I'm a systems programmer, with experience writing high performance code for mathy stuff (signal processing), network stuff, filesystem stuff, and most recently, I'm learning-by-doing "database stuff". I was joking at work the other day "if I can get compilers under my belt I'll have caught them all".
I see this applied to work (database) in a way I'll not elaborate until I can do it. :)
But in other areas, I've done a lot of signal processing, numerical algorithm development and optimization, lots of MATLAB (for improving the underlying math, but also using approximation theory for performance. Think: "replace exp(x) with a taylor series" except .... more. :)) and C/C++ (for turning those graphs of lists of equations) into machine code.
I've written dataflow style code starting from "paper of equations", to "runs on N nodes" many times.
Perhaps then you see how this looks (to me) like a "missing piece". :) I can now mix between mathematical optimizations (like replacing an expensive function with a pade approximant, using a lattice of symbolic identities, etc) and _compiler_ optimizations. \o/
I see this applied to work (database) in a way I'll not elaborate until I can do it. :)
But in other areas, I've done a lot of signal processing, numerical algorithm development and optimization, lots of MATLAB (for improving the underlying math, but also using approximation theory for performance. Think: "replace exp(x) with a taylor series" except .... more. :)) and C/C++ (for turning those graphs of lists of equations) into machine code.
I've written dataflow style code starting from "paper of equations", to "runs on N nodes" many times.
Perhaps then you see how this looks (to me) like a "missing piece". :) I can now mix between mathematical optimizations (like replacing an expensive function with a pade approximant, using a lattice of symbolic identities, etc) and _compiler_ optimizations. \o/