I am a Lamport admirer.
I gradually realized that Lamport is more of the godfather of distributed system than Hinton is to deep learning. Lamport is less prominent than Shannon is to information theory. Shannon is the closest to any title of "gold-like" figure to a scientific discipline of universal relevance in modern society.
Lamport specifically revealed a philosophical connections between computer systems and physics, in the parallel of distributed consensus to relativity theory. To me, the enlightenment is that, the relations between events happening in a distributed system, is more fundamental than their absolute ordering, thus the central role of an "observer". I haven't really analyzed if this realization is from Lamport's papers, or my general reading and thinking, but I am moderately confident that general readings are heavily influenced by Lamport's papers, or can be traced back to be compatible with Lamport's thinking. I have not seriously study if this connection is valid in depth, which might be another pure amateur speculation of mine.
One thing I think Lamport falls short is that his writing is not easy to read and understand. I unconciously feel that Lamport (and Dario from Anthropic) probably share a hidden sense of intellectual supriority grew from their own experience throughout their career. So their writing (and Dario's gospel) all share a unchangable sense of narration from their own delicate and graceful ideas, much less of faciliating the understanding to their audience. In this cateogry, Shannon is abosolutely superior in any measure, in his writing, ideas are so naturally presented, although the implications of the ideas remain elusive due to the inherent depth.
Also, among the 3 prominent figures of modern AI: Hinton/Bengio are more like Shannon, Lecun is closer to Lamport.
Enough random rambling. Lamport, as indicated by the outweight presence in this list, is no doubt the single most important scientist in distributed systems.
> the relations between events happening in a distributed system, is more fundamental than their absolute ordering
The important thing in most distributed systems is having an order. Having a single observer serialize events as it receives them is so much more tractable than trying to use absolute order. Using absolute order requires very precise time synchronization which is hard; using absolute order requires knowing when you have received all the reports of events that already happened which is hard. Determining a designated observer isn't typically easy, but having it determine the order it observes events is easy. If two events happen at a similar time it's typically not a big deal which one is considered first as long as all nodes will agree on the result --- let the designated observer just pick the first one it sees works pretty well. If your report takes an unexpectedly long time to make it to the designated observer, then it won't be first and you'll deal.
Much better than trying to figure out unknowable questions of relativity. :P
There is no such thing as a general order; if people accept this then the problem relaxes and we stop trying to design impossible things.
Nodes shouldn't be observers. End users should be the observers. The ordering of event only matters from the perspective of the "I'm adding this to my shopping cart and clicking the buy button and then it said I bought it" story being internally consistent. The observer here is the end user and the end user knows what happens-before relationships were.
Databases should flip this ordering inside out and let the observers (or proxies for them) report what they did in which order. And it just happens that protocols for doing this already exist: distributed tracing encodes these relations.
A meaningful global order doesn't exist and attempts to conjure one only conjure a meaningless one, or one that throws away a lot of turns trying to perfectly order everything from some arbitrary internal observer.
> "I'm adding this to my shopping cart and clicking the buy button and then it said I bought it"
If there's only one of the item to buy and two users wanted to buy it at the same time, it can only be shipped to one of them.
From each user's perspective, their add to cart (and maybe their click to buy) happened before the other user's, but alas, there is only one item available for immediate shipment.
Allowing customers to arbitrate a dispute over control of the item doesn't always go so well; see examples on Black Friday.
Even without a conflict over a mutually exclusive resource, it is valuable in many contexts for all users to share the order of observed events. That's difficult if user agents send events directly to all other agents and report observations to the user as soon as the remote reports arrive.
If A and B both report events and some users see A then B while others see B then A, there may be confusion and a poor user experience. Of course, for some things, relative ordering is unimportant.
> absolute order requires very precise time synchronization which is hard
Presumably relativity is the reason precise time synchronization (and thus absolute ordering) is hard.
That forms a basis for the difficulty. But then there's additional layers of difficulty in real networks where the path between nodes is often asymmetric, and you may also observe that elapsed time (A -> B -> A) is sometimes greater than elapsed time (A -> C -> B -> C -> A) or (A -> C -> B -> A) or (A -> B -> C -> A)
Doesn't each node just need to know its exact path (or latency of same) to the time source each is synchronizing from? The path from node to node doesn't matter because we're ordering log entries from the timestamp of the receiving node.
Imagine you take three atomic clocks, synchronize them, and move each within exactly 1 meter of one of those nodes, directly connected in an identical manner. Relativistic effects are constant. If there are elevation differences, you factor it once and done. The problem with this setup is not relativity. It is quantum uncertainty (and various interference sources, thermal radiation, etc).
How do you know the latency of the path if you haven't synchronized the clocks...
You might be able to control the latency on a LAN, but once you have servers in different locations, good luck. (GPS helps a lot, of course...)
There is this command called 'ping' that tells you that. But in my example, I included the network topology as a known, static factor.
> There is this command called 'ping' that tells you that.
Ping tells you the round trip latency. As I mentioned, asymmetric routing is common and very difficult to measure.
> But in my example, I included the network topology as a known, static factor.
Ok, so spherical cows.
yes, it can be easier to have a central serializer for events, but that certainly makes things problematic for fault tolerance and basically excludes large-scale solutions. using an agreed-upon post-hoc ordering based on timestamps is certainly another way, but really only if you're working in a paradigm that lets you impose it (like mvcc) or doesn't care (like crdt).
personally I find when you view events in a distributed system as a partial order, its more liberating than confusing. its not unusual to assume that there is some kind of canonical event ordering that we have to preserve, when its often just not semantically important. so its a useless constraint that can impose complexity and limit the solution space. the partial ordering exposes the real causal constraints.
Hot take of the day:
Computer scientists are in denial about it, but CS is a branch of theoretical physics, not mathematics. You can point to this or that model of computation, such as lambda calculus or mu-recursive functions and try to claim its abstracted well beyond the particular laws of physics for some specific universe, but they all have some kind of rate limit built into them... and where does the motivation for this idea, that it takes something (time, space, work) to compute something ultimately come from? That's right - from underlying physics itself[1] - from the Bekenstein bound or Bremermann's limit or the like.
Even apparently non-physically-realizable models of computation like non-deterministic Turing machines are ultimately informed by and motivated by concepts in physics... otherwise they would just be examples of chmess[2] and of no interest to anyone. Computer science is of course somewhat abstracted from the details, but no more so than, say, thermodynamics, where concepts like entropy or Gibbs free energy can be studied in the abstract without reference to whether we are talking about a gas of non-interacting molecules or the spins of a bunch of electrons trapped in a lattice.
So, it's of no surprise whatsoever that the fundamental problems of distributed computing are ultimately the same as those found in the relativity of simultaneity[3]. You've all been studying the same things all along, just with different tools and at different levels of abstraction.
[1]: https://en.wikipedia.org/wiki/Limits_of_computation
[2]: https://link.springer.com/article/10.1007/s11245-006-0005-2
[3]: https://en.wikipedia.org/wiki/Relativity_of_simultaneity
Related hot take
Maybe thats why one of the not-so-bad ideas how to go about distributed systems came from a guy who was trained as a physicist and used to complain to his fellow programmers that “a lot of systems actually break the laws of physics”[1]?
> In distributed systems there is no real shared state (imagine one machine in the USA another in Sweden) where is the shared state? In the middle of the Atlantic? - shared state breaks laws of physics. State changes are propagated at the speed of light - we always know how things were at a remote site not how they are now. What we know is what they last told us. If you make a software abstraction that ignores this fact you’ll be in trouble.[2]
[1]: “The Mess We’re In”, 2014 https://www.youtube.com/watch?v=lKXe3HUG2l4 [2]: https://news.ycombinator.com/item?id=19708900
And yet we have CRDTs.
CRDTs are eventually consistent data structures
A thought provoking leading question I have is (1) a space heater throws power into a coil and generates heat and nothing more in the world (2) an LLM into a GPU throws power into the GPU and generates heat and organized information.
Is (2) generating less heat so that the laws of thermodynamics account for the increased amount of "useful stuff" generated (the organized information)? Switch (2) for a bitcoin miner if you prefer.
Is the act of increasing order in the data, somehow costing some energy? Is there a fundamental minimum cost to flipping bits? Something about the switch of energy to matter and back, seems involved here.
I think the nature of computation is holding some secrets about the meaning of the universe, if there is any.
> Is there a fundamental minimum cost to flipping bits?
Yes:
https://en.wikipedia.org/wiki/Landauer%27s_principle
But modern computers are nowhere near this theoretical limit, nor any of the other limits I mentioned above. Nevertheless, most of heat generated from modern CPUs does come from bits turning on and off. Each transistor is a tiny capacitor, that holds a charge when its ON. When it switches OFF, it dumps that charge down the drain, creating waste heat. This is a limitation of our technolgy, not a fundamental limit of physics.
Could be worse, though; early chips would disipate heat even when they weren't doing anything. CMOS improved this enormously by pairing up "complementary" transistors so current only flows when something changes.
Still, from the universe's point of view, what we consider a super advanced computer is a lot closer to a space heater than anything that pushes up against its computational limits. Consider, for example, that quarks operate on time scales of 10^23 Hz, and the universe is happy to run three of those in every proton in every star in the universe. In fact, it runs 10^24 of them for one CPU, and that same CPU can't even simulate the quarks of one proton in real time.
Let's face it: we're like kids in Minecraft who think it's cool watch a calculation of 2+2 trickle through a redstone computer in a minute, while the GPU is rendering a billion triangles every second to give them that view.
I think you should consider the gigawatts of heat generated to create the LLM model to make it a more fair comparison.
I suspect this is old news for you, but just in case you haven't heard of it, check out Feynman's Lectures on Computation. A surprising amount of the book is still relevant, and it's fun how much he always brings everything back to the physics.
> One thing I think Lamport falls short is that his writing is not easy to read and understand
Interesting; I actually grew to be a fellow admirer of Lamport primarily because I actually found his papers to be a lot more approachable and relatively straightforward.