Most high performance computing was done with Fortran back in the day (and still used today) because working with arrays and matrices is done at a higher level than C/C++ or Assembly, but it is still a fast language and has easy access to BLAS/LAPACK. When I think of scientific computing, I think of Fortran, Matlab, Mathematica, C, C++, Python + Numpy, and recently Julia. APL could've been great here if the vendors had included low level code to do all the numeric work and used APL as the glue language, but it didn't happen that way and became popular in the finance world instead. It's a shame.
Comments
Most high performance computing was done with Fortran back in the day (and still used today) because working with arrays and matrices is done at a higher level than C/C++ or Assembly, but it is still a fast language and has easy access to BLAS/LAPACK. When I think of scientific computing, I think of Fortran, Matlab, Mathematica, C, C++, Python + Numpy, and recently Julia. APL could've been great here if the vendors had included low level code to do all the numeric work and used APL as the glue language, but it didn't happen that way and became popular in the finance world instead. It's a shame.