Tactical Pasture Management: Enhancing Profits from Poplar Box Country

The project assembled basic information to allow effective management and manipulation of native pastures in the southern Maranoa region of Queensland. This involved a range of plant studies, including a grazing trial, to quantify the costs of poor pasture composition. While the results focus on perennial grasses, we recognise the important dietary role played by broad-leaved herbs. The plant manipulation studies focussed on ways to change the proportions of plants in a grazed pasture, eg. by recruitment or accelerated morbidity of existing plants. As most perennial grasses have a wide range of potential flowering times outside of mid-winter, rainfall exerts the major influence on flowering and seedset; exceptions are black speargrass, rough speargrass and golden beardgrass that flower only for a restricted period each year. This simplifies potential control options through reducing seedset. Data from field growth studies of four pasture grasses have been used to refine the State's pasture production model GRASP. We also provide detailed data on the forage value of many native species at different growth stages. Wiregrass dominance in pastures on a sandy red earth reduced wool value by only 5-10% at Roma in 1994/95 when winters were very dry and grass seed problems were minimal.

Implementation of parallel tridiagonal solvers for a heterogeneous computing environment

Tridiagonal diagonally dominant linear systems arise in many scientific and engineering applications. The standard Thomas algorithm for solving such systems is inherently serial forming a bottleneck in computation. Algorithms such as cyclic reduction and SPIKE reduce a single large tridiagonal system into multiple small independent systems which can be solved in parallel. We have developed portable cyclic reduction and SPIKE algorithm OpenCL implementations with the intent to target a range of co-processors in a heterogeneous computing environment including Field Programmable Gate Arrays (FPGAs), Graphics Processing Units (GPUs) and other multi-core processors. In this paper, we evaluate these designs in the context of solver performance, resource efficiency and numerical accuracy.