* fix pairs, mpairs, mitems for cstring for JS backend
* add mutation check for pairs and mpairs for strings and seqs
* change the assertion message - modifying (changing elements) is not
a problem, changing the length is
* introduce temporary <//> for 'owned' to get this compile with 0.19
* make newTable[string, owned Node]() compile (but it crashes)
* make sink/owned parameters consistent
* make actiontable test compile again
* VM: support sytem.move; makes tests green
* tables: initialized by default
* sets: initialized by default
* DRY: extract shared functionality
* add a changelog entry
* fix errors
* don't test include files
* make it work for sharedtables
* fix discovered bugs
* add exhaustive tests
* make parsutils.parseBin generic and improve runnableExamples
* reimplement parseBin/Oct/Hex and improve runnableExamples
* update changelog.md file with parseBin/Oct/Hex and fix a typo
* use existing sort for CountTable, and
add SortOrder options to CountTable, OrderedTable sort(s)
* add some tests, runnables, etc.
* fix runnable imports
This plugin provides essential building block for implementing incremental computations in your programs. The idea behind incremental computations is that if you do the same calculation multiple times but with slightly different inputs you don't have to recompute everything from scratch. Also you don't want to adopt special algorithms either, you would like to write your code in standard from scratch manner and get incrementality for free when it is possible.
The plugin computes the digest of the proc bodies, recursively hashing all called procs as well . Such digest with the digest of the argument values gives a good "name" for the result. Terminology loosely follows paper "Incremental Computation with Names" link below. It works well if you have no side effects in your computations. If you have global state in your computations then you will need problem specific workarounds to represent global state in set of "names" . SideEffect tracking in Nim also useful in this topic.
Classical examples:
Dashboard with ticking data. New data arrives non stop and you would like to update the dashboard recomputing only changed outputs.
Excel spreadsheet where user changes one cell and you would like to recompute all cells that are affected by the change, but do not want to recompute every cell in the spreadsheet.
