On Tue, Jul 16, 2019 at 7:18 PM Slonik Az <slonik...@gmail.com> wrote:
> REPL in a static AOT compiled language is hard, yet Swift somehow managed > to implement it. > > I must disagree. The technique is somewhat well known and has a long history. See e.g., various common lisp, and standard ml implementations. If you are willing to accept a hybrid of a byte-code interpreter with a native code compiler at your disposal, then ocaml and haskell will suffice in addition. When a function is defined in the REPL you just call the compiler, and it emits assembly language. You then mark this region as executable in the memory, and you just jump to this when the function is invoked. In some cases, a dispatch table is used so a function can be replaced post-facto. It has fallen somewhat out of favor for the hybrid approaches. Probably because modern computers are fast enough when you are exploring. In my experience, most data science is about processing of data, so it is suitable for doing science. Exploratory tools are good for understanding the model you are working in. However, real world data processing can require you to work on several terabytes of data (or more!). There is a threshold where it starts becoming beneficial to optimize the processing pipeline, especially the pre-processing parts. And lower level languages, such as Go, tend to fare really well here. These lower level tools can then be hooked into e.g., R and Python, empowering the exploratory part of the system. Another important point is that modern computational kernels, for instance TensorFlow, are really compilers from a data-flow graph representation to highly optimized numerical routines. Some of which executes on specialized numerical hardware (8-32bit floating point SIMD hardware). You can define such a graph in Python, but then export it and use it in other systems and pipelines. As such Python, your exploratory vehicle, provides a plug-in for a more low-level processing pipeline. This also allows part of the graph to run inside a mobile client. The plug in model is also followed by parallel array processing language, see e.g., Futhark (https://futhark-lang.org/). You embed your kernel in another system. If you read Michael Jones post, there are important similarities. -- J. -- You received this message because you are subscribed to the Google Groups "golang-nuts" group. To unsubscribe from this group and stop receiving emails from it, send an email to golang-nuts+unsubscr...@googlegroups.com. To view this discussion on the web visit https://groups.google.com/d/msgid/golang-nuts/CAGrdgiWu0j-PTiRmKrs6huVRuxVu%3DN%2BJe%3DBiKYXJ%3DOi9yQHvDg%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
