SIA: A Scalable Interoperable Annotation Server for Biomedical Named Entities
Table 1: Scalability experiment results. Processing times with varying degree of parallelism, analyzing 207.551 PubMed articles with all internal annotators (SETH, mirNer, Linnaues, Banner, DiseaseNer) and ChemSpot using a single instance of SIA.
['Parallelism', 'Processing time', 'Throughput', 'Improvement']
The experiment was run on a Server with 2 Intel Xeon E5-2630 processor (8 threads, 16 cores each) and 256 GB RAM running Ubuntu 12.04. To simulate the scaling behavior, we varied the degree of parallelism used by SIA from 1 to 5 respectively and measured the overall time to annotate all documents. The parallelism controls the number of messages consumed from the input queue simultanously. When increasing the parallelism we see a decrease of processing times with a speedup of up to 3× compared to single threaded execution. Increasing the parallelism further did not yield lower processing times, as the processing is mainly CPU bound, with a ceiling hit with 5 parallel threads. This highlights that SIA is fully capable of exploiting all available CPU resources, achieving a throughput of more than 70 documents per second. Using the parallelism within SIA furthermore enables to effortlessly provide parallel processing for exiting annotators that are otherwise hard to scale.