[brightball]

Not really. Think about every object you'd have in Java that's being passed around your system. Now imagine each of those objects are their own processes and you're passing around references to them.

Just on that one case, you've taken huge chunks of a linear execution pattern and parallelized it. Now make that your norm and amplify it to everything. Now realize that the message passing allows this mode of operation to spread each part of this workload over not only more cores but more machines across the network.

And then realize that you can deploy updates to this codes individual parts while other parts continue running without taking down the whole system.

Then you get Erlang.

[BeetleB]

None of what you said will prevent the need for waiting for the majority of numerical algorithms.

No one's disputing Erlang's prowess at parallelism. What the critic in this thread was saying was that you can only get Nx speedup on an N core processor for a limited set of algorithms. Most parallel algorithms will not fall in this category. Amdahl's law is a general truth - it doesn't matter what your architecture/language is. There is nothing special about Erlang that will make any parallel algorithm scale linearly with nodes.

[amorphid]

On top of that, Erlang has a scheduler which reduces the need to rely on the operating system's concurrency model.