Our paper analyzes the issue of managing structural and technological modernization of the Russian power utilities industry based on the basic criteria of sustainable development. We have chosen coal-fired generation and its defining technologies as the main subject for our analysis. Key points of the Russian power utilities development strategy that has been drawn up to 2030 are compared against the basic principles of sustainable development. Moreover, a mathematical economic model is proposed to justify the choice of coal-fired power plant technology from the standpoint of economic, social, and environmental efficiency.
The Gillespie τ-Leaping Method is an approximate algorithm that is faster than the exact Direct Method (DM) due to the progression of the simulation with larger time steps. However, the procedure to compute the time leap τ is quite expensive. In this paper, we explore the acceleration of the τ-Leaping Method using Graphics Processing Unit (GPUs) for ultra-large networks ( reaction channels). We have developed data structures and algorithms that take advantage of the unique hardware architecture and available libraries. Our results show that we obtain a performance gain of over 60x when compared with the best conventional implementations.
The Gillespie Stochastic Simulation Algorithm (GSSA) and its variants are cornerstone techniques to simulate reaction kinetics in situations where the concentration of the reactant is too low to allow deterministic techniques such as differential equations. The inherent limitations of the GSSA include the time required for executing a single run and the need for multiple runs for parameter sweep exercises due to the stochastic nature of the simulation. Even very efficient variants of GSSA are prohibitively expensive to compute and perform parameter sweeps. Here we present a novel variant of the exact GSSA that is amenable to acceleration by using graphics processing units (GPUs). We parallelize the execution of a single realization across threads in a warp (fine-grained parallelism). A warp is a collection of threads that are executed synchronously on a single multi-processor. Warps executing in parallel on different multi-processors (coarse-grained parallelism) simultaneously generate multiple trajectories. Novel data-structures and algorithms reduce memory traffic, which is the bottleneck in computing the GSSA. Our benchmarks show an 8×−120× performance gain over various state-of-the-art serial algorithms when simulating different types of models.
This paper provides a comparative entrepreneurial analysis of modern combined-cycle power generation technologies and future-oriented high-efficiency oxy-fuel combustion cycles with zero emissions. Considering the main criteria for sustainable development, we identify the generation technology that provides the lowest cost of electricity supply and the maximum economic efficiency of investments with equally high environmental indicators. Based on a comprehensive literature review and comparison of the technical and economic parameters of modern and forward-looking generation technologies under different economic conditions, the paper develops and presents the path of increasing the technical level of generation technologies, corresponding to the conditions of sustainable development at each moment of time. Furthermore, the paper analyses the technical and economic characteristics of the combined-cycle technology successfully applied in the world's energy systems and advanced oxy-fuel combustion cycles. In addition, the paper proposes a multifactorial economic-mathematical model that allows to evaluate the performance indicators of any of the considered technologies in accordance with the criteria for sustainable development.
In this paper, we describe a new brute force algorithm for building the -Nearest Neighbor Graph (k-NNG). The k-NNG algorithm has many applications in areas such as machine learning, bio-informatics, and clustering analysis. While there are very efficient algorithms for data of low dimensions, for high dimensional data the brute force search is the best algorithm. There are two main parts to the algorithm: the first part is finding the distances between the input vectors, which may be formulated as a matrix multiplication problem; the second is the selection of the k-NNs for each of the query vectors. For the second part, we describe a novel graphics processing unit (GPU)-based multi-select algorithm based on quick sort. Our optimization makes clever use of warp voting functions available on the latest GPUs along with user-controlled cache. Benchmarks show significant improvement over state-of-the-art implementations of the k-NN search on GPUs.
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