Contact: perimosocordiae at gmail
To get the best performance you typically want to know something about the sparsity structure of your problem. Do most nonzeros fall near the diagonal? Or do they clump into dense submatrices? If you don't have that…
Same here. I've been searching for software that can simulate that accidental audio system monitor ever since, but so far I haven't found anything. Maybe I'll just have to write it myself...
If you're wondering why, the one town that managed to get fiber installed (Leverett) is relatively densely populated with UMass Amherst faculty.
For IPython-based profiling, I find the %timeit and %memit magic commands invaluable. This post has a good overview: http://pynash.org/2013/03/06/timing-and-profiling.html
I contribute to the Doppio JVM project (https://github.com/int3/doppio), which we initially wrote in Coffeescript. About 9 months ago, we decided to port the whole thing (~10kloc) to Typescript, and it proved really…
I kicked around with some JS manifold learning stuff[1] a while back for essentially the same purpose: practice in writing things from scratch, while making it easier for other people to play with. [1]:…
If it's possible to integrate this with BrowserFS, you'd be all set: https://github.com/jvilk/BrowserFS/#browserfs
Shameless plug: this seems like a good application for Doppio: https://github.com/int3/doppio The whole JVM lives in the browser, so you don't need to rely on hacks to "sanitize" user programs.
To get the best performance you typically want to know something about the sparsity structure of your problem. Do most nonzeros fall near the diagonal? Or do they clump into dense submatrices? If you don't have that…
Same here. I've been searching for software that can simulate that accidental audio system monitor ever since, but so far I haven't found anything. Maybe I'll just have to write it myself...
If you're wondering why, the one town that managed to get fiber installed (Leverett) is relatively densely populated with UMass Amherst faculty.
For IPython-based profiling, I find the %timeit and %memit magic commands invaluable. This post has a good overview: http://pynash.org/2013/03/06/timing-and-profiling.html
I contribute to the Doppio JVM project (https://github.com/int3/doppio), which we initially wrote in Coffeescript. About 9 months ago, we decided to port the whole thing (~10kloc) to Typescript, and it proved really…
I kicked around with some JS manifold learning stuff[1] a while back for essentially the same purpose: practice in writing things from scratch, while making it easier for other people to play with. [1]:…
If it's possible to integrate this with BrowserFS, you'd be all set: https://github.com/jvilk/BrowserFS/#browserfs
Shameless plug: this seems like a good application for Doppio: https://github.com/int3/doppio The whole JVM lives in the browser, so you don't need to rely on hacks to "sanitize" user programs.