Development of predictive formulae or qualitative statements about the probable outcome of control campaigns entails knowledge of bait quality and distribution, behaviour, vagaries of weather at the time of the control campaign, and other factors which govern the probability that the target animals will accept bait. This paper collates experience in recognising, estimating and using some of these variables for predicting the outcome of large-scale poisoning, and discusses possible approaches to the solution of some hard-core problems.

Control of rabbits, Oryctolagus cuniculus, by poisoned bait has been widely practiced in Australia and New Zealand for many years. Since the 1950s, Compound 1080 has been the active ingredient and yielded good results (ca 90% kills) when first used. Recent trails (1973-74) in New Zealand showed that the main reason for poor results obtained nowadays is the failure of many rabbits to eat bait. Recent trials in Western Australia showed that there has been a significant decline in effectiveness of rabbit control by baiting during the period 1958-1975. Shortcomings in operational technique and increased resistance to the poison 1080 were eliminated as being major possible causes of recent poor results. An increased incidence of neophobic behavior in rabbit populations as a result of artificial selection by poison baiting is suggested as the most likely explanation of our findings.

Performing high-resolution, high-fidelity, three-dimensional simulations of Type Ia supernovae (SNe Ia) requires not only algorithms that accurately represent the correct physics, but also codes that effectively harness the resources of the most powerful supercomputers. We are developing a suite of codes that provide the capability to perform end-to-end simulations of SNe Ia, from the early convective phase leading up to ignition to the explosion phase in which deflagration/detonation waves explode the star to the computation of the light curves resulting from the explosion. In this paper we discuss these codes with an emphasis on the techniques needed to scale them to petascale architectures. We also demonstrate our ability to map data from a low Mach number formulation to a compressible solver.

Large-scale simulations are increasingly being used to study complex scientific and engineering phenomena. As a result, advanced visualization and data analysis are also becoming an integral part of the scientific process. Often, a key step in extracting insight from these large simulations involves the definition, extraction, and evaluation of features in the space and time coordinates of the solution. However, in many applications, these features involve a range of parameters and decisions that will affect the quality and direction of the analysis. Examples include particular level sets of a specific scalar field, or local inequalities between derived quantities. A critical step in the analysis is to understand how these arbitrary parameters/decisions impact the statistical properties of the features, since such a characterization will help to evaluate the conclusions of the analysis as a whole. We present a new topological framework that in a single-pass extracts and encodes entire families of possible features definitions as well as their statistical properties. For each time step we construct a hierarchical merge tree a highly compact, yet flexible feature representation. While this data structure is more than two orders of magnitude smaller than the raw simulation data it allows us to extract a set of features for any given parameter selection in a postprocessing step. Furthermore, we augment the trees with additional attributes making it possible to gather a large number of useful global, local, as well as conditional statistic that would otherwise be extremely difficult to compile. We also use this representation to create tracking graphs that describe the temporal evolution of the features over time. Our system provides a linked-view interface to explore the time-evolution of the graph interactively alongside the segmentation, thus making it possible to perform extensive data analysis in a very efficient manner. We demonstrate our framework by extracting and analyzing burning cells from a large-scale turbulent combustion simulation. In particular, we show how the statistical analysis enabled by our techniques provides new insight into the combustion process.

After a decade where high-end computing was dominated by the rapid pace of improvements to CPU frequencies, the performance of next-generation supercomputers is increasingly differentiated by varying interconnect designs and levels of integration. Understanding the tradeoffs of these system designs is a key step towards making effective petascale computing a reality. In this work, we conduct an extensive performance evaluation of five key scientific application areas: plasma micro-turbulence, quantum chromodynamics, micro-finite-element solid mechanics, supernovae, and general relativistic astrophysics that use a variety of advanced computation methods, including adaptive mesh refinement, lattice topologies, particle in cell, and unstructured finite elements. Scalability results and analysis are presented on three current high-end HPC systems, the IBM Blue Gene/P at Argonne National Laboratory, the Cray XT4 and the Berkeley Laboratory’s NERSC Center, and an Intel Xeon cluster at Lawrence Livermore National Laboratory. In this chapter, we present each code as a section, where we describe the application, the parallelization strategies, and the primary results on each of the three platforms. Then we follow with a collective analysis of the codes performance and make concluding remarks.

Turning the current experimental plasma accelerator state-of-the-art from a promising technology into mainstream scientific tools depends critically on high-performance, high-fidelity modeling of complex processes that develop over a wide range of space and time scales. As part of the U.S. Department of Energy's Exascale Computing Project, a team from Lawrence Berkeley National Laboratory, in collaboration with teams from SLAC National Accelerator Laboratory and Lawrence Livermore National Laboratory, is developing a new plasma accelerator simulation tool that will harness the power of future exascale supercomputers for high-performance modeling of plasma accelerators. We present the various components of the codes such as the new Particle-In-Cell Scalable Application Resource (PICSAR) and the redesigned adaptive mesh refinement library AMReX, which are combined with redesigned elements of the Warp code, in the new WarpX software. The code structure, status, early examples of applications and plans are discussed.