charred VS neanderthal

After building tech.ml.dataset and charred I wanted to take the lessons learned there and apply them back into the base Clojure substrate of persistent maps, persistent vectors, and algorithmic primitives like group-by and frequencies.

...and in some circumstances, the most correct Clojure program has a little Java in it because it's faster or whatever. (Chris Nuernberger's charred is a recent example.) This is unusual Clojuring; most Clojurians never do this.

Introducing Charred - fast json/csv encode and decode. This library finalizes my research into csv and json parsing and is a complete drop-in replacement for clojure.data.csv and clojure.data.json. Same API, much better (5-10x) performance. This library gets as good performance for those tasks as anything on the JVM and avoids the jackson hairball entirely.

There is the official Hello World https://github.com/uncomplicate/neanderthal/tree/master/examples/hello-world

Did you have any occasion to evaluate neanderthal during your research? People seem to prefer it over core.matrix because it focus on primitive speed and sticking to BLAS idioms (as well as offering a decent api for working with GPU backends via cuda and opencl). I am curious to see if you did and found anything lacking there. I have a project on the backburner to try and target neanderthal for local search stuff, expressing problems in a high-level API that can then be baked into some numerically-friendly representation for efficient execution. It's often easier (trivial) to express solution representations, neighborhood functions, and objectives/constraints in a general purpose language, of which none of the things we like (sparse data structures, dynamically allocated stuff) are amenable to the contiguous memory, primitive numeric model that the hardware wants.

Do clojure as a side gig or in free time. Let day job pay the bills. If you can, maybe incorporate clojure into work job to solve small problems (https://github.com/clj-python/libpython-clj and https://github.com/scicloj/clojisr provide bridges to/from python and r). There is a lot of effort going into the data science side as well; the scicloj effort has resulted in a lot of growth over the last 2 years. tech.ml.dataset, tech.ml (now scicloj.ml). Dragan has a bunch of excellent stuff in neanderthal and deep diamond. There are also bindings to other jvm libraries from multiple languages.