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Has Supercomputing Hit a Brick Wall?
anzha writes "Horst Simon, Deputy Director of Lawrence Berkeley National Laboratory, has stood up at conferences of late and said the unthinkable: supercomputing is hitting a wall and will not build an exaFLOPS HPC system by 2020. This is defined as one that passes linpack with a performance of one exaFLOPS sustained or better. He's even placed money on it. You can read the original presentation here."
moore's law only talks about transistor counts. building a supercomputer means getting thousands of CPUs to cooperate which is a much harder challenge.
Anyone (with a large wallet) can stick an exoflop worth of CPUs in a large room. by 2020 you'll be able to do that with a not so large wallet. but that does not result in a useful exoflop computer
Clarke's Three Laws are three "laws" of prediction formulated by the British writer Arthur C. Clarke. They are:
1. When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong.
2. The only way of discovering the limits of the possible is to venture a little way past them into the impossible.
3. Any sufficiently advanced technology is indistinguishable from magic.
Prove anything by multiplying Huge Number times Tiny Number
It's a particular nuisance because the speed of light is pretty strictly enforced...
Even if you went full-on-nuts and replaced fiber interconnects with little tubes full of hard vacuum, to squeak out that slight improvement over the speed of light in glass or air, you'll still see latency that meaningfully hinders the cooperation of multi-GHz CPUs and RAM across systems of any nontrivial size.
For loosely coupled problems, that barely matters; but not all problems are loosely coupled.
Power consumption and MTBF: power consumption (high operating costs) be solved perhaps be solved by a larger budget, but the mean time between failures (MTBF) means, that the machine will fail before it can compute anything meaningful. Right know the machines we build, and even more importantly, the software we build rely on all parts of the machine to function. If even a single node fails, then the data it holds becomes inaccessible and the rest of the compute job crashes like a house of cards.
This can be remedied by taking frequent snapshots and then restarting from the last snapshot, but the time for checkpoint/restart has been continuously growing for the last systems. No one really expects exascale systems to do full system checkpoint/restart in a reasonable time frame. They'd spend more time taking snapshots than actually computing.
Source: I'm doing my PhD in supercomputing.
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