2,293 research outputs found
A spectral deferred correction strategy for low Mach number reacting flows subject to electric fields
We propose an algorithm for low Mach number reacting flows subjected to
electric field that includes the chemical production and transport of charged
species. This work is an extension of a multi-implicit spectral deferred
correction (MISDC) algorithm designed to advance the conservation equations in
time at scales associated with advective transport. The fast and nontrivial
interactions of electrons with the electric field are treated implicitly using
a Jacobian-Free Newton Krylov approach for which a preconditioning strategy is
developed. Within the MISDC framework, this enables a close and stable coupling
of diffusion, reactions and dielectric relaxation terms with advective
transport and is shown to exhibit second-order convergence in space and time.
The algorithm is then applied to a series of steady and unsteady problems to
demonstrate its capability and stability. Although developed in a
one-dimensional case, the algorithmic ingredients are carefully designed to be
amenable to multidimensional applications
A performance, energy consumption and reliability evaluation of workload distribution on heterogeneous devices
The constant need of higher performances and reduced power consumption has lead vendors to design heterogeneous devices that embed traditional Central Process Unit (CPU) and an accelerator, like a Graphics Processing Unit (GPU) or Field-programmable Gate Array (FPGA). When the CPU and the accelerator are used collaboratively the device computational performances reach their peak. However, the higher amount of resources employed for computation has, potentially, the side effect of increasing soft error rate. This thesis evaluates the reliability behaviour of AMD Kaveri Accelerated Processing Units (APU) executing four heterogeneous applications, each one representing an algorithm class. The workload is gradually distributed from the CPU to the GPU and both the energy consumption and execution time are measured. Then, an accelerated neutron beam was used to measure the realistic error rates of the different workload distributions. Finally, we evaluate which configuration provides the lowest error rate or allows the computation of the highest amount of data before experiencing a failure. As is shown in this thesis, energy consumption and execution time are mold by the same trend while error rates highly depend on algorithm class and workload distribution. Additionally, we show that, in most cases, the most reliable workload distribution is the one that delivers the highest performances. As experimentally proven, by choosing the correct workload distribution the device reliability can increase of up to 90x.A constante necessidade de maior desempenho e menor consumo de energia levou aos fabricantes a projetar dispositivos heterogêneos que incorporam uma Unidade Central de Processameno (CPU) tradicional e um acelerador, como uma Unidade de Processamento Gráfico (GPU) ou um Arranjo de Portas Programáveis em Campo (FPGA). Quando a CPU e o acelerador são usados de forma colaborativa, o desempenho computacional do dispositivo atinge seu pico. No entanto, a maior quantidade de recursos empregados para o cálculo tem, potencialmente, o efeito colateral de aumentar a taxa de erros. Esta tese avalia a confiabilidade das AMD Kaveri "Accelerated Processing Units"(APUs) executando quatro aplicações heterogêneas, cada uma representando uma classe de algoritmos. A carga de trabalho é gradualmente distribuÃda da CPU para a GPU e o consumo de energia e o tempo de execução são medidos. Em seguida, um feixe de neutrões é utilizado para medir as taxas de erro reais das diferentes distribuições de carga de trabalho. Por fim, avalia-se qual configuração fornece a menor taxa de erro ou permite o cálculo da maior quantidade de dados antes de ocorrer uma falha. Como é mostrado nesta tese, o consumo de energia e o tempo de execução são moldados pela mesma tendência, enquanto as taxas de erro dependem da classe de algoritmos e da distribuição da carga de trabalho. Além disso, é mostrado que, na maioria dos casos, a distribuição de carga de trabalho mais confiável é a que fornece o maior desempenho. Como comprovado experimentalmente, ao escolher a distribuição de carga de trabalho correta, a confiabilidade do dispositivo pode aumentar até 9 vezes
Quantifying knowledge exchange in R&D networks: A data-driven model
We propose a model that reflects two important processes in R&D activities of
firms, the formation of R&D alliances and the exchange of knowledge as a result
of these collaborations. In a data-driven approach, we analyze two large-scale
data sets extracting unique information about 7500 R&D alliances and 5200
patent portfolios of firms. This data is used to calibrate the model parameters
for network formation and knowledge exchange. We obtain probabilities for
incumbent and newcomer firms to link to other incumbents or newcomers which are
able to reproduce the topology of the empirical R&D network. The position of
firms in a knowledge space is obtained from their patents using two different
classification schemes, IPC in 8 dimensions and ISI-OST-INPI in 35 dimensions.
Our dynamics of knowledge exchange assumes that collaborating firms approach
each other in knowledge space at a rate for an alliance duration .
Both parameters are obtained in two different ways, by comparing knowledge
distances from simulations and empirics and by analyzing the collaboration
efficiency . This is a new measure, that takes also in
account the effort of firms to maintain concurrent alliances, and is evaluated
via extensive computer simulations. We find that R&D alliances have a duration
of around two years and that the subsequent knowledge exchange occurs at a very
low rate. Hence, a firm's position in the knowledge space is rather a
determinant than a consequence of its R&D alliances. From our data-driven
approach we also find model configurations that can be both realistic and
optimized with respect to the collaboration efficiency .
Effective policies, as suggested by our model, would incentivize shorter R&D
alliances and higher knowledge exchange rates.Comment: 35 pages, 10 figure
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