magicl

MAGICL [1] is another Lisp library for scientific computing, specifically linear algebra, under active development. [1] https://github.com/rigetti/magicl

I guess my number one piece of advice is to estimate time accordingly. Most things can be solved using pre-existing solutions with a bit of work, if you’re patient and you can afford to put in the time to do it. Secondary to that: - Learn to use FFI very well try hard to find libraries written in C. - Familiarize yourself with the structure of LAPACK and what it offers. - Learn to use a profiler and debugger (if using Lisp: SB-SPROF, TIME, SLIME, and SLDB). - (if using Lisp) Contribute useful things back to existing libraries, like MAGICL [0]. In my opinion, Lisp has no good libraries for plotting. I always have to plot by using another tool. SIMD/AVX are things you use directly in SBCL if you want to achieve very high FLOPS. Maybe it’s not the best analogy, but scientific programming in Lisp is currently like woodworking (compared to bui

I mostly just write my own stuff directly in CL like Runge-Kutta solvers, FFT, etc. Here's a library I trust for linear algebra that uses BLAS and LAPACK. I haven't tried it on Windows. https://github.com/rigetti/magicl And of course there's always Maxima, the clone of Macsyma which was the grandfather of Mathematica: https://en.wikipedia.org/wiki/Maxima_(software)

They’re complete in the sense that all standard functions are exposed. However, there’s only a modest “high-level” interface. The library: https://github.com/rigetti/magicl Comparison with NumPy: https://github.com/rigetti/magicl/blob/master/doc/high-level...

For your first point: Yes, good quality domain-specific libraries go very far in allowing one to be productive. Lisp does not offer a good, general-purpose, integrated numerical computing environment (plotting, scientific functions, etc.) and as such wont be good for belting out scientific code. If you like DIY, then Lisp is wonderful to write scientific code in because it can compile to extremely fast code. For FFTs in particular, Common Lisp has very good support for them [1, 2, 3]. For matrix arithmetic, there’s an up-and-coming library called MAGICL [4]. As for DSLs, there’s no problem using these on a team. Their utility does scale if the DSL is actually solving a problem. [1] NAPA-FFT3 https://github.com/pkhuong/Napa-FFT3 [2] BORDEAUX-FFT https://github.com/ahefner/bordeaux-fft [3] Patrick Stein’s FFT https://github.com/nklein/FFT [

Scanning their GitHub repositories shows that they are written for SBCL. [0] https://github.com/rigetti/rpcq [1] https://github.com/rigetti/magicl