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MathCompile is a package that translates Wolfram Language functions into C++ code and generate dynamic libraries that can be called in Wolfram Language.
The primary usage of MathCompile is to speed up the execution of Wolfram Language code, similar to the built-in compiler. Compared to the built-in compiler, MathCompile supports functional programming much better, and it implements all supported functions so that the compiled functions do not depend on the Wolfram run-time library to run.
Here are some scenarios where you should consider using MathCompile:
- This part of the code is a performance bottleneck;
- The code does numerical calculation;
- The code uses loop, such as
Table
,Do
, orNestList
; - The code does numerous operations on small lists.
And here are some scenarios where you cannot/do not need to use MathCompile:
- The code does symbolic computation;
- The code uses functions that are not compilable, e.g.
NIntegrate
; - The performance is limited by the functions highly optimized already, e.g. matrix multiplication;
- Compilation does not provide a speed-up according to tests.
See the Guide.
See Performance Tests on some small-scale test cases. The comparisons between MathCompile and the built-in compiler can also be found.
MathCompile is still in a very early stage, where a lot of functionalities are to be implemented. Here is a incomplete list of what will possibly be added.
-
More functions (for completeness)
Partition
,Outer
,ReplacePart
, and a few more. -
Detect Abort[]
-
Import and export functions
Import and export of binary data and tables of numbers can be built into the compiler without too much effort. -
Parallelization
Parallelization can greatly improve the performance of listable functions andDot
, whileParallelTable
andParallelMap
, etc. are harder to implement correctly. -
Interacting with the kernel
The built-in compiler (withCompilationTarget->"C"
) is able to call a not-compilable function, which is useful when a necessary function happens to be missing. -
More data structures?
Supporting sparse array might not be worthwhile, butAssociation
(asstd::map
) has the potential of performance improvement.