per loop in an appropriate way.

As the computation for each point and loop follows the same algorithm, this approach fits the (single instruction, multiple data) processing approach very well. Powerful massively parallel hardware is now available at a highly competitive price in the form of specialized Graphics Processing Units (s), where development has – to a large extent – been driven by the video game industry.

When implementing Worldline Numerics on hardware, one has to bear in mind certain constraints of programming. The memory hierarchy of most s discerns between global memory and a special form of fast local memory called “shared memory”. It is often favourable to perform most of the computations in shared memory. This fast shared memory typically is quite small and has to be divided between simultaneously running work items, thus limiting the number of loop points that can be computed concurrently. One also has to keep in mind that s perform rather poorly on complex branching patterns and the hardware optimizations on s mainly target a high throughput of floating point operations, often neglecting the performance on integer calculations needed for most Pseudo Random Number Generators (s).

On account of shared memory size limitations, 