5 ms·
Chances aren't, actually. GPUs impose many constraints. I engineer embarrassingly parallel workloads for a living, using both CPUs and GPUs, and only a small
by pinko 15y ago
Chances aren't, actually. GPUs impose many constraints. I engineer embarrassingly parallel workloads for a living, using both CPUs and GPUs, and only a small minority of real codes can be usefully ported to GPUs today. Those that can usually see a ridiculous speedup, but they're the exception, not the rule.
In any case, I know these guys and they're not newbies. They're familiar with GPUs and what they can do. They're running the workload they need to be running.
- mrb 15y agoI seem to be right. The workload was molecular modeling (see blog post); and many of the algorithms are very well suited to GPU, see: http://www.ks.uiuc.edu/Research/gpu/ http://www.ks.uiuc.edu/Research/gpu/ I am not saying they are newbies for failing to exploit GPUs. There could be other reasons why they did that. For example the pharma needed the results ASAP and may not have ported that particular molecular modeling app to GPU yet. GPGPU is a nascent field after all. I do too write GPU applications (crypto bruteforcers), just so you know ;)
- mrb 15y agoAnd I was right. They replied to my comment, saying that workload "still need to be ported [to GPUs]". They just couldn't do it because it was a proprietary app. http://blog.cyclecomputing.com/2011/09/new-cyclecloud-cluster-is-a-triple-threat-30000-cores-massive-spot-instances-grill-chef-monitoring-g.html http://blog.cyclecomputing.com/2011/09/new-cyclecloud-cluste...