In this project we explore several different techniques for visualizing data created by Hardware/Hybrid Accelerated Cosmology Code (HACC) cosmology simulations. We present four methods that have thus far been explored...
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ISBN:
(纸本)9781538668740
In this project we explore several different techniques for visualizing data created by Hardware/Hybrid Accelerated Cosmology Code (HACC) cosmology simulations. We present four methods that have thus far been explored. Mainly, we discuss visualizing data through ParaView, vl3, SPH interpolation, and virtual reality environments. We display our preliminary results, as well as our plans to begin applying our techniques to large scale data sets.
In this paper, we proposed a practical and efficient algorithm based on conventional semi-direct monocular visual odometry (SVO) algorithm, which mainly aims at the future application of the Simultaneous Localization ...
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ISBN:
(纸本)9781728152103
In this paper, we proposed a practical and efficient algorithm based on conventional semi-direct monocular visual odometry (SVO) algorithm, which mainly aims at the future application of the Simultaneous Localization and Mapping (SLAM) for embedded or mobile platforms such as robots and wearable devices. By applying the velocity momentum during the initial pose estimation, we present a novel algorithm for obtaining the initial pose, which is closer to the true value and more effective to solving the limitation of non-convergence in most existing approaches. A sparse image alignment module is also proposed to rectify the pose offset occurred at the corner, by elaborately resetting the relative pose at the location with large photometric error. The proposed lifted semi-direct monocular visual odometry has been extensively evaluated on benchmark dataset. The experimental result demonstrates that our method can explicitly generate the accurate initial poses without reducing the speed.
One of the greatest challenges facing scientists doing large computation of vector fields in a distributed parallel setting is the need for optimal parallel algorithms for flow visualization. To address this need, we ...
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ISBN:
(纸本)9781538668740
One of the greatest challenges facing scientists doing large computation of vector fields in a distributed parallel setting is the need for optimal parallel algorithms for flow visualization. To address this need, we present a new flow visualization method based on parallel 3D line integral convolution (LIC). Our approach uses the fact that 3D LIC only needs limited local information to design an embarrassingly parallel model with a trade-off between the additional memory cost of external cells and the time cost for communication. All data required for each process can be stored in either a local data block or the external cells which are sets of exterior data surrounding the local partition. One problem for parallel LIC is that equal domain size decomposition of the data cannot guarantee balanced parallel processes. To achieve a load-balanced visualization process, we repartition data using an estimate of the LIC computation time. In addition, to minimize the memory cost, we introduce a vector-driven external cell expansion method to reduce the required memory cost. We find that we can use fewer external cells with minimal loss of visual quality. We evaluate the performance of our visualization method by first comparing its parallel scalability with traditional integral field line visualization. Next, we compare our new partition method with other data partition methods to verify that the workload of our model is more balanced. Finally, we compare our external cell expansion method with a traditional layer-based external cell expansion method. Consequently, together with the new partition and external cell expansion methods, our parallel 3D LIC visualization proves to be an efficient and well-balanced parallel flow visualization with limited extra memory cost and a large saving of communication time.
Layout bugs commonly exist in mobile apps. Due to the fragmentation issues of smartphones, a layout bug may occur only on particular versions of smartphones. It is quite challenging to detect such bugs for state-of-th...
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ISBN:
(纸本)9781728149837
Layout bugs commonly exist in mobile apps. Due to the fragmentation issues of smartphones, a layout bug may occur only on particular versions of smartphones. It is quite challenging to detect such bugs for state-of-the-art commercial automated testing platforms, although they can test an app with thousands of different smartphones in parallel. The main reason is that typical layout bugs neither crash an app nor generate any error messages. In this paper, we present our work for detecting text-layout bugs, which account for a large portion of layout bugs. We model text-layout bug detection as a classification problem. This then allows us to address it with sophisticated image processing and machine learning techniques. To this end, we propose an approach which we call Textout. Textout takes screenshots as its input and adopts a specifically-tailored text detection method and a convolutional neural network (CNN) classifier to perform automatic text-layout bug detection. We collect 33,102 text-region images as our training dataset and verify the effectiveness of our tool with 1,481 text-region images collected from real-world apps. Textout achieves an AUC (area under the curve) of 0.956 on the test dataset and shows an acceptable overhead. The dataset is open-source released for follow-up research.
Due to the uncertain nature of weather prediction, climate simulations are usually performed multiple times with different spatial resolutions. The outputs of simulations are multi-resolution spatial temporal ensemble...
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Due to the uncertain nature of weather prediction, climate simulations are usually performed multiple times with different spatial resolutions. The outputs of simulations are multi-resolution spatial temporal ensembles. Each simulation run uses a unique set of values for multiple convective parameters. Distinct parameter settings from different simulation runs in different resolutions constitute a multi-resolution high-dimensional parameter space. Understanding the correlation between the different convective parameters, and establishing a connection between the parameter settings and the ensemble outputs are crucial to domain scientists. The multi-resolution high-dimensional parameter space, however, presents a unique challenge to the existing correlation visualization techniques. We present Nested parallel Coordinates Plot (NPCP), a new type of parallel coordinates plots that enables visualization of intra-resolution and inter-resolution parameter correlations. With flexible user control, NPCP integrates superimposition, juxtaposition and explicit encodings in a single view for comparative datavisualization and analysis. We develop an integrated visual analytics system to help domain scientists understand the connection between multi-resolution convective parameters and the large spatial temporal ensembles. Our system presents intricate climate ensembles with a comprehensive overview and on-demand geographic details. We demonstrate NPCP, along with the climate ensemble visualization system, based on real-world use-cases from our collaborators in computational and predictive science.
Leading-edge supercomputer systems have been designed to achieve the highest computational performance possible for running a wide variety of large-scale simulations, and the pre- and post-processing are usually not c...
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ISBN:
(纸本)9781538668740
Leading-edge supercomputer systems have been designed to achieve the highest computational performance possible for running a wide variety of large-scale simulations, and the pre- and post-processing are usually not considered in the main design feature. Although supercomputer systems may have peculiar CPU architecture, the auxiliary computational systems tend to use commodity based hardware and software in the form of servers and clusters. In the case of the K computer operational environment, at RIKEN R-CCS, the supercomputer itself is based on SPARC64 fx CPU architecture, and pre- and post-processing servers are based on traditional x86 CPU architecture. In this poster we present a largedatavisualization environment developed for this peculiar HPC operational environment, presenting some of the efforts made to meet the largedatavisualization needs. It is publicly known that the next-generation leading-edge Japanese supercomputer will abandon this CPU architecture in favor of another architecture, but we expect that some of the knowledge obtained in this development will also be useful for this future coming supercomputer system.
We study effective shared-memory, data-parallel techniques for searching for duplicate elements. We consider several data-parallel approaches, and how hash function, machine architecture, and data set can affect perfo...
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ISBN:
(纸本)9781538606179
We study effective shared-memory, data-parallel techniques for searching for duplicate elements. We consider several data-parallel approaches, and how hash function, machine architecture, and data set can affect performance. We conclude that most choices of algorithm and hash function are problematic for general usage. However, we demonstrate that the choice of the Hash-Fight algorithm with the FNV1a hash function has consistently good performance over all configurations.
Figure 1:All images rendered with our speculative ray tracing technique. First three columns: a massive channel-flow turbulence DNS dataset. Last two columns: an RM fluid instability dataset and an Enzo Astrophysics A...
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ISBN:
(纸本)9781538668740
Figure 1:All images rendered with our speculative ray tracing technique. First three columns: a massive channel-flow turbulence DNS dataset. Last two columns: an RM fluid instability dataset and an Enzo Astrophysics AMR dataset (left and right). The number of triangles from left to right: DNS2 (1.8 billion), DNS1-side and DNS1-back (0.9 billion), RM (108 million), and Enzo (8 million). Five images on the bottom row show ambient occlusion shading, and the rest show three-bounce path tracing. For all datasets, 32 samples per pixel were used to render images at 1024×1024 resolution, and one diffuse ray and 16 shadow rays were generated at every hit point. Using Stampede2 Skylake at the Texas Advanced Computing Center, each node with 192 GB memory, at least eight nodes are required to render the DNS dataset with speculation *** modern supercomputers offering petascale compute capability, scientific simulations are now producing terascale data. For comprehensive understanding of such largedata, ray tracing is becoming increasingly important for 3D-rendering in visualization due to its inherent ability to convey physically realistic visual information to the user. Implementing efficient parallel ray tracing systems on supercomputers while maximizing locality and parallelism is challenging because of the overhead incurred by ray communication across the cluster of compute nodes and data loading from storage. To address the problem, reordering rendering computations by means of ray batching and scheduling has been proposed to temporarily avoid inherent dependencies in the rendering computations and amortize the cost of expensive data moving operations over ray batches. In this paper, we introduce a novel speculative ray scheduling method that builds upon this insight but radically changes the approach to resolving dependencies by allowing redundant computations to a certain extent. To evaluate the method, we measure the performance of different implementations for
The unprecedented amounts of data generated from large scientific simulations impose a grand challenge in data analytics, and I/O simply becomes a major performance bottleneck. To address this challenge, we present an...
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ISBN:
(纸本)9780769561493
The unprecedented amounts of data generated from large scientific simulations impose a grand challenge in data analytics, and I/O simply becomes a major performance bottleneck. To address this challenge, we present an application-aware I/O optimization technique in support of interactive large-scale scientific visualization. We partition a scientific data into blocks, and carefully place data blocks in a memory hierarchy according to a characterization of data access patterns of user visualization operations. We conduct an empirical study to explore the parameter space to derive optimal solutions. We use real-world large-scale simulation datasets to demonstrate the effectiveness of our approach.
Nek5000 is a massively-parallel computational fluid dynamics code, which is widely used and researched, including as part of a co-design center of the Exascale Computing Project (ECP). As computation capacity reaches ...
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ISBN:
(纸本)9781538668740
Nek5000 is a massively-parallel computational fluid dynamics code, which is widely used and researched, including as part of a co-design center of the Exascale Computing Project (ECP). As computation capacity reaches exascale, storage bandwidth remains stable leading to a larger percentage of time spent performing I/O. In situ analysis overcomes this issue by processing the data before it is written to disk. One method for accomplishing in situ analysis is through SENSEI, a generic in situ interface that enables the use of many existing in situ infrastructures with little modification to the simulation. In this work, we present the instrumentation of Nek5000 with SENSEI and evaluate its ability to accelerate the development of large scale simulation campaigns.
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