For each algorithm and program, there are architectures that are better than others. Some computation may need a lot of FLOPS, but FLOPS are not the only thing to consider. Communication and memory bandwidth and latency are as important as computational power, specially since memory speed and CPU speed are decoupled.
It has been a while since my last post on manifold learning, and I still have some things to speak about (unfortunately, it will be the end post of the dimensionality reduction series on my blog, as my current job is not about this anymore). After the multidimensional regression, it is possible to use it to project new samples on the modelized manifold, and to classify data.