  The Multilevel Fast Multipole Algorithm (MLFMA) has known applications in
scientific modeling in the fields of telecommunications, physics, mechanics,
and chemistry. Accelerating calculation of far-field using GPUs and GPU
clusters for large-scale problems has been studied for more than a decade. The
acceleration of the Near Field Computation (P2P operator) however was less of a
concern because it does not face the challenges of distributed processing which
does far field. This article proposes a modification of the P2P algorithm and
uses performance models to determine its optimality criteria. By modeling the
speedup, we found that making threads independence by creating redundancy in
the data makes the algorithm for lower dense (higher frequency) problems nearly
13 times faster than non-redundant mode.
