Structural and Parametric Optimization of S-CO2 Nuclear Power Plants.

The transition to the use of supercritical carbon dioxide as a working fluid for power generation units will significantly reduce the equipment's overall dimensions while increasing fuel efficiency and environmental safety. Structural and parametric optimization of S-CO2 nuclear power plants was carried out to ensure the maximum efficiency of electricity production. Based on the results of mathematical modeling, it was found that the transition to a carbon dioxide working fluid for the nuclear power plant with the BREST-OD-300 reactor leads to an increase of efficiency from 39.8 to 43.1%. Nuclear power plant transition from the Rankine water cycle to the carbon dioxide Brayton cycle with recompression is reasonable at a working fluid temperature above 455 °C due to the carbon dioxide cycle's more effective regeneration system.

Graphene Oxide Chemistry Management via the Use of KMnO4/K2Cr2O7 Oxidizing Agents.

In this paper, we propose a facile approach to the management of graphene oxide (GO) chemistry via its synthesis using KMnO4/K2Cr2O7 oxidizing agents at different ratios. Using Fourier Transformed Infrared Spectroscopy, X-ray Photoelectron Spectroscopy, and X-ray Absorption Spectroscopy, we show that the number of basal-plane and edge-located oxygenic groups can be controllably tuned by altering the KMnO4/K2Cr2O7 ratio. The linear two-fold reduction in the number of the hydroxyls and epoxides with the simultaneous three-fold rise in the content of carbonyls and carboxyls is indicated upon the transition from KMnO4 to K2Cr2O7 as a predominant oxidizing agent. The effect of the oxidation mixture's composition on the structure of the synthesized GOs is also comprehensively studied by means of X-ray diffraction, Raman spectroscopy, transmission electron microscopy, atomic-force microscopy, optical microscopy, and the laser diffraction method. The nanoscale corrugation of the GO platelets with the increase of the K2Cr2O7 content is signified, whereas the 10-100 µm lateral size, lamellar, and defect-free structure is demonstrated for all of the synthesized GOs regardless of the KMnO4/K2Cr2O7 ratio. The proposed method for the synthesis of GO with the desired chemistry opens up new horizons for the development of graphene-based materials with tunable functional properties.

Fast k-NNG construction with GPU-based quick multi-select.

In this paper, we describe a new brute force algorithm for building the k-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.

Efficient computation of k-Nearest Neighbour Graphs for large high-dimensional data sets on GPU clusters.

This paper presents an implementation of the brute-force exact k-Nearest Neighbor Graph (k-NNG) construction for ultra-large high-dimensional data cloud. The proposed method uses Graphics Processing Units (GPUs) and is scalable with multi-levels of parallelism (between nodes of a cluster, between different GPUs on a single node, and within a GPU). The method is applicable to homogeneous computing clusters with a varying number of nodes and GPUs per node. We achieve a 6-fold speedup in data processing as compared with an optimized method running on a cluster of CPUs and bring a hitherto impossible [Formula: see text]-NNG generation for a dataset of twenty million images with 15 k dimensionality into the realm of practical possibility.

Accelerating the Gillespie Exact Stochastic Simulation Algorithm using hybrid parallel execution on graphics processing units.

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.

Accelerating the Gillespie τ-Leaping Method using graphics processing units.

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 (>0.5e(6) 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.