  Modern processor architectures, in addition to having still more cores, also
require still more consideration to memory-layout in order to run at full
capacity. The usefulness of most languages is deprecating as their
abstractions, structures or objects are hard to map onto modern processor
architectures efficiently.
  The work in this paper introduces a new abstract machine framework, cphVB,
that enables vector oriented high-level programming languages to map onto a
broad range of architectures efficiently. The idea is to close the gap between
high-level languages and hardware optimized low-level implementations. By
translating high-level vector operations into an intermediate vector bytecode,
cphVB enables specialized vector engines to efficiently execute the vector
operations.
  The primary success parameters are to maintain a complete abstraction from
low-level details and to provide efficient code execution across different,
modern, processors. We evaluate the presented design through a setup that
targets multi-core CPU architectures. We evaluate the performance of the
implementation using Python implementations of well-known algorithms: a jacobi
solver, a kNN search, a shallow water simulation and a synthetic stencil
simulation. All demonstrate good performance.
