Numerical Python for scalable architectures
Mads Ruben Burgdorff Kristensen, Brian Vinter · 2010
In this paper, we introduce DistNumPy, a library for doing numerical computation in Python that targets scalable distributed memory architectures. DistNumPy extends the NumPy module[15], which is popular for scientific programming. Replacing NumPy with Dist-NumPy enables the user to write sequential Python programs that seamlessly utilize distributed memory architectures. This feature is obtained by introducing a new backend for NumPy arrays, which distribute data amongst the nodes in a distributed memory multi-processor. All operations on this new array will seek to utilize all available processors. The array itself is distributed between multiple processors in order to support larger arrays than a single node can hold in memory.