How do you rapidly record the output from your three million dollar analog computer in the 1940s when the results are only available on analog meters? The team responsible for the Westinghouse 1947 AC Network Calculator at Georgia Tech was faced with just this problem and came up with a nifty solution — hack the control panel and wire in a special-purpose drafting table.
What Is It?
What is this beast of a computer? Machines of this type were developed during and after World War 2, and strictly speaking, belong in the category of scale models rather than true computers. Although these machines were very flexible, they were primarily designed to simulate power distribution grids. There is a lot of theory under the hood, but basically a real world, multi-phase distribution system would be scaled to single-phase at 400 Hz for modeling.
The engineers would “program” the machine by connecting together the appropriate circuit elements (like capacitors, inductors, transmission lines, generators, etc.) on big patch panels. Thus programmed, a 10 kW motor-generator located in the basement would be started up and the simulation was underway. Continue reading “The Modding, Restoration, And Demise Of A $3M Analog Computer”
With all the trained academics who have pored over the Antikythera mechanism in the 120 years since it was pulled from the Mediterranean Sea, you’d think all of the features of the ancient analog computer would have been discovered by now. But the mechanism still holds secrets, some of which can only be appreciated by someone in tune with the original maker of the device. At least that what appears to have happened with the recent discovery of a hitherto unknown lunar calendar in the Antikythera mechanism. (Video, embedded below.)
The Antikythera mechanism is fascinating in its own right, but the real treat here is that this discovery comes from one of our own community — [Chris] at Clickspring, maker of amazing clocks and other mechanical works of art. When he undertook a reproduction of the Antikythera mechanism using nothing but period-correct materials and tools four years ago, he had no idea that the effort would take the direction it has. The video below — also on Vimeo — sums up the serendipitous discovery, which is based on the unusual number of divisions etched into one of the rings of the mechanisms. Scholars had dismissed this as a mistake, but having walked a mile in the shoes of the mechanism’s creator, [Chris] knew better.
The craftsmanship and ingenuity evidenced in the original led [Chris] and his collaborators to the conclusion that the calendar ring is actually a 354-day calendar that reflects a lunar cycle rather than a solar cycle. The findings are summarized in a scholarly paper in the Horological Journal. Getting a paper accepted in a peer-reviewed journal is no mean feat, so hats off to the authors for not only finding this long-lost feature of the Antikythera mechanism and figuring out its significance, but also for persisting through the writing and publication process while putting other projects on hold. Clickspring fans have extra reason to rejoice, too — more videos are now on the way!
Continue reading “Hacker’s Discovery Changes Understanding Of The Antikythera Mechanism”
In the grand scheme of things, it really wasn’t all that long ago that a slide rule was part of an engineer’s every day equipment. Long before electronic calculators came along, a couple of sticks of wood inscribed with accurate scales was all it took to do everything from simple multiplication to logarithms and trig functions.
While finding a slide rule these days isn’t impossible, it’s still not exactly easy, and buying one off the shelf isn’t as fun or as instructive as building one yourself. [JavierL90]’s slide rule build started, ironically enough, on the computer, with a Python program designed to graphically plot the various scales needed for the fixed sections of the slide rules (the “stators”) and the moving bit (the “slide”). His first throught was to laser-engrave the scales, but the route of printing them onto self-adhesive vinyl stock proved to be easier.
With the scale squared away, work turned to the mechanism itself. He chose walnut for the wood, aluminum for the brackets, and a 3D-printed frame holding a thin acrylic window for the sliding cursor. The woodworking is simple but well-done, as is the metalwork. We especially like the method used to create the cursor line — a simple line scored into the acrylic with a razor, which was then filled with red inks. The assembled slide rule is a thing of beauty, looking for all the world like a commercial model, especially when decked out with its custom faux leather carry case.
We have to admit that the use of a slide rule is a life skill that passed us by, but seeing this puts us in the mood for another try. We might have to start really, really simple and work up from there.
Today, most of what we think of as a computer uses digital technology. But that wasn’t always the case. From slide rules to mechanical fire solution computers to electronic analog computers, there have been plenty of computers that don’t work on 1s and 0s, but on analog quantities such as angle or voltage. [Ken Shirriff] is working to restore an analog computer from around 1969 provided by [CuriousMarc]. He’ll probably write a few posts, but this month’s one focuses on the op-amps.
For an electronic analog computer, the op-amp was the main processing element. You could feed multiple voltages in to do addition, and gain works for multiplication. If you add a capacitor, you can do integration. But there’s a problem.
Continue reading “Secrets From A 1969 Analog Computer”
When your only tool is a hammer, everything starts to look like a nail. That’s an old saying and perhaps somewhat obvious, but our tools do color our solutions and sometimes in very subtle ways. For example, using a computer causes our solutions to take a certain shape, especially related to numbers. A digital computer deals with numbers as integers and anything that isn’t is actually some representation with some limit. Sure, an IEEE floating point number has a wide range, but there’s still some discrete step between one and the next nearest that you can’t reduce. Even if you treat numbers as arbitrary text strings or fractions, the digital nature of computers will color your solution. But there are other ways to do computing, and they affect your outcome differently. That’s why [Bill Schweber’s] analog computation series caught our eye.
One great example of analog vs digital methods is reading an arbitrary analog quantity, say a voltage, a temperature, or a shaft position. In the digital domain, there’s some converter that has a certain number of bits. You can get that number of bits to something ridiculous, of course, but it isn’t easy. The fewer bits, the less you can understand the real-world quantity.
For example, you could consider a single comparator to be a one-bit analog to digital converter, but all you can tell then is if the number is above or below a certain value. A two-bit converter would let you break a 0-3V signal into 1V steps. But a cheap and simple potentiometer can divide a 0-3V signal into a virtually infinite number of smaller voltages. Sure there’s some physical limit to the pot, and we suppose at some level many physical values are quantized due to the physics, but those are infinitesimal compared to a dozen or so bits of a converter. On top of that, sampled signals are measured at discrete time points which changes certain things and leads to effects like aliasing, for example.
Continue reading “Continuous Computing The Analog Way”
We were always taught that the fundamental passive components were resistors, capacitors, and inductors. But in 1971, [Leon Chua] introduced the idea of a memristor — a sort of resistor with memory. HP created one in 2008 and since then we haven’t really had the burning need to use one. In a recent Nature article, [Mohammed Zidan] and others discuss a 32 by 32 memristor array on a chip they call a memory processing unit. This analog computer on a chip is useful for certain kinds of operations that CPUs are historically not efficient at, including solving differential equations. Other applications include matrix operations used in things like machine learning and weather prediction. The paper is behind a paywall, although the usual places to find scholarly papers will probably have it soon.
There are several key ideas for using these analog elements for high-precision computing. First, the array is set up in a passive crossbar arrangement. In addition, the memristors are quantized so that different resistance values represent different numbers. For example, a memristor element that could have 16 different resistance values would allow it to operate as a base-16 digit.
Continue reading “Memristors On A Chip Solve Partial Differential Equations”
I’ll be brutally honest. When I set out to write this post, I was going to talk about IBM’s Q Experience — the website where you can run real code on some older IBM quantum computing hardware. I am going to get to that — I promise — but that’s going to have to wait for another time. It turns out that quantum computing is mindbending and — to make matters worse — there are a lot of oversimplifications floating around that make it even harder to understand than it ought to be. Because the IBM system matches up with real hardware, it is has a lot more limitations than a simulator — think of programming a microcontroller with on debugging versus using a software emulator. You can zoom into any level of detail with the emulator but with the bare micro you can toggle a line, use a scope, and hope things don’t go too far wrong.
So before we get to the real quantum hardware, I am going to show you a simulator written by [Craig Gidney]. He wrote it and promptly got a job with Google, who took over the project. Sort of. Even if you don’t like working in a browser, [Craig’s] simulator is easy enough, you don’t need an account, and a bookmark will save your work.
It isn’t the only available simulator, but as [Craig] immodestly (but correctly) points out, his simulator is much better than IBM’s. Starting with the simulator avoids tripping on the hardware limitations. For example, IBM’s devices are not fully connected, like a CPU where only some registers can get to other registers. In addition, real devices have to deal with noise and the quantum states not lasting very long. If your algorithm is too slow, your program will collapse and invalidate your results. These aren’t issues on a simulator. You can find a list of other simulators, but I’m focusing on Quirk.
What Quantum Computing Is
As I mentioned, there is a lot of misinformation about quantum computing (QC) floating around. I think part of it revolves around the word computing. If you are old enough to remember analog computers, QC is much more like that. You build “circuits” to create results. There’s also a lot of difficult math — mostly linear algebra — that I’m going to try to avoid as much as possible. However, if you can dig into the math, it is worth your time to do so. However, just like you can design a resonant circuit without solving differential equations about inductors, I think you can do QC without some of the bigger math by just using results. We’ll see how well that holds up in practice.
Continue reading “Quantum Weirdness In Your Browser”