Анализ силовых алгоритмов и инструментов визуализации графа и разработка инструмента визуализации графа

In order to improve the processing speed of highly complex video coding that involves high data rate and large amount of computation, a parallel video coding system optimization (PVCSO) method is proposed in this paper. By using PC cluster and general H.264 video coding standard for parallel coding experiments, the important factors that are critical to parallel coding efficiency are thoroughly examined and analyzed, such as the number of computation nodes, performance of the network and cluster, parallel coding mechanism, algorithm complexity and raw data distribution mode. Based on the experimental results and analysis, the PVCSO method is proposed and used to design and build practical high efficiency parallel video coding system. For illustration, four high efficiency application-oriented PC cluster based parallel video coding systems are designed and shown. Experimental results show that the PVCSO method can effectively balance the conflicting efficiency factors to achieve the best parallel efficiency and cost performance of the parallel coding system. It provides an effective and practical solution for designing high efficiency parallel video coding system which can significantly improve the processing speed of video coding with huge amount of computation.

The next generation VLSI circuits will be designed with millions of densely packed interconnect segments on a single chip. Inductive effects between these segments begin to dominate signal delay as the clock frequency is increased. Modern parasitic extraction tools to estimate the onchip inductive effects with high accuracy have had limited impact due to large computational and storage requirements. This work describes a parallel software package for inductance extraction called ParIS, which is capable of analyzing interconnect configurations involving several conductors within reasonable time. The main component of the software is a novel preconditioned iterative method that is used to solve a dense complex linear system of equations. The linear system represents the inductive coupling between filaments that are used to discretize the conductors. A variant of the fast multipole method is used to compute dense matrix-vector products with the coefficient matrix. ParIS uses a two-tier parallel formulation that allows mixed mode parallelization using both MPIand OpenMP. An MPI process is associated with each conductor. The computation within a conductor is parallelized using OpenMP. The parallel efficiency and scalability of the software is demonstrated through experiments on the IBM p690 and Intel and AMD Linux clusters. These experiments highlight the portability and efficiency of the software on multiprocessors with shared, distributed, and distributed-shared memory architectures.

Many widely used, latency sensitive, data-parallel distributed systems, such as HDFS, Hive, and Spark choose to use the Java Virtual Machine (JVM), despite debate on the overhead of doing so. This paper analyzes the extent and causes of the JVM performance overhead in the above mentioned systems. Surprisingly, we find that the warm-up overhead, i.e., class loading and interpretation of bytecode, is frequently the bottleneck. For example, even an I/O intensive, 1GB read on HDFS spends 33% of its execution time in JVM warm-up, and Spark queries spend an average of 21 seconds in warm-up.The findings on JVM warm-up overhead reveal a contradiction between the principle of parallelization, i.e., speeding up long running jobs by parallelizing them into short tasks, and amortizing JVM warm-up overhead through long tasks. We solve this problem by designing HotTub, a new JVM that amortizes the warm-up overhead over the lifetime of a cluster node instead of over a single job by reusing a pool of already warm JVMs across multiple applications. The speed-up is significant. For example, using HotTub results in up to 1.8X speedups for Spark queries, despite not adhering to the JVM specification in edge cases.

This paper presents the optimized design and implementation of sparse tensor-times-dense matrix multiply (SpTTM) for CPU and GPU platforms. This primitive is a critical bottleneck in data analysis and mining applications based on tensor methods, such as the Tucker decomposition. We first design and implement sequential SpTTM to avoid explicit data transformations between a tensor and a matrix, which is the conventional approach. We further optimize SpTTM on multicore CPU and GPU systems by parallelizing, avoiding locks, and exploiting data locality. Our sequential SpTTM is up to 3.5× faster than the SpTTM from Tensor Toolbox and 1.5× over that from Cyclops Tensor Framework. Our parallel algorithms show 4.1× speedup on multicore Intel Core i7 and 18.8× speedup on NVIDIA K40c GPU over our sequential SpTTM respectively.

Several graph visualization tools exist. However, they are not able to handle large graphs, and/or they do not allow interaction. We are interested on large graphs, with hundreds of thousands of nodes. Such graphs bring two challenges: the first one is that any straightforward interactive manipulation will be prohibitively slow. The second one is sensory overload: even if we could plot and replot the graph quickly, the user would be overwhelmed with the vast volume of information because the screen would be too cluttered as nodes and edges overlap each other.Our GMine system addresses both these issues, by using summarization and multi-resolution. GMine offers multi-resolution graph exploration by partitioning a given graph into a hierarchy of communities-within-communities and storing it into a novel R-treelike structure which we name G-Tree. GMine offers summarization by implementing an innovative subgraph extraction algorithm and then visualizing its output.

Mainly designed for simulating the fluid motion, Smooth Particle Hydrodynamics (SPH) SPH has been widely applied to many fields, such as aerospace aircraft design and ship design. First, this paper presents an implementation of serial SPH method algorithm using the reflection method as the boundary condition. This paper describes entire process of SPH and gives detailed descriptions of some important procedures. Second, this paper realizes the corresponding parallel algorithm of SPH, which includes neighbor particle searching and building, physical attributes computation and other main SPH method procedures. Third, details of mapping different parts of the SPH serial algorithm to parallel algorithm on GPU with CUDA C as parallel programming language are also shown in this paper. Taking advantage of powerful computation ability of GPU, the experiment approximately achieves a 8X speedup that shows the high efficiency of GPU-based programming. The parallel algorithm not only achieves a high performance, but also promises a reliability result with a small error rate as the experiment result shows.

Knowledge graphs are a key technique for linking and integrating cross‐domain data, concepts, tools, and knowledge to enable data‐driven analytics. As much of the world's data have become massive in size, visualizing graph entities and their interrelationships intuitively and interactively has become a crucial task for ingesting and better utilizing graph content to support semantic reasoning, discovering hidden knowledge discovering, and better scientific understanding of geophysical and social phenomena. Despite the fact that many such phenomena (e.g., disasters) have clear spatial footprints and geographic properties, their location information is considered only as a textual label in existing graph visualization tools, limiting their capability to reveal the geospatial distribution patterns of the graph nodes. In addition, most graph visualization techniques rely on 2D graph visualization, which constrains the dimensions of information that can be presented and lacks support for graph structure examination from multiple angles. To tackle the above challenges, we developed a novel 3D map‐based graph visualization algorithm to enable interactive exploration of graph content and patterns in a spatially explicit manner. The algorithm extends a 3D force directed graph by integrating a web map, an additional geolocational force, and a force balancing variable that allows for the dynamic adjustment of the 3D graph structure and layout. This mechanism helps create a balanced graph view between the semantic forces among the graph nodes and the attractive force from a geolocation to a graph node. Our solution offers a new perspective in visualizing and understanding spatial entities and events in a knowledge graph.

Application of massive parallel computer to computational wind engineering

Computer graphics applications require efficient tools to model geometric objects. The traditional approach based on compute-intensive matrix calculations is error-prone due to a lack of integration between geometric reasoning and matrix-based algorithms. Clifford algebra offers a solution to these issues since it permits specification of geometry at a coordinate-free level. The best way to exploit the symbolic computing power of geometric (Clifford) algebra is supporting its data types and operators directly in hardware. This paper outlines the architecture of S-CliffoSor (Sliced Clifford coprocessor), a parallelizable embedded coprocessor that executes native Clifford algebra operations. S-CliffoSor is a sliced coprocessor that can be replicated for parallel execution of concurrent Clifford operations. A single slice has been designed, implemented and tested on the Celoxica Inc. RC1000 board. The experimental results show the potential to achieve a 3times speedup for Clifford sums and 4times speedup for Clifford products compared to against the analogous operations in the software library generator GAIGEN.

Synchronous electrical activity in different brain regions is generally assumed to imply functional relationships between these regions. A measure for this synchrony is electroencephalography (EEG) coherence, computed between pairs of signals as a function of frequency. Existing high-density EEG coherence visualizations are generally either hypothesis-driven, or data-driven graph visualizations which are cluttered. In this paper, a new method is presented for data-driven visualization of high-density EEG coherence, which strongly reduces clutter and is referred to as functional unit (FU) map. Starting from an initial graph, with vertices representing electrodes and edges representing significant coherences between electrode signals, we define an FU as a set of electrodes represented by a clique consisting of spatially connected vertices. In an FU map, the spatial relationship between electrodes is preserved, and all electrodes in one FU are assigned an identical gray value. Adjacent FUs are visualized with different gray values and FUs are connected by a line if the average coherence between FUs exceeds a threshold. Results obtained with our visualization are in accordance with known electrophysiological findings. FU maps can be used as a preprocessing step for conventional analysis.

Exploring the Role of Domain Partitioning on Efficiency of Parallel Distributed Hydrologic Model Simulations Spatially distributed hydrologic models of watersheds and river basins are data and computation intensive because of the combined nature of hydrodynamics, complex forcings and heterogeneous parameter fields. Application of these models at fine temporal and spatial resolutions, and on large problem domains, is facilitated by parallel computation on multi-processor clusters. Notably, the computation efficiency of parallel simulations is crucially determined by the efficiency with which data are divided-and-distributed in a multiprocessor environment and how the information is shared between processors. While numerous data partitioning algorithms exist and have been extensively studied in computer science literature, detailed elucidation of the role of hydrologic model structure on data partitioning has not been presented yet. In addition, the relative role of computational load balance and interprocessor communication on parallel computation efficiency of a hydrologic model is not known. Considering the unstructured domain discretization scheme used in PIHM hydrologic model as an example, the paper first presents a generic methodology for incorporating hydrologic factors in optimal domain partitioning algorithms. The partitions are then used to explore the isolated role of computation load balance and interprocessor communication on parallel efficiency. Results confirm that parallel simulations on partitions that minimize interprocessor communication and divide the computational load equally are the most efficient. More importantly, load balance between processors is observed to be a more sensitive control on parallel efficiency than minimization of interprocessor communication. Further analyses of the efficiency and scalability of the parallel code for different partitioning configurations reveal a direct correspondence between parallel efficiency and theoretical metrics such as load balance ratio and communication to computation ratio. Results indicate that theoretical metrics can be used for the selection of best partitions before computationally intensive parallel simulations are performed. The study serves as a proof-of-concept evaluation of the impact of computation and communication on the efficiency of parallelized distributed hydrologic models at multiple resolutions.

Parallel computing of building fire using a domain decomposition method based on load balancing

Detecting the existence of variation from massive human genome data, and determining the breakpoints and types of variations is essential for analyzing structural variation. Pindel, an accurate detection tool based on pattern growth approach, is commonly used for discovering indels and other types of structural variations from next-generation sequencing data. The explosive growth of sequencing data poses new challenges to current implementation of Pindel. Here, we proposed ParaPindel, an optimized version of Pindel that utilizes distributed multiprocess, for efficient large-scale detection of structural variation for human whole-genome sequencing data. ParaPindel divides the chromosome into multiple small windows with a fixed-length window size, so as to realize the parallel detection between different windows and different chromosomes. A crosswindow with a smaller length is introduced to cope with possible structural variations at the edge of the window. The experimental results show that ParaPindel shortens the time to detect an individual’s genome-wide structural variation from 186 hours to 33 minutes under the premise that the detection results are basically consistent. Employing 256 processes on 128 nodes on the TH-IHN supercomputer, the speedup ratio has reached 163 times, and the parallel efficiency has reached 69.74%.

This study presents an asynchronous parallel strategy coordinating central processing unit (CPU) and graphic processing unit (GPU) to accelerate neighborhood operation (NO). Specifically, we propose a data partitioning method called multi-anchor task queuing and a task scheduling method called bi-direction task scheduling, which can support CPU and GPU to find the responsible data blocks rapidly and concurrently handle their tasks via a bi-direction merge. Moreover, we optimize the organization of threads distributed among the CPU and GPU. Experimental results show that when a 1.7 GB raster dataset is processed, the speedup ratio achieved by the proposed parallel algorithm reaches 29.63, which is 19% and 18% higher than those of the GPU and standard asynchronous parallel algorithm, respectively. Additionally, the load balance index is below 0.085, which is significantly better than the value achieved by a conventional algorithm. Thus, the strategy achieves a higher speedup ratio and more adaptable load balance, thereby accelerating the NO more efficiently. Further, the impacts of the data volume, computational intensity, organization mode of the GPU threads, and granularity of the GPU stream on the parallel efficiency are evaluated and discussed. We also test the efficiency of four other common NOs with our strategy.

We present a new implementation of the Fourier acceleration method for Landau gauge fixing. By means of a multigrid inversion we are able to avoid the use of the fast Fourier transform. This makes the method more flexible, and well suited for vector and parallel machines. We study the performance of this algorithm on serial and on parallel (APE100) machines for the 4-dimensional SU(2) case. We find that our method is equivalent to the standard implementation of Fourier acceleration already on a serial machine, and that it parallelizes very efficiently: its computational cost shows a linear speedup with the number of processors. We have also implemented, on the parallel machines, a version of the method using conjugate gradient instead of multigrid. This leads to an algorithm that is efficient at intermediate lattice volumes.