Recursive Layout Research Articles

A layout optimization method based on wave wake preprocessing concept for wave-wind hybrid energy farms

Major investment of renewable energy currently focuses on wind and solar, which are commercially mature. However, there is no large commercial application of wave energy, despite more than four decades of continuous development. Previous research has indicated that wave energy could supply a significant portion of world electricity consumption. Therefore, it is critical to incentivize the utilization of wave energy. The hybrid energy farms, combining wave energy with wind energy, have been considered as one of the most viable solutions to promote mature grid integration of wave energy. However, combining wind and wave requires the identification of adequate locations for both resources and development of layout optimization algorithms capable of handling the complexity of wave wakes. Wave wake analysis has been one of the biggest hurdles for the development of recursive wave farm layout optimization algorithms due to the required extremely time consuming computation processes for each wave wake iteration. This research proposes a new approach by preprocessing the wave wakes beforehand the actual execution of the recursive layout optimization algorithm. This proposed preprocessed wave wake model can be integrated with the different optimization algorithms to identify optimal layouts for hybrid wave-wind farms. The new approach was tested in two selected locations in the Gulf of Mexico with over 36 years (1979–2015) of historical meteorological data. It identifies locations capable of sustaining commercially viable levels of wind and wave energy while simultaneously avoiding risk from extreme oceanic conditions that in the past have damaged or destroyed wave energy converters. Although the two locations have different meteorological conditions, the new approach was able to identify layouts with promising results in both locations. Results indicated that the selected locations could produce very good power output with a wave-wind hybrid energy farm, and most wave and wind energy devices generated capacity factor with values higher than commercial threshold limits.

Compressed histograms with arbitrary bucket layouts for selectivity estimation

Selectivity estimation is an important step of query optimization in a database management system, and multi-dimensional histogram techniques have proved promising for selectivity estimation. Recent multi-dimensional histogram techniques such as GenHist and STHoles use an arbitrary bucket layout. This layout has the advantage of requiring a smaller number of buckets to model tuple densities than those required by the traditional grid or recursive layouts. However, the arbitrary bucket layout brings an inherent disadvantage of requiring more memory to store each bucket location information. This diminishes the advantage of requiring fewer buckets and, therefore, has an adverse effect on the resulting selectivity estimation accuracy. To our knowledge, however, no existing histogram-based technique with arbitrary layout addresses this issue. In this paper, we introduce the idea of bucket location compression and then demonstrate its effectiveness for improving selectivity estimation accuracy by proposing the STHoles+ technique. STHoles+ extends STHoles by quantizing each coordinate of a bucket relative to the coordinate of the smallest enclosing bucket. This quantization increases the number of histogram buckets that can be stored in the histogram. Our quantization scheme allows STHoles+ to trade precision of histogram bucket locations for storing more buckets. Experimental results show that STHoles+ outperforms STHoles on various data distributions, query distributions, and other factors such as available memory size, quantization resolution, and dimensionality of the data space.

Recursive array layouts and fast matrix multiplication

The performance of both serial and parallel implementations of matrix multiplication is highly sensitive to memory system behavior. False sharing and cache conflicts cause traditional column-major or row-major array layouts to incur high variability in memory system performance as matrix size varies. This paper investigates the use of recursive array layouts to improve performance and reduce variability. Previous work on recursive matrix multiplication is extended to examine several recursive array layouts and three recursive algorithms: standard matrix multiplication and the more complex algorithms of Strassen (1969) and Winograd. While recursive layouts significantly outperform traditional layouts (reducing execution times by a factor of 1.2-2.5) for the standard algorithm, they offer little improvement for Strassen's and Winograd's algorithms. For a purely sequential implementation, it is possible to reorder computation to conserve memory space and improve performance between 10 percent and 20 percent. Carrying the recursive layout down to the level of individual matrix elements is shown to be counterproductive; a combination of recursive layouts down to canonically ordered matrix tiles instead yields higher performance. Five recursive layouts with successively increasing complexity of address computation are evaluated and it is shown that addressing overheads can be kept in control even for the most computationally demanding of these layouts.

QUADTREE LAYOUTS AND I/O BANDWIDTH

Quadtree data structure, popular in 2-D applications, has not been studied in the context of VLSI embedding. This paper proposes two generic layout strategies for quadtree layout. Layout attributes are derived with primary focus on area-I/O trade-off. We demonstrate a simple layout mixing strategy to obtain improved boundary I/O characteristics. This is followed by development of a recursive layout mixing strategy which yields an order of improvement.