Scientific computing is chugging along about the same as it always has, quantum computers aren't really that relevant yet.
> I don't think the programming language makes that big of a difference, ultimately.
I can see how you might think this, but it's really ahistorical.
Scientific computing has always been a niche because of the range of skills needed. To have any real success at it as a team you needed to be a good enough at numerical analysis to understand the implementation, a good enough programmers to write something like production code (e.g. not your typical lab code) and good enough at the science to do the right project.
In the old days you were basically looking for one person who could do all of this, and in Fortran 77. You can carve off the last requirement if you only work on tools for other people, but that still leaves you with two domains.
Fortran 77 basically limited the scope of project that was reasonable. Things like matlab essentially started as wrappers on good libraries (in F77) so that people could get some work done without spending all their time fighting that complexity. This had a massive impact on productivity globally.
Introduction of things like c++ allowed more complex programs to be built for good or ill (also lead to improvements in fortran) but a lot of the same problems remain in terms of managing the complexity.
Later people added enough numerical libraries to python to get real work done, and that started to eclipse matlab at least in some specific domains (mainly because it's free and open).
Neither matlab or python are particularly good languages for scientific programming, but they are accessible - a gazillion grad students shoot themselves in the foot less in python than they would in fortran or c++, and iterate much faster.
In some ways systems like this have impact because they have reduced the necessary skill level across domains. There is always going to be room at the margins for a polymath but a lot of people who aren't can get things done much more easily now than a few decades ago. Now you may argue that nobody "does" scientific programming in python but it's a bit of a semantic flip, the core algorithms are all in c or something but depending on domain you may mostly be using python wrappers to access them.
Julia is an attempt (not the first one) to define a language that is both approachable and interactive (important) but also well designed for numerics etc. It's a very interesting project for that reason.
I've obviously skipped a lot of important stuff, but the impact of languages and particularly their accessibility has been really significant, especially when we get past scientific programming for it's own sake, and into real applications.
Scientific computing is chugging along about the same as it always has, quantum computers aren't really that relevant yet.
> I don't think the programming language makes that big of a difference, ultimately.
I can see how you might think this, but it's really ahistorical.
Scientific computing has always been a niche because of the range of skills needed. To have any real success at it as a team you needed to be a good enough at numerical analysis to understand the implementation, a good enough programmers to write something like production code (e.g. not your typical lab code) and good enough at the science to do the right project.
In the old days you were basically looking for one person who could do all of this, and in Fortran 77. You can carve off the last requirement if you only work on tools for other people, but that still leaves you with two domains.
Fortran 77 basically limited the scope of project that was reasonable. Things like matlab essentially started as wrappers on good libraries (in F77) so that people could get some work done without spending all their time fighting that complexity. This had a massive impact on productivity globally.
Introduction of things like c++ allowed more complex programs to be built for good or ill (also lead to improvements in fortran) but a lot of the same problems remain in terms of managing the complexity.
Later people added enough numerical libraries to python to get real work done, and that started to eclipse matlab at least in some specific domains (mainly because it's free and open).
Neither matlab or python are particularly good languages for scientific programming, but they are accessible - a gazillion grad students shoot themselves in the foot less in python than they would in fortran or c++, and iterate much faster.
In some ways systems like this have impact because they have reduced the necessary skill level across domains. There is always going to be room at the margins for a polymath but a lot of people who aren't can get things done much more easily now than a few decades ago. Now you may argue that nobody "does" scientific programming in python but it's a bit of a semantic flip, the core algorithms are all in c or something but depending on domain you may mostly be using python wrappers to access them.
Julia is an attempt (not the first one) to define a language that is both approachable and interactive (important) but also well designed for numerics etc. It's a very interesting project for that reason.
I've obviously skipped a lot of important stuff, but the impact of languages and particularly their accessibility has been really significant, especially when we get past scientific programming for it's own sake, and into real applications.