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Joe Armstrong You can do it again. It s not so much remembering. When I say you can remember a program exactly, I don t think that it s actually remembering. But you can do it again. If Bill could remember the actual text, I can t do that. But I can certainly remember the structure for quite a long time. Seibel: Is Erlang-style message passing a silver bullet for slaying the problem of concurrent programming Armstrong: Oh, it s not. It s an improvement. It s a lot better than shared memory programming. I think that s the one thing Erlang has done it has actually demonstrated that. When we first did Erlang and we went to conferences and said, You should copy all your data. And I think they accepted the arguments over fault tolerance the reason you copy all your data is to make the system fault tolerant. They said, It ll be terribly inefficient if you do that, and we said, Yeah, it will but it ll be fault tolerant. The thing that is surprising is that it s more efficient in certain circumstances. What we did for the reasons of fault tolerance, turned out to be, in many circumstances, just as efficient or even more efficient than sharing. Then we asked the question, Why is that Because it increased the concurrency. When you re sharing, you ve got to lock your data when you access it. And you ve forgotten about the cost of the locks. And maybe the amount of data you re copying isn t that big. If the amount of data you re copying is pretty small and if you re doing lots of updates and accesses and lots of locks, suddenly it s not so bad to copy everything. And then on the multicores, if you ve got the old sharing model, the locks can stop all the cores. You ve got a thousand-core CPU and one program does a global lock all the thousand cores have got to stop. I m also very skeptical about implicit parallelism. Your programming language can have parallel constructs but if it doesn t map into hardware that s parallel, if it s just being emulated by your programming system, it s not a benefit. So there are three types of hardware parallelism. There s pipeline parallelism so you make a deeper pipeline in the chip so you can do things in parallel. Well, that s once and for all when you design
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Joe Armstrong the chip. A normal programmer can t do anything about the instructionlevel parallelism. There s data parallelism, which is not really parallelism but it has to do with cache behavior. If you want to make a C program go efficiently, if *p is on a 16-byte boundary, if you access *p, then the access to *(p + 1) is free, basically, because the cache line pulls it in. Then you need to worry about how wide the cache lines are how many bytes do you pull in in one cache transfer That s data parallelism, which the programmer can use by being very careful about their structures and knowing exactly how it s laid out in memory. Messy stuff you don t really want to do that. The other source of real concurrency in the chip are multicores. There ll be 32 cores by the end of the decade and a million cores by 2019 or whatever. So you have to take the granules of concurrency in your program and map them onto the cores of the computer. Of course that s quite a heavyweight operation. Starting a computation on a different core and getting the answer back is itself something that takes time. So if you re just adding two numbers together, it s just not worth the effort you re spending more effort in moving it to another core and doing it and getting the answer back than you are in doing it in place. Erlang s quite well suited there because the programmer has said, I want a process, I want another process, I want another process. Then we just put them on the cores. And maybe we should be thinking about actually physically placing them on cores. Probably a process that spawns another process talks to that process. So if we put it on a core that s physically near, that s a good place to put it, not on one that s a long way away. And maybe if we know it s not going to talk to it a lot maybe we can put it a long way away. And maybe processes that do I/O should be near the edge of the chip the ones that talk to the I/O processes. As the chips get bigger we re going to have to think about how getting data to the middle of the chip is going to cost more than getting it to the edge of the chip. Maybe you ve got two or three servers and a database and maybe you re going to map this onto the cores so we ll put the database in the middle of the chip and these ones talk to the client so we ll put them near the edge of the chip. I don t know this is research.
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