Rapid prototyping of numerically expressed problems is essential for a broad range of research areas. Finding the solution for computational scientific and engineering problems often requires experimenting with various algorithms and different parameters using the feedback from several iterations. Therefore, being able to quickly prototype the solution is critical for a timely and successful scientific discovery. In this book, I have explored the possibility of seamlessly executing sequential scientific applications in parallel. The idea is to introduce implicit data parallelism in order to provide a high-productivity and high-performance framework. I introduce two new projects, DistNumPy and Bohrium, that strive to provide a high-performance back-end for Numerical Python (NumPy) without reducing the high-productivity of Python/NumPy. I present several performance studies that demonstrate good scalable performance on a variety of architectures: from a small Ethernet Linux cluster with 32 CPU-cores to the Cray XE-6 supercomputer Hopper with 1536 CPU-cores.