Honestly, a combination of time, money, and expertise. Getting a good, low level factorization that works well in parallel is hard. I can write them. Others

Honestly, a combination of time, money, and expertise. Getting a good, low level factorization that works well in parallel is hard. I can write them. Others write them better. However, outside of that, I don't have any financial support to write them and there's no way that I can convince my clients to fund that effort. Personally, I don't really care where the libraries come from, but I will contend that the hardware manufacturers are generally the best place for this work to occur. For many years, each of the chip makers published their own high performance BLAS and LAPACK routines, which worked really well. Intel had MKL, AMD had AMCL, Sun had sunperf, IBM had ESSL. NVIDIA does the same, and I'm hugely grateful for that, but it's not complete. Really, though, the comment is more to answer why more mathematicians don't use GPUs. My contention is not that I or my colleagues view it as a fad, but more that there's a lack of routines that we depend on. I currently work for myself, but I'll also mention that the manufacturers did go to management at places I've worked and management did put pressure on staff to just rewrite everything in CUDA. As staff does, they said, ok, fine, but it's going to cost you labor. Management didn't have the money, or didn't want to spend it, and some mild office conflict occurred. Anyway, mostly that's to say that I will gladly spend thousands on GPUs and recommend it to my clients as soon as I don't have to write all of the low level routines myself.