真的，你的笔记本电脑赛过千万亿台 60 年前的大型机
大型机
![](/data/attachment/album/202112/28/233034wltru5h9rwpw9yll.jpg)


每当我听到有人大谈特谈我们现在的计算机要比上世纪 60 年代阿波罗登月时期的计算机强大得多时，我都会感到震惊。这些比较通常严重低估了差异。


1961 年，世界上的一些大学已经购买了 IBM 7090 大型机。7090 系列是第一个全晶体管计算机系列，以今天的货币计算，它的成本为 2000 万美元，相当于今天的顶级笔记本电脑的 6000 倍。它的早期买家通常将计算机作为整个校园的共享资源来部署。很少有用户能幸运地每周获得一小时的计算机时间。


7090 的时钟周期为 2.18 微秒，因此其工作频率略低于 500 KHz。但在那个年代，计算机指令没有流水线执行，所以大多数指令需要一个以上的时钟周期来执行。一些整数运算需要 14 个时钟周期，而一个浮点运算可能需要 15 个时钟周期。因此，通常估计 7090 每秒钟可以执行约 10 万条指令。而大多数现代计算机的 CPU 核心能够以每秒 30 亿条指令的速度持续运行，甚至其峰值速度要快得多。这就是 3 万倍的差距，所以一个有四核或八核的现代芯片很容易达到比 7090 快 10 万倍的运算速度。


与 1961 年那个得到一小时计算机时间的幸运儿不同，你可以一直运行你的笔记本电脑，每周你可以累计得到超过 1900 年的 7090 计算机时间。(我不想问其中有多少时间是花在“我的世界”游戏上的）。


继续这个比较，考虑一下训练流行的自然语言人工智能模型 GPT-3 所需的指令数量。在云服务器上执行这些指令相当于 355 年的笔记本电脑时间，这在 7090 上就是超过 3600 万年。当你等待这项工作完成时，你会需要大量的咖啡。


但是，说真的，这种比较对今天的电脑还是不公平的。你的笔记本电脑可能有 16 GB 的主内存。而 7090 的最大内存为 144 KB。要运行同样的程序，需要将大量的数据从 7090 中换入换出，而且必须使用磁带来完成。当时最好的磁带机的最大数据传输率为每秒 60KB。虽然一台 7090 计算机上可以连接 12 个磁带设备，但这个速率是在它们之间共享的。而且，这种共享需要一组人类操作员来交换驱动器上的磁带；以这种方式读取（或写入）16GB 的数据将需要三天时间。因此，与今天的速度相比，数据传输也慢了大约 10 万倍。


所以现在 7090 看起来运行速度是你 2021 年的笔记本电脑的千万亿分之一（10<sup> -15</sup>）。在现代的笔记本电脑上运行一周的计算任务，就算是 7090 从宇宙诞生就开始运行也做不完。


但等等，不止如此。笔记本电脑中的每个核心都有内置的 SIMD（单指令、多数据）扩展，在用于向量运算时可以大幅提升浮点运算速度。而在 7090 上甚至没有这些东西的影子。然后是 GPU，最初用于图形加速，但现在用于大部分的人工智能学习，如训练 GPT-3。最新的 iPhone 芯片 A15 Bionic 上就不止一个 GPU，而是有五个 GPU，以及一个额外的神经引擎，在我们所做的所有其他比较的基础上，它可以每秒运行 15 万亿次算术运算。


短短 60 年的差异让人难以置信。但我想知道，我们是否有效地利用了所有这些计算能力，可以像我们的祖先从纸笔到 7090 的飞跃那样做出改变？


这篇文章将以《摩尔如此之多》为题发表在 2022 年 1 月的印刷版上。




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via: <https://spectrum.ieee.org/ibm-mainframe> 


作者：[Rodney Brooks](https://spectrum.ieee.org/u/rodney-brooks) 译者：[wxy](https://github.com/wxy) 校对：[wxy](https://github.com/wxy)


本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](/article-14123-1.html) 荣誉推出


OK
**Whenever I hear someone rhapsodize **about how much more computer power we have now compared with what was available in the 1960s during the Apollo era, I cringe. Those comparisons usually grossly *underestimate* the difference.

By 1961, a few universities around the world had bought [IBM 7090 mainframes](https://www.ibm.com/ibm/history/exhibits/mainframe/mainframe_PP7090.html). The 7090 was the first line of all-transistor computers, and it cost US $20 million in today's money, or about 6,000 times as much as a top-of-the-line laptop today. Its early buyers typically deployed the computers as a shared resource for an entire campus. Very few users were fortunate enough to get as much as an hour of computer time per week.

The 7090 had a clock cycle of 2.18 microseconds, so the operating frequency was just under 500 kilohertz. But in those days, instructions were not [pipelined](https://cs.stanford.edu/people/eroberts/courses/soco/projects/risc/pipelining/index.html), so most took more than one cycle to execute. Some integer arithmetic took up to 14 cycles, and a floating-point operation could hog up to 15. So the 7090 is generally estimated to have executed about 100,000 instructions per second. Most modern computer cores can operate at a sustained rate of 3 billion instructions per second, with much faster peak speeds. That is 30,000 times as fast, so a modern chip with four or eight cores is easily 100,000 times as fast.

Unlike the lucky person in 1961 who got an hour of computer time, you can run your laptop all the time, racking up more than 1,900 years of 7090 computer time every week. (Far be it from me to ask how many of those hours are spent on *Minecraft*.)

Continuing with this comparison, consider the number of instructions needed to train the popular natural-language AI model, [GPT-3](https://spectrum.ieee.org/open-ais-powerful-text-generating-tool-is-ready-for-business). Executing them on cloud servers took the equivalent of 355 years of laptop time, which translates to more than 36 million years on the 7090. You’d need a *lot* of coffee as you waited for that job to finish.

A week of computing time on a modern laptop would take *longer than the age of the universe* on the 7090.

But, really, this comparison is unfair to today’s computers. Your laptop probably has 16 gigabytes of main memory. The 7090 maxed out at 144 kilobytes. To run the same program would require an awful lot of shuffling of data into and out of the 7090—and it would have to be done using [magnetic tapes](https://spectrum.ieee.org/tape-is-back-and-better-than-ever). The best tape drives in those days had maximum data-transfer rates of 60 KB per second. Although 12 tape units could be attached to a single 7090 computer, that rate needed to be shared among them. But such sharing would require that a group of human operators swap tapes on the drives; to read (or write) 16 GB of data this way would take three days. So data transfer, too, was slower by a factor of about 100,000 compared with today’s rate.

So now the 7090 looks to have run at about a quadrillionth (10-15) the speed of your 2021 laptop. A week of computing time on a modern laptop would take *longer than the age of the universe* on the 7090.

But wait, there’s more! Each core in your laptop has built-in [SIMD](https://www.sciencedirect.com/topics/computer-science/single-instruction-multiple-data) (single instruction, multiple data) extensions that turbocharge floating-point arithmetic, used for vector operations. Not even a whiff of those on the 7090. And then there’s the GPU, originally used for graphics speedup, but now used for the bulk of AI learning such as in training [GPT-3](https://spectrum.ieee.org/tag/gpt-3). And the latest iPhone chip, the A15 Bionic, has not one, but five GPUs, as well as a bonus neural engine that runs 15 trillion arithmetic operations per second on top of all the other comparisons we have made.

The difference in just 60 years is mind boggling. But I wonder, are we using all that computation effectively to make as much difference as our forebears did after the leap from pencil and paper to the 7090?

*This article appears in the January 2022 print issue as “So Much Moore.”*

[Rodney Brooks](https://spectrum.ieee.org/u/rodney-brooks)

[Rodney Brooks](https://rodneybrooks.com/) is the [Panasonic Professor of Robotics (emeritus) at MIT](https://people.csail.mit.edu/brooks/), where he was director of the AI Lab and then [CSAIL](https://www.csail.mit.edu/). He has been cofounder of [iRobot](https://www.irobot.com/), [Rethink Robotics](https://spectrum.ieee.org/robotics/industrial-robots/rethink-robotics-baxter-robot-factory-worker), and [Robust AI](https://www.robust.ai/), where he is currently CTO.