Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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1506 research outputs found
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Modifying and Accelerating the Method of Moments Calculation
Get PDFThis manuscript deals with optimizing the numerical method called the method of moments (MoM). This method is widely utilized for field computation of 3D structures. MoM is exploited in hydraulics as well as in the electromagnetic field theory. Emphasis is put on minimizing calculations necessary for constructing a system of linear equations exploiting symmetry or similarity of elements of geometric structure. The manuscript also contains a comparison of computing times using standard MoM and a proposed modified MoM keeping the same structure of the solved construction
On Realization of Cinema Hall Fire Simulation Using Fire Dynamics Simulator
Get PDFCurrently known fire models are capable to describe fire dynamics in complex environments incorporating a wide variety of fire-related physical and chemical phenomena and utilizing large computational power of contemporary computers. In this paper, some issues related to realization of the simulation of fire in a cinema hall with sloping floor and curved ceiling furnished by upholstered seats modelled by FDS (Fire Dynamics Simulator) are discussed. The paper concentrates particularly on the impact of a computational meshes choice on resolving flow field and turbulence in the simulation and indicates problems related to parallelization of the calculation illustrated comparing sequential and parallel MPI calculation using 6 CPU cores. Results of the simulation described and their discussion demonstrate the ability of FDS simulation to capture main tendencies of smoke spread and to forecast the related safety risks realistically
Coupled Multiple Kernel Learning for Supervised Classification
Get PDFMultiple kernel learning (MKL) has recently received significant attention due to the fact that it is able to automatically fuse information embedded in multiple base kernels and then find a new kernel for classification or regression. In this paper, we propose a coupled multiple kernel learning method for supervised classification (CMKL-C), which comprehensively involves the intra-coupling within each kernel, inter-coupling among different kernels and coupling between target labels and real ones in MKL. Specifically, the intra-coupling controls the class distribution in a kernel space, the inter-coupling captures the co-information of base kernel matrices, and the last type of coupling determines whether the new learned kernel can make a correct decision. Furthermore, we deduce the analytical solutions to solve the CMKL-C optimization problem for highly efficient learning. Experimental results over eight UCI data sets and three bioinformatics data sets demonstrate the superior performance of CMKL-C in terms of the classification accuracy
Supervised Sparsity Preserving Projections for Face Recognition
Get PDFRecently feature extraction methods have commonly been used as a principled approach to understand the intrinsic structure hidden in high-dimensional data. In this paper, a novel supervised learning method, called Supervised Sparsity Preserving Projections (SSPP), is proposed. SSPP attempts to preserve the sparse representation structure of the data when identifying an efficient discriminant subspace. First, SSPP creates a concatenated dictionary by class-wise PCA decompositions and learns the sparse representation structure of each sample under the constructed dictionary using the least squares method. Second, by maximizing the ratio of non-local scatter to local scatter, a Laplacian discriminant function is defined to characterize the separability of the samples in the different sub-manifolds. Then, to achieve improved recognition results, SSPP integrates the learned sparse representation structure as a regular term into the Laplacian discriminant function. Finally, the proposed method is converted into a generalized eigenvalue problem. The extensive and promising experimental results on several popular face databases validate the feasibility and effectiveness of the proposed approach
An Efficient Certificate-Based Designated Verifier Signature Scheme
Get PDFCertificate-based public key cryptography not only solves certificate revocation problem in traditional PKI but also overcomes key escrow problem inherent in identity-based cryptosystems. This new primitive has become an attractive cryptographic paradigm. In this paper, we propose the notion and the security model of certificate-based designated verifier signatures (CBDVS). We provide the first construction of CBDVS and prove that our scheme is existentially unforgeable against adaptive chosen message attacks in the random oracle model. Our scheme only needs two pairing operations, and the signature is only one element in the bilinear group G1. To the best of our knowledge, our scheme enjoys shortest signature length with less operation cost
Energy Wall for Exascale Supercomputing
Get PDF"Sustainable development" is one of the major issues in the 21st century. Thus the notions of green computing, green development and so on show up one after another. As the large-scale parallel computing systems develop rapidly, energy consumption of such systems is becoming very huge, especially system performance reaches Petascale (10^15 Flops) or even Exascale (10^18 Flops). The huge energy consumption increases the system temperature, which seriously undermines the stability and reliability, and limits the growth of system size. The effects of energy consumption on scalability become a growing concern. Against the background, this paper proposes the concept of "Energy Wall" to highlight the significance of achieving scalable performance in peta/exascale supercomputing by taking energy consumption into account. We quantify the effect of energy consumption on scalability by building the energy-efficiency speedup model, which integrates computing performance and system energy. We define the energy wall quantitatively, and provide the theorem on the existence of the energy wall, and categorize the large-scale parallel computers according to the energy consumption. In the context of several representative types of HPC applications, we analyze and extrapolate the existence of the energy wall considering three kinds of topologies, 3D-Torus, binary n-cube and Fat tree which provides insights on how to mitigate the energy wall effect in system design and through hardware/software optimization in peta/exascale supercomputing
Nested-Loops Tiling for Parallelization and Locality Optimization
Get PDFData locality improvement and nested loops parallelization are two complementary and competing approaches for optimizing loop nests that constitute a large portion of computation times in scientific and engineering programs. While there are effective methods for each one of these, prior studies have paid less attention to address these two simultaneously. This paper proposes a unified approach that integrates these two techniques to obtain an appropriate locality conscious loop transformation to partition the loop iteration space into outer parallel tiled loops. The approach is based on the polyhedral model to achieve a multidimensional affine scheduling as a transformation that result the largest groups of tilable loops with maximum coarse grain parallelism, as far as possible. Furthermore, tiles will be scheduled on processor cores to exploit maximum data reuse through scheduling tiles with high volume of data sharing on the same core consecutively or on different cores with shared cache at around the same time
Realization and Extension of the Xfrog Approach for Plant Modelling in the Graph-Grammar Based Language XL
Get PDFTwo well-known approaches for modelling virtual vegetation are grammar-based methods (L-systems) and the Xfrog method, which is based on graph transformations expanding "multiplier" nodes. We show that both approaches can be unified in the framework of "relational growth grammars", a variant of parallel graph grammars. We demonstrate this possibility and the synergistic benefits of the combination of both methods at simple plant models which were processed using our open-source software GroIMP
Algorithms for Measuring Similarity Between ELH Concept Descriptions: A Case Study on Snomed ct
Get PDFIn Description Logics, subsumption is regarded as one of the most prominent reasoning services. It checks, relative to the logical definitions in the ontology, whether one concept is more general/specific than another. When no subsumption relationship is identified, however, no information about the two concepts can be given. In several realistic Semantic Web applications, knowing the level of similarity between two concepts, though lacking the subsumption relationship, is beneficial. This work introduces a new method for measuring the degree of similarity between two concept descriptions in the DL ELH, despite not being in a subsumption relation. Two algorithms are devised based on the known homomorphism-based structural subsumption characterization. The first algorithm employs the top-down approach, whereas the second is carried out in the reverse direction. A bottom-up algorithm has better efficiency, making it more suitable to large-scale ontologies developed using an inexpressive DL in the EL family, such as the renowned medical ontology Snomed ct. The computational performance of the proposed measure is intensively studied, and interesting findings in Snomed ct are reported
Energy-Efficient Concurrency Control for Dynamic-Priority Real-Time Tasks with Abortable Critical Sections
Get PDFIn this paper, we are interested in energy-efficient concurrency control for real-time tasks on a non-ideal DVS processor. Based on well-known ceiling-based concurrency control protocols (such as priority ceiling protocol (PCP) and stack resource policy (SRP)), researchers have proposed energy-efficient approaches to mange concurrent accesses to shared resources so that the energy consumption can be reduced. However, ceiling-based protocols have a problem of ceiling blocking which imposes a great impact on the performance of real-time systems. In order to achieve sufficient performance, we propose a new protocol, called conditional abortable stack resource policy (CA-SRP), to resolve the ceiling blocking problem for dynamic-priority real-time tasks by incorporating a conditional abort rule into SRP. Based on the schedulability analysis of CA-SRP, we also propose a method, called dynamic speed assignment (DSA), to dynamically calculate and assign proper processor speeds for task execution so that the energy consumption can be reduced further. The capabilities of our proposed CA-SRP and DSA have been evaluated by a series of experiments, for which we have encouraging results