[jerven]

And still is ;) Partly because some key work loads just did not run well on GPU's due to lack of addressable memory. Lots of amdahls getting in the way. Some of the key use cases required stupendous large memory machines (genome assembly using only short reads).

Then a lot of code is very branchy but massively parallel leading to clusters of pure CPUs to be more flexible, which is important in research settings, and with higher utilization than mixed CPU/GPU clusters.

GPU code takes longer to get to market and has more specialized skills required then standard CPU orientated programming. Late to market means you miss a whole wave of experimental methods from the lab. i.e. GPU short read aligners came when long reads started to come out of the sequencing lab. Leading to people to stop doing short reads or at least stop doing pure short reads.

Secondly quite a bunch of the key staff at the large research institutes had been burned by previous hardware acceleration attempts and where not going to throw money at it until market proven.

Bio-informatics tends to cutting edge (the hemorrhaging kind) on the bio/lab tech side yet the production IT tends to balance that to doing the things we know as we already have enough risks. i.e. focus on the algorithms and robustness not on pure power.