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劳伦斯利弗莫尔国家实验室(Lawrence Livermore National Laboratory)的科学家们正在加紧构建一台新的超级计算机,称为“红杉”(Sequoia),并且希望一旦在2012年底完成,它能成为世界上最强大的计算机。

As I reported recently in Newsweek, scientists at Lawrence Livermore National Laboratory are racing to build a new supercomputer, called Sequoia, that they hope will be the most powerful machine in the world once it’s completed by the end of 2012.

正如我最近发表在《新闻周刊》上的报道,劳伦斯利弗莫尔国家实验室(Lawrence Livermore National Laboratory)的科学家们正在加紧构建一台新的超级计算机,称为“红杉”(Sequoia),并且希望一旦在2012年底完成,它能成为世界上最强大的计算机。

Meanwhile, another team at Oak Ridge National Laboratory has announced plans to build a machine of comparable power.

与此同时,橡树岭国家实验室(Oak Ridge National Laboratory)的另一个团队宣布了构建有类似功能计算机的计划。

And yet another team at the National Center for Supercomputing Applications at the University of Illinois is assembling a supercomputer that will be not quite as mighty but will still rank among the most powerful machines in the world.

而伊利诺伊大学 (University of Illinois) 国家超级电脑应用中心(NCSA)的另一个团队正在组装一台超级计算机,虽然其功能没有那么强大,但是仍然会跻身于世界最强大计算机之列。

But there’s one big thing missing, according to people in the supercomputing community—we need more people who know how to make use of all this computing power. “It’s not enough to keep building powerful supercomputers unless we have the brains,” says Stan Ahalt, a 20-year supercomputing veteran and the director of a supercomputing center with its headquarters at the University of North Carolina at Chapel Hill. “Think of a supercomputer as a very fast racing engine. We need more drivers to use those engines.”

然而,据超级计算机社区的人说,还缺少一个大部件——我们需要更多懂得如何应用所有这些计算功能的人。“只是不断构建强大的超级计算机还不行,我们还要拥有这些优秀人才”。斯坦·阿豪特(Stan Ahalt)说。他有20年研发超级计算机的经验,是总部位于教堂山(Chapel Hill)北卡罗莱纳大学超级计算机中心主任。“把超级计算机想像为非常快的赛车引擎,而我们需要更多操纵那些引擎的司机。”

Supercomputers are used in a wide range of applications, from climate modeling to designing airplanes. They are far more complex than ordinary laptops and desktop computers. Some use hundreds of thousands of microprocessors. Writing software programs that can split work up among all those microprocessors is a daunting task and requires lots of specialized training, way beyond what the average computer science student gets.


The Blue Gene supercomputer at Lawrence Livermore National Laboratory in California., Frederic Larson, The San Francisco Chronicle / Corbis

加利福尼亚劳伦斯利弗莫尔国家实验室蓝色基因(Blue Gene)超级计算机。《旧金山纪事报》弗罗德里克·拉森/考比斯

Scientists refer to the talent shortage as the “missing middle,” meaning there are enough specialists to run the handful of world-beating supercomputers that cost a few hundred million dollars, and plenty of people who can manage ordinary personal computers and server computer—but there are not nearly enough people who know how to use the small and mid-sized high-performance machines that cost anywhere from $1 million to $10 million.

科学家把这种人才短缺称为“缺失中间人”(missing middle)。意思是说,我们有足够运行价值数亿美元为数不多一流超级计算机的专家,也有大批能够管理普通个人电脑和服务器计算机的人——但是知道如何使用价值在1百万至1千万美元之间的那些中小型高性能计算机的人才远远不够。

Those small and mid-sized machines have huge potential to help companies. In health care, for example, supercomputers could let scientists sift through mountains of data about patient populations to determine which treatments work and which ones don’t. Supercomputers could play a key role in genomics research that could let pharmaceutical companies develop drugs tailored to individual patients.


To get there, however, we need to start training more students. “It’s not that we’ve had a drop-off in enrollments, it’s that we need an increase,” says Jack Dongarra, a supercomputer specialist at the University of Tennessee. “We need people who can build the applications and algorithms needed to effectively use the equipment.”

然而,要做到这一点,我们需要开始培训更多的学生。“这并不是说我们培训的人数下降,而是说我们需要增加培训的人数。”田纳西大学(University of Tennessee)超级计算机专家杰克·唐加拉(Jack Dongarra)说,“我们需要能够构建有效使用这种设备所需的应用程序和算法的人才。”

Wu Feng, a professor of supercomputing at Virginia Polytechnic whose work focuses on small and mid-sized supercomputers, says companies are so hungry for talent that “I’ve even had different parts of a single company vying for my students. They’re in high demand.”

弗吉尼亚理工大学(Virginia Polytechnic)超级计算机教授吴风(音)主要致力于中小型超级计算机研发工作。他说,各家公司对人才的需求如此迫切,“甚至曾经有一家公司的不同部门在争夺我的学生。他们供不应求。”

Feng has 15 graduate students, most of them from other countries. That’s another issue. Ahalt says in the past, U.S. supercomputing centers could attract top students from around the world, train them and keep them here.


But now many students leave the U.S. after getting their education because there are better opportunities in countries like India and China. And the U.S. is not training enough American students to fill the gap.


“We need to attract more young people to come into this very arcane field,” Ahalt says. “We need a new generation of scientists and engineers who know how to use supercomputers to solve complex engineering and science problems.”


One attempt to address the shortage is the Virtual School for Computational Science and Engineering, a program that offers online courses for graduate students who want to learn how to use high-performance computers.


This year 1,000 students participated, up from 40 in 2008 when the program began, says Thom Dunning, director of the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign.

托姆·邓宁(Thom Dunning)是伊利诺伊大学香槟分校(University of Illinois at Urbana-Champaign)国家超级计算应用中心主任。据他透露,今年参加学习的学生有1000人,而2008年计划开始时参加学习的人数只有40人。

“This is really encouraging,” Dunning says. “We have a younger generation that’s willing to roll up its sleeves and learn how to program these big beasts.”


Another step is a consortium sponsored by NCSA and others whose goal is to get smaller companies using high-performance computers to design better products. NCSA will let small companies use its supercomputers, and will train people inside those companies to write programs to run on high-performance computers.


“We want to step outside this small group of very skilled experts, and build a broader class of people who can use this very specialized capability,” Dunning says.


If we don’t, Ahalt says, we risk falling behind rivals. China, for example, has recognized that high-performance computing creates economic advantages and is training huge numbers of scientists and engineers to use the machines.


“China already owns the market for low-end manufacturing, and with supercomputer expertise they will increasingly move into high-end manufacturing,” Ahalt says.


Falling behind in computing could mean falling behind in fields that rely on computation to get an edge on rivals. “What if we lose companies like Boeing, GE, Westinghouse, and IBM, as employers in the U.S.?” Ahalt says.


Today that seems unthinkable. Ahalt and his peers in the supercomputer community are hoping it stays that way.


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