The Apertif Monitor for Bursts Encountered in Real-time (AMBER) auto-tuning optimization with genetic algorithms. (arXiv:1811.04165v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Mikhailov_K/0/1/0/all/0/1">Klim Mikhailov</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Sclocco_A/0/1/0/all/0/1">Alessio Sclocco</a>
Real-time searches for faint radio pulses from unknown radio transients are
computationally challenging. Detections become further complicated due to
continuously increasing technical capabilities of transient surveys: telescope
sensitivity, searched area of the sky, number of antennas or dishes, temporal
and frequency resolution. The new Apertif transient survey on the Westerbork
telescope happens in real-time on GPUs by means of the single-pulse search
pipeline AMBER (Sclocco, 2017). AMBER initially carries out auto tuning: it
finds the most optimal configuration of user-controlled parameters per each of
four pipeline kernels so that each kernel performs its task as fast as
possible. The pipeline uses a brute-force (BF) exhaustive search which in total
takes 5 – 24 hours to run depending on the processing cluster architecture. We
apply more heuristic, biologically driven genetic algorithms (GAs) to limit the
exploration of the total parameter space, tune all four kernels together and
reduce the tuning time to few hours. Our results show that after only few hours
of tuning, GAs always find similar or even better configurations for all
kernels together than the combination of single kernel configurations tuned by
the BF approach. At the same time, by means of their genetic operators, GAs
converge into better solutions than those obtained by pure random searches. The
explored multi-dimensional parameter space is very complex and has multiple
local optima as the evolution of randomly generated configurations does not
always guarantee global solution.
Real-time searches for faint radio pulses from unknown radio transients are
computationally challenging. Detections become further complicated due to
continuously increasing technical capabilities of transient surveys: telescope
sensitivity, searched area of the sky, number of antennas or dishes, temporal
and frequency resolution. The new Apertif transient survey on the Westerbork
telescope happens in real-time on GPUs by means of the single-pulse search
pipeline AMBER (Sclocco, 2017). AMBER initially carries out auto tuning: it
finds the most optimal configuration of user-controlled parameters per each of
four pipeline kernels so that each kernel performs its task as fast as
possible. The pipeline uses a brute-force (BF) exhaustive search which in total
takes 5 – 24 hours to run depending on the processing cluster architecture. We
apply more heuristic, biologically driven genetic algorithms (GAs) to limit the
exploration of the total parameter space, tune all four kernels together and
reduce the tuning time to few hours. Our results show that after only few hours
of tuning, GAs always find similar or even better configurations for all
kernels together than the combination of single kernel configurations tuned by
the BF approach. At the same time, by means of their genetic operators, GAs
converge into better solutions than those obtained by pure random searches. The
explored multi-dimensional parameter space is very complex and has multiple
local optima as the evolution of randomly generated configurations does not
always guarantee global solution.
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