You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
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.

Cited Abstract(s):
[1]: We describe an energy-based analogue of the time-based roofline model. We create this model from the perspective of algorithm designers and performance tuners, with the intent not of making exact predictions, but rather, developing highlevel analytic insights into the possible relationships among the time, energy, and power costs of an algorithm. The model expresses algorithms in terms of operations, concurrency, and memory traffic; and characterizes the machine based on a small number of simple cost parameters, namely, the time and energy costs per operation or per word of communication. We confirm the basic form of the model experimentally. From this model, we suggest under what conditions we ought to expect an algorithmic time-energy trade-off, and show how algorithm properties may help inform power management.
With energy efficiency moving into the focus of HPC research, modeling the power dissipation of chips has received much attention in recent years. A power model similar to the one used in this paper has been introduced in [1] . It concentrates, however, on microscopic quantities such as the energy cost for transferring data or for performing floating-point operations, and does not address multi-core issues. It would be interesting to reconcile both models for a more holistic view on chip power, but this is left for future work.