Computer Architectures to Close the Loop in Real-time Optimization

© 2015 IEEE.Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as fast optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other

Balancing locality and concurrency: solving sparse triangular systems on GPUs

Many numerical optimisation problems rely on fast algorithms for solving sparse triangular systems of linear equations (STLs). To accelerate the solution of such equations, two types of approaches have been used: on GPUs, concurrency has been prioritised to the disadvantage of data locality, while on multi-core CPUs, data locality has been prioritised to the disadvantage of concurrency. In this paper, we discuss the interaction between data locality and concurrency in the solution of STLs on GPUs, and we present a new algorithm that balances both. We demonstrate empirically that, subject to there being enough concurrency available in the input matrix, our algorithm outperforms Nvidia’s concurrencyprioritising CUSPARSE algorithm for GPUs. Experimental results show a maximum speedup of 5.8-fold. Our solution algorithm, which we have implemented in OpenCL, requires a pre-processing phase that partitions the graph associated with the input matrix into sub-graphs, whose data can be stored in low-latency local memories. This preliminary analysis phase is expensive, but because it depends only on the input matrix, its cost can be amortised when solving for many different right-hand sides

The mosaic leafhopper Orientus ishidae: host plants, spatial distribution, infectivity, and transmission of 16SrV phytoplasmas to vines

Orientus ishidae (Matsumura) is an Asian species introduced into Europe and recently associated with 16SrV phytoplasmas, related to grapevine “flavescence dorée”. Its life cycle, host plants, spatial distribution, infection and vector capability have been investigated in vine-growing areas of Piedmont, NW Italy. The spatial distribution of adults in vineyards was studied by applying interpolation methods to trap capture data. Insects were subject to molecular analyses to verify phytoplasma presence and identity. DNA extraction and PCR were made to detect 16SrV phytoplasmas. Transmission experiments were set up, using different sources for phytoplasma acquisition, and two plant species and an artificial diet for inoculation. Whole mount in situ hybridization was made to detect phytoplasmas in the salivary glands of adults. In the vineyard agro-ecosystem, 19 plant species (11 families), mainly broadleaf trees and shrubs, were recognized as host plants of the insect. Adults were more abundant on putative host plants than on grapevines, with a clear clustering at the edges of vineyards, and without a massive intrusion into the vineyard from outside. 16SrV phytoplasmas were detected only in adults captured with yellow sticky traps (20 out of 188 tested). The transmission of 16SrV phytoplasmas was successful after phytoplasma acquisition from infected broad bean and inoculation on grapevine