65 lines
2.3 KiB
Markdown
65 lines
2.3 KiB
Markdown
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# Nga in Python
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Nga in Python has a number of important differences from the standard
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reference implementation in C.
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## Cell Size
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Typically cells are either 32-bit or 64-bit signed integers. Python
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supports infinite precision, so this implementation does not restrict
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the cell size.
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It should be noted that Retro provides two words (`n:MIN` and `n:MAX`)
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for determining the limits. This is used by part of the floating point
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device, and may be checked by user code. The Python implementation
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reports these as if the cell size is 128 bits. In the standard system,
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this will only affect the use of floating point values when encoded to
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normal cells.
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The use of unlimited precision will be an issue if you try to save
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images to run on other VM instances. At this point I'm not implementing
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saving of images under the Python implementation, so this isn't a
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problem currently, but if you add this, make sure to truncate the
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cells to 32 or 64 bits.
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## Performance
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The Python implementation is slower than the C one. To help compensate
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for this, it makes use of a number of techniques.
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The VM detects when certain words are being called and will instead
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execute native Python functions.
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Of note, this currently traps and replaces:
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- s:length
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- s:eq?
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- s:to-number
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- d:lookup
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String operations are slow, so providing faster length, equality,
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and conversion to numbers provides a very noticable performance
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improvement.
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Dictionary lookups are also slow. In Retro, each token has two
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lookups associated with it. First, Retro checks to see if the first
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character is a `prefix:` word. If this is not the case, then the
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dictionary is searched for the entire token.
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The Python implementation now caches the entire dictionary as a
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native dict(), and searches this instead. As with string operations,
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this provides a substantial improvement.
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To keep up to date, the VM also notices when `d:add-header` is
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called and adds an entry to the dict before running the Retro word to
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actually create the header.
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It will also add entries to the dict if the overridden `d:lookup`
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fails to find an entry in the cached set.
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If you want to override an additional word:
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- in `cached_words()`, add an entry to map in the word
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- in `execute()` add an `elif` handler for the word
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- make sure the `elif` handler ends in `self.ip = self.address.pop()'
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