(翻译)超越图像——通用 GPU 的现状和展望——Folding@home

标题:Beyond Graphics – The Present and Future of GP-GPU – Folding@Home / 超越图像——通用 GPU 的现状和展望——Folding@home
概要:简要介绍了当前 Folding@home 的 GPU 客户端的一些情况、统计和它与其它客户端的比较。

Stanford University runs one of the most popular distributed computing applications around, Folding@Home. It calculates protein folding on a massive scale, using thousands of computers and PlayStation 3s around the world. The idea is to better understand how proteins "fold" or assemble themselves, and how the process goes wrong sometimes—which is thought to be at the heart of diseases like Alzheimer’s and Parkinson’s.
斯坦福大学主持着现在最流行的分布式计算项目之一,Folding@home。这个项目利用了全世界数以千计的个人计算机和 PS3 来进行大规模的蛋白质折叠模拟计算,目的就是希望能对蛋白质如何组装和“折叠”起来的过程有更深入的认识,这样的话我们就能明白为什么这个过程有时候会出差错——这被认为是阿兹海默氏症和帕金森氏症这一类疾病的核心原因。

For awhile now the labs at Stanford have been working with ATI to produce a GPU-accelerated version of their folding software. Now, the second generation of this GPU folding app is freely available in beta, and it uses the more reliable and better-performing CUDA for Nvidia GPUs, or CAL for ATI cards.
斯坦福大学的研究人员已经和 ATI 一起开发用 GPU 加速的蛋白质折叠计算程序有一段时间了。现在,他们的第二代 GPU 客户端 beta 版已经发布,这个版本的客户端使用的是更可靠和性能更高的平台:对于 NVIDIA 的 GPU 来说是 CUDA,对应 ATI 的就是 CAL。

A quick look at the Client Stats page shows that there are, at the time of this writing, about 7600 active GPUs running the FAH app, generating around 840 teraflops of computational power (it’s not a theoretical peak number, it’s real work running the FAH computations). That’s somewhere around 110 gigaflops per GPU, on average. To put that in perspective, the regular windows CPU client is about one gigaflop per client (it’s a mix of the single-threaded client and the multi-core SMP version). The PS3 looks like it leads the pack with a total of 1,358 teraflops, but that’s from over 48,000 active PS3s. Each PS3 is actually delivering about 28 gigaflops apiece.
在撰写本文时,我瞄了一眼项目的客户端统计,当前大概有 7600 颗“活跃”的 GPU(在这里意味着它们在过去的7天内至少完成了一个工作包),它们贡献的计算能力大概是 840 Teraflops。这就是说,大概每颗 GPU 能贡献 110 Gigaflops 的运算能力。与之形成对比的是,每个普通的 Windows CPU 客户端大概只能贡献 1 Gigaflops 的运算能力(在这里我们没有区分单 CPU 客户端和 SMP 客户端)。PS3 客户端以总计贡献 1358 Teraflops 遥遥领先,但“活跃”的 PS3 超过了 48000 台,也就是说每台 PS3 平均只贡献大约 28 Teraflops 的计算能力。

In other words, the average GPU client is four times faster than a PS3. That includes relatively older and low-end graphics cards, too. Newer cards are likely six to eight times faster.
换言之,GPU 客户端平均比 PS3 要快四倍,这其中还包括了一些老显卡和低端显卡。新显卡的话大概要比 PS3 快上六至八倍。

Right now, Nvidia’s cards are better folders, due primarily to better optimized code. With the latest drivers, most GeForce cards are getting pretty close to peak utilization. ATI’s cards, which rely on their CAL driver, still seem to have a lot of headroom. In fact, the new Radeon HD 4800 have 800 stream processors, but the current client runs on them as if they were older cards with only 320.
目前为止,由于代码进行了较好的优化,NVIDIA 的显卡在蛋白质折叠方面的性能比较高。在最新的驱动下,绝大部分 Geforce 显卡都能基本达到峰值运算水平。基于 CAL 驱动的 ATI 显卡看起来则是还有不小的提升空间。实际上,新推出的 Radeon HD 4800 拥有 800 个流处理器,但现在的客户端把它看作老显卡,只利用了其中的 320 个。

The GPU2 Folding@Home client itself, and the CAL/CUDA drivers from the graphics manufacturers, still need some optimization. The performance, display features, and reliability are all going to improve over the coming weeks. It’s simply too early to compare one architecture or graphics card against another with regards to FAH. Still, the current client is stable and fast enough to be usable, and you should use it if you can.
不论是 Folding@home 的 GPU2 客户端还是显卡制造商的 CAL/CUDA 驱动也还都需要进一步的优化。在未来的一段时间内,性能、显示图形和可靠性还会进一步提高。现在对于在 FAH 上比较两个架构的显卡来说还是太早了一点。毕竟,现在的客户端对于使用来说已经足够可靠和高速了,您应该尽可能的使用它。

Over here, we all band together on the truly impressive DL.TV folding team, currently #10 in the worldwide rankings and climbing fast. We’d love it if you devoted some of your computer power to helping discover cures for some truly nasty diseases with us. Just join team 57391 when you install Folding@Home and you’re all set. You can see some of our team stats here.
在这里,我们在现在世界排名第十的团队 DL.TV 一起努力,我们前进得很快,成绩也是惊人的。我们希望您能够跟我们一起投入您的空余计算能力来帮助科学家们发现一些麻烦疾病的疗法。在您安装 Folding@home 时,请加入团队 57391。您可以在这里看到我们队的统计。(译注:本网站推荐的团队是中国团队 China Folding Power,编号 3213)





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