的操作。如果您的问题归结为此类操作，那么您就可以开始了:)。可惜并不是所有的东西都属于这一类......
CUDA has been used to vastly improve speeds in computer tomography, the FASTRA project for instance performs on par with supercomputers (not just quad-core desktops!) while being assembled out of consumer-grade hardware for a few thousand euros.
Other research topics I'm aware of are swarm optimization and real-time audio processing.
In general: the technique can be used in every domain where all data must be processed the same way since all cores will perform the same operation. If your problem boils down to this kind of operations you're good to go :). Too bad not everything falls into this category...
并行性一般有两种类型：任务并行性和数据并行性。 CPU 的加速在前者，GPU 的加速在后者。其原因是 CPU 具有复杂的分支预测、乱序执行硬件和多级管道，使它们能够并行执行独立任务（例如，四核上的 4 个独立任务）。另一方面，GPU 已经剥离了大部分控制逻辑，取而代之的是大量 ALU。因此，对于具有数据并行性的任务（简单的，例如矩阵加法），GPU 可以利用其许多 ALU 来并行操作该数据。像汉明距离这样的东西对于 GPU 来说非常有用，因为您只需计算两个字符串之间的差异数量，其中每个字符仅根据位置而不同，并且独立于同一字符串中的任何其他字符。
There are generally two types of parallelism: task parallelism and data parallelism. CPU's accelerate at the former and GPU's at the latter. The reason for this is that CPU's have sophisticated branch prediction, out-of-order execution hardware and many-stage pipelines that let them execute independent tasks in parallel (e.g. 4 independent tasks on a quad-core). GPU's, on the other hand, have stripped out most of the control logic and instead have lots of ALU's. Thus, for tasks with data-parallelism (simple e.g. matrix addition) the GPU can take advantage of its many ALU's to operate on this data in parallel. Something like Hamming distance would be great for a GPU since you're just counting the number of differences between two strings, where each character is different based only on the position, and independent of any other character in the same string.