A domain specific approach to high performance heterogeneous computing

Users of heterogeneous computing systems face two problems: first, in understanding the trade-off relationships between the observable characteristics of their applications, such as latency and quality of the result, and second, how to exploit knowledge of these characteristics to allocate work to distributed computing platforms efficiently. A domain specific approach addresses both of these problems. By considering a subset of operations or functions, models of the observable characteristics or domain metrics may be formulated in advance, and populated at run-time for task instances. These metric models can then be used to express the allocation of work as a constrained integer program. These claims are illustrated using the domain of derivatives pricing in computational finance, with the domain metrics of workload latency and pricing accuracy. For a large, varied workload of 128 Black-Scholes and Heston model-based option pricing tasks, running upon a diverse array of 16 Multicore CPUs, GPUs and FPGAs platforms, predictions made by models of both the makespan and accuracy are generally within 10 percent of the run-time performance. When these models are used as inputs to machine learning and MILP-based workload allocation approaches, a latency improvement of up to 24 and 270 times over the heuristic approach is seen

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

Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal

Platform Dependent Verification: On Engineering Verification Tools for 21st Century

The paper overviews recent developments in platform-dependent explicit-state LTL model checking.Comment: In Proceedings PDMC 2011, arXiv:1111.006

The Influence of Meteorology on the Spread of Influenza: Survival Analysis of an Equine Influenza (A/H3N8) Outbreak

The influences of relative humidity and ambient temperature on the transmission of influenza A viruses have recently been established under controlled laboratory conditions. The interplay of meteorological factors during an actual influenza epidemic is less clear, and research into the contribution of wind to epidemic spread is scarce. By applying geostatistics and survival analysis to data from a large outbreak of equine influenza (A/H3N8), we quantified the association between hazard of infection and air temperature, relative humidity, rainfall, and wind velocity, whilst controlling for premises-level covariates. The pattern of disease spread in space and time was described using extraction mapping and instantaneous hazard curves. Meteorological conditions at each premises location were estimated by kriging daily meteorological data and analysed as time-lagged time-varying predictors using generalised Cox regression. Meteorological covariates time-lagged by three days were strongly associated with hazard of influenza infection, corresponding closely with the incubation period of equine influenza. Hazard of equine influenza infection was higher when relative humidity was <60% and lowest on days when daily maximum air temperature was 20–25°C. Wind speeds >30 km hour−1 from the direction of nearby infected premises were associated with increased hazard of infection. Through combining detailed influenza outbreak and meteorological data, we provide empirical evidence for the underlying environmental mechanisms that influenced the local spread of an outbreak of influenza A. Our analysis supports, and extends, the findings of studies into influenza A transmission conducted under laboratory conditions. The relationships described are of direct importance for managing disease risk during influenza outbreaks in horses, and more generally, advance our understanding of the transmission of influenza A viruses under field conditions

Detection and monitoring of surface subsidence associated with mining activities in the Witbank Coalfields South Africa using differential radar interferometry

Surface subsidence associated with coal mining activities in the Mpumalanga Province, South Africa, changes the natural environment in several ways and current challenges for mining companies lie in rehabilitation of the natural environment and the prevention of further degradation. To monitor the spatial and temporal evolution of surface subsidence, traditional field-based monitoring approaches, including GPS and spirit levelling, are employed at a number of locations. However, the resulting measurements are point-based and frequent visitations are necessary to map the evolution of surface subsidence basins over time. To address these limitations, differential interferograms derived from repeat-pass satellite-borne synthetic aperture radar (SAR) systems were tested for their ability to measure and monitor surface deformation. The resulting interferograms revealed several features indicative of surface subsidence. Ground truth data confirmed the presence of a subsidence basin detected using differential interferometry techniques during the 35 day period between April 12, 2008 and May 17, 2008, with a maximum vertical deformation of 3.2 cm being recorded. Interferometric monitoring revealed an eastward migration of the subsidence basin between June 2, 2008 and September 15, 2008, with an additional 4.7 cm of subsidence being observed. This migration coincides with the advance of the working face of the mine during this period. The results demonstrate the ability of interferometric synthetic aperture radar techniques to measure surface subsidence as well as the monitoring of the evolution of subsidence basins over time. This implies that the technique could be included, together with traditional field-based surveying techniques, in an operational monitoring system

On the relevance of Open Wireless sensors for NGN

Open Wireless Sensors are based on the Open Source Software and Open Source Hardware paradigms. The code used to program them and the information about the hardware design are freely released. We present the main characteristics of Open Wireless Sensor Networks (OWSNs) and report on two examples with the experimental results revealing the performance of OWSNs in terms of link quality and battery life. We demonstrate the relevance of using OWSNs in Next Generation Networks by showing the advantages of the Open Source model when applied to Wireless Sensor networks in terms of cost, personalisation and independence from a single entity as compared to proprietary solutions