评论
打印
Science and technology
科学技术
Supercomputing
超级计算
Deeper thought
更深奥的思维
The world has a new fastest computer, thanks to video games
多亏电子游戏，让世界拥有了一台新的最快的计算机
The ultimate games machine
终极游戏机
SPEED fanatics that they are, computer nerds like to check the website of Top500, a collaboration between German and American computer scientists that keeps tabs on which of the world's supercomputers is the fastest.
作为速度控，电脑迷们喜欢查看Top500的网站，该网站是由德国和美国的计算机科学家合办，记录世界上最快的超级计算机。
On November 12th the website released its latest list, and unveiled a new champion.
11月12日，该网站发布了最新榜单，揭开了新一任冠军的面纱。
The computer in question is called Titan, and it lives at Oak Ridge National Laboratory, in Tennessee.
获得冠军的计算机名为泰坦，居于田纳西州的橡树岭国家实验室，
It took first place from another American machine, IBM's Sequoia, which is housed at Lawrence Livermore National Laboratory, in California.
它是击败了另一台美国的计算机-IBM的红杉而取得冠军的，红杉位于加利福尼亚州的劳伦斯利物莫国家实验室。
These two machines have helped reassert America's dominance of a list that had, in the past few years, been headed by computers from China and Japan.
这两台计算机使美国重新在Top500榜单上获得优势地位，而在过去数年，中国和日本的计算机一直雄踞榜首。
Titan is different from the previous champion in several ways.
泰坦与之前的冠军在多个方面均有所不同。
For one thing, it is an open system, meaning that scientific researchers with sufficiently thorny problems will be able to bid for time on it, in much the same way that astronomers can bid for time on telescopes.
其一，它是一个开放的系统，意味着科研人员可以争取泰坦的使用时间来解决非常棘手的问题，与天文学家争取天文望远镜使用时间的方式差不多。
Sequoia, by contrast, spends most of its time running hush-hush simulations of exploding nuclear weapons, and is therefore rarely available for public use.
相比之下，红杉将其大多数时间用在绝密的核武器爆炸模拟上，因此很少用于公共用途。
Titan has an unusual design, too.
泰坦的设计也与众不同。
All supercomputers are composed of thousands of processor chips harnessed together.
所有的超级计算机是由成千上万个处理芯片连在一起组成的。
Often, these are derivatives of the central processing units, or CPUs, that sit at the heart of modern, desktop machines.
这些芯片通常都是由现代台式电脑的心脏-中央处理器，即CPU，衍生出来的。
But Titan derives the majority of its oomph—more than 90%—from technology originally developed for the video-game industry.
但是泰坦的大部分性能源于最初由电子游戏行业开发出来的技术。
Half of its 37,376 processors are ordinary CPUs.
泰坦有37376个处理器，其中一半是普通CPU，
But the other half are graphics processing units, or GPUs.
但另外一半是图形处理器，即GPU。
These are specialised devices designed to cope with modern video games, which are some of the most demanding applications any home machine is ever likely to run.
GPU是用于处理现代电子游戏的专业设备，其中一些要求最苛刻的游戏甚至都没有家用机能运行起来。
China's TianHe-1 machine, a previous Top500 champion, was built in the same way, as are 60 other machines in the Top500 list.
前Top500冠军，中国的天河1号也是采用同样的方式搭建的，Top500榜单中还有60台计算机也是用此方式搭建的。
Parallel worlds
并行的世界
Broadly speaking, a CPU—which will be expected to run everything from spreadsheets to voice-recognition software to encoded video—has to be a generalist, competent at every sort of mathematical task but excelling at nothing.
从广义上说，一个CPU—被寄予运行一切事物的厚望，从电子表格到语音视频软件，再到解码视频—必须成为一个全能选手，要胜任所有类型的数学计算任务，但却无一精通。
A GPU, by contrast, is designed to excel at one thing only: manipulating huge numbers of the triangles out of which all modern computer graphics are made.
相比之下，GPU就是为专精一件事而特别设计的：操控大量的三角形，所有现代计算机的图形都是由这些三角形构成的。
Several years ago researchers at Nvidia and AMD realised that many scientific problems which demand huge amounts of computing power—everything from climate simulations and modelling combustion in an engine to seismic analysis for the oil-and-gas industry—could be translated into a form that was digestible by their GPUs.
几年前，英伟达及超微半导体的研究人员意识到许多需要大量运算能力的科学问题—从气候模拟及发动机燃烧方式建模到油气行业的抗震分析—都可以转译成GPU可以理解的形式。
Soon after, supercomputer builders such as Cray began to take notice.
此后不久，这开始引起了超级计算机制造商的注意，如克雷。
Borrowing from the games industry in this way brings several benefits. One big one is efficiency.
以这种方式借鉴游戏行业的技术带来诸多好处。最大的一个好处是效能。
Titan is an upgrade of Oak Ridge's existing Jaguar machine.
泰坦是橡树岭现有的美洲虎超级计算机的升级版。
Upgrading Jaguar with ordinary CPUs would have meant building a computer that sucked around 30MW of electricity when running flat out—enough juice to power a small town.
如使用普通的CPU对美洲虎进行升级，则意味着升级后的计算机在全速运行时将会狂饮大约30MW的电力，这么多电力供给一个小镇都绰绰有余。
Because GPUs are so good at their specialised tasks, Titan can achieve its blistering performance while sipping a modest 8.2MW.
由于GPU非常擅长处理专门性务，泰坦在达到最高性能时只是抿掉8.2MW的电力，不算太多。
It makes sense financially, too, says Sumit Gupta, head of supercomputing at Nvidia.
使用GPU从经济上说也颇有意义，英伟达超级计算业务的负责人苏米特古普塔称。
The chips that the firm sells to supercomputer-makers are almost identical to those it sells to gamers.
英伟达销售给超级计算机制造商的GPU几乎与销售给游戏玩家的GPU完全相同。
As Dr Gupta observes, The history of high-performance computing is littered with the bodies of firms that tried to build products just for the supercomputing market.
据古普塔博士观察，在高性能运算的历史上，遍布着那些只想为超级计算机市场提供产品的公司的尸体，
By itself, it's just too small a niche.
就其自身而言，这个利基市场太过狭小了。
It is not all upsides, though.
然而使用GPU也有不利的一面。
Machines like Titan achieve their speed by breaking a problem into thousands of tiny pieces and farming each out to a single processor.
泰坦这类计算机是将一个问题打散为成千上万个小碎片，然后将每个小碎片分发给单独的处理器运算，从而达到其很高的运算速度。
A helpful analogy, perhaps, is painting a house: one strategy might be to hire a single painter, but it is probably quicker to employ several people and give each a room to do.
或许将其比喻为粉刷房子有助于理解：一种策略是只雇用一名粉刷工，但是多雇几个粉刷工，然后每人刷一个房间很可能会更快一点。
Not all problems are susceptible to being chopped up in such a way, though.
然而不是所有的问题都能以这种方式切分。
The requirement to translate a problem into the sort of mathematics that a GPU can digest adds another barrier.
而将一个问题转译为GPU可以理解的那中数学运算问题也是一个障碍。
Dr Gupta gives the example of the models used to simulate how a car will react in a crash as one problem that has so far resisted what the industry calls the massively parallel approach.
古普塔博士举了模拟汽车碰撞的模型的例子，解决该问题目前仍需采用被行业称为大规模并行的方法。
Clever programmers can sometimes find a way around such issues: ray-tracing, a high-quality, mathematically intense approach to computer graphics that aims to simulate individual light rays, was, ironically, long thought to be the kind of problem that a modern GPU would struggle with.
聪明的程序员有时能够找到绕过这种问题的方法：射线追踪，是一种针对电脑图形的高质量，数学运算频繁的方法，目的是模拟单独的光线。但讽刺的是，射线追踪一直被认为是现代GPU难以处理的一类问题，
Yet at a graphics conference in 2008, a group of researchers from Nvidia announced that they had, nevertheless, found a way to do it.
但在2008年的一次图形大会上，一个来自英伟达的研究团队宣布，尽管这类问题很难，但他们还是找到了处理的方法。
Oak Ridge and Nvidia plan to work with scientists wanting time on Titan to see if their algorithms can be tweaked in similar ways, to make them digestible by the new machine.
橡树岭和英伟达计划和想要使用泰坦的科学家合作，来检查这些科学家的算法是否能够以类似的方式进行微调，使新计算机能够理解这些算法。
Dr Gupta is bullish.
古普塔博士对其表示乐观。
Even the recalcitrant car-crash simulations, he thinks, will yield to the new approach soon.
他认为，即便是顽固的汽车碰撞模拟问题不久也会出现新的解决方法。
But that is not to say that every problem can be made to work.
但这并不是说所有的问题都能解决。
And those scientists who find that they cannot tweak their code may find themselves struggling to take advantage of the ever-rising performance of the world's fastest computers.
对于无法对其编码进行微调的科学家来说，会发现他们很难去利用世界上最快的计算机们不断提升的性能。
