Software is really becoming the broader language of science. Even broader than mathematics, but we don’t really know how to fund it.
Those are words from Ed Seidel, Director of the NSF‘s Office of Cyberinfrastructure. They are included in an article at Inside HPC, where Ed tries to clear up the air on some controversial positions on the status of supercomputing software.
Whether you agree with Ed Seidel on whether Fortran is antiquated or not, it’s pretty clear, and not just in HPC that science does not know how to fund sustainable software development, and if we do want to get a generation of programmers into scientific computing, we have to embrace more “modern” programming languages, or develop our own, because whether you like it or not, getting good Fortran programmers is going to get harder and harder. Some of his statements are very encouraging. He adds that reproducibility, bringing software engineering disciplines out of business applications and into scientific software are key issues. He goes on to say that we need to do more work on abstraction layers, documentation and teaching researchers to contribute to existing codes.
Will the NSF take the lead in making all of that happen? I sure hope so, but we need to do more as a community as well and teach those who are coming into scientific computing about these principles.
HPC and software … again
Those are words from Ed Seidel, Director of the NSF‘s Office of Cyberinfrastructure. They are included in an article at Inside HPC, where Ed tries to clear up the air on some controversial positions on the status of supercomputing software.
Whether you agree with Ed Seidel on whether Fortran is antiquated or not, it’s pretty clear, and not just in HPC that science does not know how to fund sustainable software development, and if we do want to get a generation of programmers into scientific computing, we have to embrace more “modern” programming languages, or develop our own, because whether you like it or not, getting good Fortran programmers is going to get harder and harder. Some of his statements are very encouraging. He adds that reproducibility, bringing software engineering disciplines out of business applications and into scientific software are key issues. He goes on to say that we need to do more work on abstraction layers, documentation and teaching researchers to contribute to existing codes.
Will the NSF take the lead in making all of that happen? I sure hope so, but we need to do more as a community as well and teach those who are coming into scientific computing about these principles.
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