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High performance computing (HPC) systems underwent a significant increase intheir processing capabilities. Modern HPC systems combine large numbers ofhomogeneous and heterogeneous computing resources. Scalability is, therefore,an essential aspect of scientific applications to efficiently exploit themassive parallelism of modern HPC systems. This work introduces an efficientversion of the parallel spin-image algorithm (PSIA), called EPSIA. The PSIA isa parallel version of the spin-image algorithm (SIA). The (P)SIA is used invarious domains, such as 3D object recognition, categorization, and 3D facerecognition. EPSIA refers to the extended version of the PSIA that integratesvarious well-known dynamic loop scheduling (DLS) techniques. The present work:(1) Proposes EPSIA, a novel flexible version of PSIA; (2) Showcases thebenefits of applying DLS techniques for optimizing the performance of the PSIA;(3) Assesses the performance of the proposed EPSIA by conducting severalscalability experiments. The performance results are promising and show thatusing well-known DLS techniques, the performance of the EPSIA outperforms theperformance of the PSIA by a factor of 1.2 and 2 for homogeneous andheterogeneous computing resources, respectively.

Efficient Generation of Parallel Spin-images Using Dynamic Loop Scheduling