Comment Both supercomputers and clusters are usefull. (Score 1) 346
Writing code for clusters with much larger latencies than those supercomputers is more difficult. Parallel coding by itself is already an art form. Scientists want to think about the science behind their problem, not the technical details behind the parallelization of their code. And the more complex your code, the more likely it is that you make mistakes, and that is a big problem for simulations that take a long time to complete.
However, the problem at small universities and these expensive "super computers" they own, like the enterprise 10000 we have, is that they are intended to be replaced a lot less frequent than a desktop workstation. At first your code will run a lot faster on a few nodes of such a system, but it's gained upon by a standard workstation quite fast. So when you're halfway through the time you are stuck with the super computer, your new ultra cheap workstation will outperform the expensive supercomp on problems that require small latencies, or scale badly. A cluster is often much cheaper to update.
So for smaller facilities, where most of the jobs that are submitted are allowed to use up to 8 nodes for example, I would use clusters and update the network infrastructure and CPU's as often as is possible. For large jobs I don't think we can do without nationally owned, big supercomputers. There simply is a group of problems that require these supercomputers, and where clusters can't be used. And these national science centers can of course maintain different kinds of supercomputers. If your problem requires low latency, use the supercomp, if it doesn't, use the cluster.
The authors propose to give scientists money to buy their own clusters, but I already saw calls for proposals (where you can apply for a research grant) where you could either reserve computing power on the national super computers, or get money to buy a cluster, or otherwise spend it on computer hardware. Of course the real question stated in the article is whether a country like America needs to have the fastest supercomputer. I guess that question is just a political one, as is the question whether that country would need the biggest storage facility in the world.
I also do not really understand the storage facility thing. Storage is not something I would expect you need temporarily. Only intermediate results are temporary, but the data in the big databases they mention seem there to stay. Once you've bought storage for one project, you can not allocate the storage to another project like in the case of supercomputing, where a project takes a month and then the power is handed to another project. If you have got such a project that needs lots of permanent storage space, why then not give THEM money to built such a storage system. Every university nowadays is on a fast line, and I don't see why that has to be central. Even storage divided among groups of researchers does not have to reside on a super data center. Just build systems for every requirement, with room to spare. Or am I missing something?
However, the problem at small universities and these expensive "super computers" they own, like the enterprise 10000 we have, is that they are intended to be replaced a lot less frequent than a desktop workstation. At first your code will run a lot faster on a few nodes of such a system, but it's gained upon by a standard workstation quite fast. So when you're halfway through the time you are stuck with the super computer, your new ultra cheap workstation will outperform the expensive supercomp on problems that require small latencies, or scale badly. A cluster is often much cheaper to update.
So for smaller facilities, where most of the jobs that are submitted are allowed to use up to 8 nodes for example, I would use clusters and update the network infrastructure and CPU's as often as is possible. For large jobs I don't think we can do without nationally owned, big supercomputers. There simply is a group of problems that require these supercomputers, and where clusters can't be used. And these national science centers can of course maintain different kinds of supercomputers. If your problem requires low latency, use the supercomp, if it doesn't, use the cluster.
The authors propose to give scientists money to buy their own clusters, but I already saw calls for proposals (where you can apply for a research grant) where you could either reserve computing power on the national super computers, or get money to buy a cluster, or otherwise spend it on computer hardware. Of course the real question stated in the article is whether a country like America needs to have the fastest supercomputer. I guess that question is just a political one, as is the question whether that country would need the biggest storage facility in the world.
I also do not really understand the storage facility thing. Storage is not something I would expect you need temporarily. Only intermediate results are temporary, but the data in the big databases they mention seem there to stay. Once you've bought storage for one project, you can not allocate the storage to another project like in the case of supercomputing, where a project takes a month and then the power is handed to another project. If you have got such a project that needs lots of permanent storage space, why then not give THEM money to built such a storage system. Every university nowadays is on a fast line, and I don't see why that has to be central. Even storage divided among groups of researchers does not have to reside on a super data center. Just build systems for every requirement, with room to spare. Or am I missing something?
