This is a very clear case of a well-defined application. The field of pharma needs a combination of mechanistic models (based on differential equations), statistics, and ability to work with large datasets. The existing tools are far from sufficient. If anything, the need for high performance and scalable tools has been felt in the industry for a very long time.
There are hundreds of thousands of Julia programmers worldwide now, and the user base is doubling every year. After all, new ideas do start with a tiny minority, but one that truly believes in those ideas and is enthusiastic in adopting them early.
Not necessarily, because the kernel cost can still be too high in many cases. It's not the machine learning case where everything in Python is a matrix multiplication call that will be costly enough to mask the cost of Python. In this cases, users will be writing models that are for scalar operations, and doing things like inlining those functions matters a lot. Of course, you could write something that takes Python code and generates Fortran code that it then statically compiles inline to your other Fortran codes to eliminate all of the overhead, and from that you can see exactly why we resorted to a JIT compiled language. FWIW, other software in this field have essentially built mini JIT compilers for Fortran for doing exactly this, and I'll just say that I'm glad I'm not the one who has to maintain that kind of thing while working on modeling and simulation algorithms.
This is a very clear case of a well-defined application. The field of pharma needs a combination of mechanistic models (based on differential equations), statistics, and ability to work with large datasets. The existing tools are far from sufficient. If anything, the need for high performance and scalable tools has been felt in the industry for a very long time.
There are hundreds of thousands of Julia programmers worldwide now, and the user base is doubling every year. After all, new ideas do start with a tiny minority, but one that truly believes in those ideas and is enthusiastic in adopting them early.