| I almost spew my coffee when reading your grand parent comments. One of the first teams that ported LAPACK to CUDA or CULA are apparently being paid handsomely by Nividia [1],[2]. Interestingly, DCompute is a little known effort to support compute on CUDA and OpenCL in D language, and it was done by a part-time undergrad student [3]. I strongly believe we need a very capable language to make advancement much easier in HPC/AI/etc, and D language fit the bill very much and then some. Heck it even beat other BLAS libraries that other so called data languages namely Matlab and Julia still heavily depended on for their performances to this very day. It does it in style back in 2016 more than seven years ago [4]. The DCompute implementation by the part-timer in 2017 actually depended on this native D implementation of these linear algebra routines in Mir [5]. [1] CULA: hybrid GPU accelerated linear algebra routines: https://www.spiedigitallibrary.org/conference-proceedings-of... [2] CUDA Spotlight: John Humphrey: https://www.nvidia.com/content/cuda/spotlights/john-humphrey... [3] DCompute: GPGPU with Native D for OpenCL and CUDA: https://dlang.org/blog/2017/07/17/dcompute-gpgpu-with-native... [4] Numeric age for D: Mir GLAS is faster than OpenBLAS and Eigen: http://blog.mir.dlang.io/glas/benchmark/openblas/2016/09/23/... [5] DCompute: Native execution of D on GPUs and other Accelerators: https://github.com/libmir/dcompute |
But I'm one of those old-school HPC guys who believes that libraries are mostly irrelevant, and absolutely no substitute for compilers and targeted code generation.
Julia is cool, btw. It could very well end up supplanting Fortran, once they fix the poor performance code generation issues.