Memory-bound algorithms show complex performance and energy consumption behavior on multicore processors. We choose the lattice-Boltzmann method (LBM) on an Intel Sandy Bridge cluster as a prototype scenario to investigate if and how single-chip performance and power characteristics can be generalized to the highly parallel case. First we perform an analysis of a sparse-lattice LBM implementation for complex geometries. Using a single-core performance model, we predict the intra-chip saturation characteristics and the optimal operating point in terms of energy to solution as a function of implementation details, clock frequency, vectorization, and number of active cores per chip. We show that high single-core performance and a correct choice of the number of active cores per chip are the essential optimizations for lowest energy to solution at minimal performance degradation. Then we extrapolate to the MPI-parallel level and quantify the energy-saving potential of various optimizations and execution modes, where we find these guidelines to be even more important, especially when communication overhead is non-negligible. In our setup we could achieve energy savings of 35 in this case, compared to a naive approach. We also demonstrate that a simple non-reflective reduction of the clock speed leaves most of the energy saving potential unused.
The model of et al @cite_9 provides valuable insight into how much performance can be achieved with regard to the typical limiting factors memory bandwidth and arithmetic throughput. This allows for a first assessment of how far the performance of the code at hand deviates from the maximum sustainable performance (the light speed'').
@cite_9
2396986403
The throughput capacity of arbitrary wireless networks under the physical Signal to Interference Plus Noise Ratio (SINR) model has received much attention in recent years. In this paper, we investigate the question of how much the worst-case performance of uniform and non-uniform power assignments differ under constraints such as a bound on the area where nodes are distributed or restrictions on the maximum power available. We determine the maximum factor by which a non-uniform power assignment can outperform the uniform case in the SINR model. More precisely, we prove that in one-dimensional settings the capacity of a non-uniform assignment exceeds a uniform assignment by at most a factor of (O( L_ ) ) when the length of the network is (L_ ). In two-dimensional settings, the uniform assignment is at most a factor of (O( P_ ) ) worse than the non-uniform assignment if the maximum power is (P_ ). We provide algorithms that reach this capacity in both cases. These bounds are tight in the sense that previous work gave examples of networks where the lack of power control causes a performance loss in the order of these factors. To complement our theoretical results and to evaluate our algorithms with concrete input networks, we carry out simulations on random wireless networks. The results demonstrate that the link sets generated by the algorithms contain around 20–35 of all links. As a consequence, engineers and researchers may prefer the uniform model due to its simplicity if this degree of performance deterioration is acceptable.