2021/06/22 by Claude Tadonki, Tadonki, Claude · 1 citation
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Business #Computer science #Distributed #Exascale computing #FOS: Computer and information sciences #Optimization and Packing Problems #Parallel #Parallel computing #Supercomputer #Vehicle Routing Optimization Methods #and Cluster Computing (cs.DC) #cs.DC
paper · pdf · doi:10.48550/arxiv.2106.11819
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/06/22 · openalex publication_date 2021/06/22 · arxiv updated 2021/06/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
quest for processing speed potential. In fact, we always get a fraction of the technically available computing power (so-called \em theoretical peak), and the gap is likely to go hand-to-hand with the hardware complexity of the target system. Among the key aspects of this complexity, we have: the \em heterogeneity of the computing units, the \em memory hierarchy and partitioning including the non-uniform memory access (NUMA) configuration, and the \em interconnect for data exchanges among the computing nodes. Scientific investigations and cutting-edge technical activities should ideally scale-up with respect to sustained performance. The special case of quantitative approaches for solving (large-scale) problems deserves a special focus. Indeed, most of common real-life problems, even when considering the artificial intelligence paradigm, rely on optimization techniques for the main kernels of algorithmic solutions. Mathematical programming and pure combinatorial methods are not easy to implement efficiently on large-scale supercomputers because of \em irregular control flow, \em complex memory access patterns, \em heterogeneous kernels, \em numerical issues, to name a few. We describe and examine our thoughts from the standpoint of large-scale supercomputers.