Hello, About one year ago, a high-level, objected-oriented SIMD API was added to Mono. For example, there is a class Vector4f for vectors of 4 floats and this class implements methods such as basic operators, bitwise operators, comparison operators, min, max, sqrt, shuffle directly using SIMD operations. You can have a look at the following pages for further details: http://tirania.org/blog/archive/2008/Nov-03.html (blog post) http://go-mono.com/docs/index.aspx?tlink=0@N%3aMono.Simd (API reference) It seems to me that such an API would possibly be a great fit in Numpy too. It would also be possible to add classes that don't directly map to SIMD types. For example, Vector8f can easily be implemented in terms of 2 Vector4f. In addition to vectors, additional API may be added to support operations on matrices of fixed width or height. I search the archives for similar discussions but I only found a discussion about memory-alignment so I hope I am not restarting an existing discussion here. Memory-alignment is an import related issue since non-aligned movs can tank the performance. Any thoughts? I don't know the Numpy code base yet but I'm willing to help if such an effort is started. Thanks, Mathieu