Say I have a memory buffer with a vector of type std::decimal::decimal128 (IEEE754R) elements, can I wrap and expose that as a NumPy array, and do fast operations on those decimal vectors, like for example compute variance or auto-correlation over the vector? How would I do that best?

CodePudding user response：

Numpy does not support such a data type yet (at least on mainstream architectures). Only float16, float32, float64 and the non standard native extended double (generally with 80 bits) are supported. Put it shortly, only floating-point types natively supported by the target architecture. If the target machine support 128 bit double-precision numbers, then you could try the numpy.longdouble type but I do not expect this to be the case. In practice, x86 processors does not support that yet as well as ARM. IBM processors like POWER9 supports that natively but I am not sure they (fully) support the IEEE-754R standard. For more information please read this. Note that you could theoretically wrap binary data in Numpy types but you will not be able to do anything (really) useful with it. The Numpy code can theoretically be extended with new types but please note that Numpy is written in C and not C so adding the std::decimal::decimal128 in the source code will not be easy.

Note that if you really want to wrap such a type in Numpy array without having to change/rebuild the Numpy code, could wrap your type in a pure-Python class. However, be aware that the performance will be very bad since using pure-Python object prevent all the optimization done in Numpy (eg. SIMD vectorization, use of fast native code, specific algorithm optimized for a given type, etc.).