Large-Scale Response-Aware Online ANN Search in Dynamic Datasets

Abstract

Similarity search is an key operation in Content-based multimedia retrieval(CBMR) applications. Online CBMR applications, which is the focus of thiswork, have to search in large and dynamic datasets that are updated during theexecution while offering low response times. Additionally, these applications aresubmitted to workloads that vary at runtime. The computing demands in thisscenario exceeds the processing power of a single computer, motivating the large-scale machines in the domain. Thus, in this work, we proposed a distributedmemory parallelization of similarity search that addresses the mentioned chal-lenges. Our solution employs the efficient Inverted File System with AsymmetricDistance Computation algorithm (IVFADC) algorithm as the baseline, which isextended here to support dynamic datasets. Further, we developed a dynamicresource management algorithm, called Multi-Stream Adaptation (MS-ADAPT),that is executed at run-time to change the computing resource assignment withthe goal of minimizing response times. We have evaluated our system solutionwith multiple data partitioning strategies using up to 160 compute nodes anda dataset with 344 billion multimedia descriptors. It demonstrated superlinearscalability for our Spatial-Aware data partition algorithms and MS-ADAPT out-performed the best static approach (oracle) by reducing the response times upto 32× on high-load cases.