Performance evaluation of an integrated RFI database for the MeerKAT/SKA radio telescope

For radio telescopes, radio frequency interference from terrestrial and other sources is a recognized problem that contaminates the signal (RFI) and must be tracked and ultimately removed. At the MeerKAT/SKA telescope, RFI is recorded with a variety of devices, including telescopes, sensors, and scanners; but the combination of data from these multiple sources to yield a unified view of RFI remains a challenging problem. Previously, we demonstrated that a scalable database model with an implementation based on the Polystore framework is a potential solution for RFI monitoring. Here we extend this work, implementing the database model in an integrated environment and evaluating its performance across a range of workloads with three data stores: SciDB, PSQL, and Accumulo. We find that SciDB and Accumulo scale better than PSQL under multi-user environments. Results show a minimal latency as low as 0.02 seconds, irrespective of the location, and data store type. Further, integrated APIs provide single notation and are 5% faster than third-party APIs. Our findings thus provide a guide to the proposed integrated RFI system at MeerKAT/SKA radio telescope

Benchmarking Data Analysis and Machine Learning Applications on the Intel KNL Many-Core Processor

Knights Landing (KNL) is the code name for the second-generation Intel Xeon Phi product family. KNL has generated significant interest in the data analysis and machine learning communities because its new many-core architecture targets both of these workloads. The KNL many-core vector processor design enables it to exploit much higher levels of parallelism. At the Lincoln Laboratory Supercomputing Center (LLSC), the majority of users are running data analysis applications such as MATLAB and Octave. More recently, machine learning applications, such as the UC Berkeley Caffe deep learning framework, have become increasingly important to LLSC users. Thus, the performance of these applications on KNL systems is of high interest to LLSC users and the broader data analysis and machine learning communities. Our data analysis benchmarks of these application on the Intel KNL processor indicate that single-core double-precision generalized matrix multiply (DGEMM) performance on KNL systems has improved by ~3.5x compared to prior Intel Xeon technologies. Our data analysis applications also achieved ~60% of the theoretical peak performance. Also a performance comparison of a machine learning application, Caffe, between the two different Intel CPUs, Xeon E5 v3 and Xeon Phi 7210, demonstrated a 2.7x improvement on a KNL node.Comment: 6 pages; 9 figures; accepted to IEEE HPEC 201