I just finished up some animal artwork for my iPhone Spelling Toy. I also added translations for Japanese and German (in addition to the English, French, and Spanish that were already there). After I double-check the translations, I will get it uploaded to the App Store.
If you have any expertise in Spanish, French, German, or Japanese, I’d appreciate any feedback you can give me about the words I chose in those languages. Would they be the word one would think of when shown the picture? Thanks, in advance.
I just uploaded the first update to my iPhone Spelling Toy. The update includes two minor changes:
- Corrected spelling of
siete
(Spanish for seven
)
- Corrected spelling of
colores
(Spanish for colors
)
Today, I am working on adding animal drawings.
I released version 1.3 of my Common Lisp Fourier Transform library today. It is significantly faster than yesterday’s version. On my MacBook Pro in SBCL 1.0.30 with a 512 by 512 by 16 array, version 1.3 takes 3.74 seconds where version 1.2 took 9.77 seconds and version 1.0 took 25.31 seconds. For a breakdown of performance on various array sizes with various Lisp implementations, see the performance section of the library page.
Most of the speed improvement in this version came from memory improvements. Version 1.3 doesn’t cons at all in SBCL during the processing of each row. Version 1.2 inadvertently consed three rows worth of complex numbers for every row transformed.
My library is still about 25% slower than Bordeaux FFT for one-dimensional arrays. My library, however, has full support for multi-dimensional arrays where Bordeaux-FFT does not.
I also included some validation test cases using NST to this release. To try them, go to the library directory, hop in your Lisp, and load regression.lisp
.
I released version 1.2 of my Common Lisp Fourier Transform library today. It is significantly faster than the old version. On my MacBook Pro in SBCL 1.0.30 with a 512 by 512 by 16 array, version 1.2 takes 9.77 seconds where version 1.0 took 25.31 seconds. Version 1.2 consed 2.4M while version 1.0 consed 7.4M. Neither version should have to cons nearly that much, so there’s still work to be done.
This release is significantly faster under Clozure64 1.3-r11936, too. For a 512 by 512 array, version 1.2 takes 2.72 seconds (single-threaded) and 2.07 seconds (multi-threaded) where version 1.0 took 4.45 seconds (single-threaded) and 3.06 seconds (multi-threaded).
My library is still about 1/2 to 1/3 the speed of Bordeaux FFT and still allocates memory in situations that Bordeaux does not. Hopefully, I can close those gaps most of the way soon.
This version simply contains some tweaks to deal with more modern C++ compilers.
