New auto-tuning technique for the hydrogen maser

Auto-tuning of the maser cavity compensates for cavity pulling effect, and other sources of contribution to the long term frequency drift. Schemes previously proposed for the maser cavity auto-tuning can have adverse effects on the performance of the maser. A new scheme is proposed based on the phase relationship between the electric and the magnetic fields inside the cavity. This technique has the desired feature of auto-tuning the cavity with a very high sensitivity and without disturbing the maser performance. Some approaches for the implementation of this scheme and possible areas of difficulty are examined

Auto-tuning for high performance autopilot design

A novel auto-tuning method for the RIDE controller algorithm is presented. The RIDE controller is applied to a high performance aircraft model. The tuner utilises a constrained genetic algorithm to automate the tuning process. The results of the tuner are compared with that of another tuning method which utilises unconstrained optimisation so as to highlight the efficacy of constrained optimisation for this application. It is shown from the results that the constrained genetic algorithm optimisation scheme offers a highly effective tuning solution which can be used to attain safe and high performance control with the RIDE control algorithm

Garbage collection auto-tuning for Java MapReduce on Multi-Cores

MapReduce has been widely accepted as a simple programming pattern that can form the basis for efficient, large-scale, distributed data processing. The success of the MapReduce pattern has led to a variety of implementations for different computational scenarios. In this paper we present MRJ, a MapReduce Java framework for multi-core architectures. We evaluate its scalability on a four-core, hyperthreaded Intel Core i7 processor, using a set of standard MapReduce benchmarks. We investigate the significant impact that Java runtime garbage collection has on the performance and scalability of MRJ. We propose the use of memory management auto-tuning techniques based on machine learning. With our auto-tuning approach, we are able to achieve MRJ performance within 10% of optimal on 75% of our benchmark tests

Real-Time Dedispersion for Fast Radio Transient Surveys, using Auto Tuning on Many-Core Accelerators

Dedispersion, the removal of deleterious smearing of impulsive signals by the interstellar matter, is one of the most intensive processing steps in any radio survey for pulsars and fast transients. We here present a study of the parallelization of this algorithm on many-core accelerators, including GPUs from AMD and NVIDIA, and the Intel Xeon Phi. We find that dedispersion is inherently memory-bound. Even in a perfect scenario, hardware limitations keep the arithmetic intensity low, thus limiting performance. We next exploit auto-tuning to adapt dedispersion to different accelerators, observations, and even telescopes. We demonstrate that the optimal settings differ between observational setups, and that auto-tuning significantly improves performance. This impacts time-domain surveys from Apertif to SKA.Comment: 8 pages, accepted for publication in Astronomy and Computin

Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning

Fine tuning distributed systems is considered to be a craftsmanship, relying on intuition and experience. This becomes even more challenging when the systems need to react in near real time, as streaming engines have to do to maintain pre-agreed service quality metrics. In this article, we present an automated approach that builds on a combination of supervised and reinforcement learning methods to recommend the most appropriate lever configurations based on previous load. With this, streaming engines can be automatically tuned without requiring a human to determine the right way and proper time to deploy them. This opens the door to new configurations that are not being applied today since the complexity of managing these systems has surpassed the abilities of human experts. We show how reinforcement learning systems can find substantially better configurations in less time than their human counterparts and adapt to changing workloads