To elucidate the dynamics of information processing in the brain, it is necessary to identify the direction of neural information transmission in the neuronal network and clarify the effects (i.e., the causal relationship) of neuronal activity in one area on neuronal activity in another area. Convergent cross mapping (CCM) has been employed in the neuroscience field to examine the effective connectivity of brain functions. CCM can detect causality from time series data created from deterministic and nonlinear systems. Because CCM includes complicated processes such as the determination of advance parameters, the confirmation of nonlinearity, and the interpretation of results, which results in a lowering of the usability of CCM, there is a strong need for an effective visual interface. In this paper, we propose a visual analytic system that increases the usability of CCM and contributes to new discoveries in effective connectivity. The usability was evaluated using a domain expert questionnaire. It was confirmed that the usability was improved by comparing the proposed system to the original character user interface from the viewpoint of the results and process comprehensibility. In addition, with the proposed system, new findings in human brain connectivity have been obtained from actual magnetoencephalography data during visual cognitive task and resting-state task.
{"title":"Visual analytics of brain effective connectivity using convergent cross mapping","authors":"H. Natsukawa, K. Koyamada","doi":"10.1145/3139295.3139303","DOIUrl":"https://doi.org/10.1145/3139295.3139303","url":null,"abstract":"To elucidate the dynamics of information processing in the brain, it is necessary to identify the direction of neural information transmission in the neuronal network and clarify the effects (i.e., the causal relationship) of neuronal activity in one area on neuronal activity in another area. Convergent cross mapping (CCM) has been employed in the neuroscience field to examine the effective connectivity of brain functions. CCM can detect causality from time series data created from deterministic and nonlinear systems. Because CCM includes complicated processes such as the determination of advance parameters, the confirmation of nonlinearity, and the interpretation of results, which results in a lowering of the usability of CCM, there is a strong need for an effective visual interface. In this paper, we propose a visual analytic system that increases the usability of CCM and contributes to new discoveries in effective connectivity. The usability was evaluated using a domain expert questionnaire. It was confirmed that the usability was improved by comparing the proposed system to the original character user interface from the viewpoint of the results and process comprehensibility. In addition, with the proposed system, new findings in human brain connectivity have been obtained from actual magnetoencephalography data during visual cognitive task and resting-state task.","PeriodicalId":92446,"journal":{"name":"SIGGRAPH Asia 2017 Symposium on Visualization. SIGGRAPH Asia Symposium on Visualization (2017 : Bangkok, Thailand)","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89605000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mingqian Zhao, Yijia Su, Jian Zhao, Shaoyu Chen, Huamin Qu
Situated Analytics has become popular and important with the resurge of Augmented Reality techniques and the prevalence of mobile platforms. However, existing Situated Analytics could only assist in simple visual analytical tasks such as data retrieval, and most visualization systems capable of aiding complex Visual Analytics are only designed for desktops. Thus, there remain lots of open questions about how to adapt desktop visualization systems to mobile platforms. In this paper, we conduct a study to discuss challenges and trade-offs during the process of adapting an existing desktop system to a mobile platform. With a specific example of interest, egoSlider [Wu et al. 2016], a four-view dynamic ego-centric network visualization system is tailored to adapt the iPhone platform. We study how different view management techniques and interactions influence the effectiveness of presenting multi-scale visualizations including Scatterplot and Storyline visualizations. Simultaneously, a novel Main view+Thumbnails interface layout is devised to support smooth linking between multiple views on mobile platforms. We assess the effectiveness of our system through expert interviews with four experts in data visualization.
随着增强现实技术的复兴和移动平台的普及，定位分析已经变得流行和重要。然而，现有的located Analytics只能辅助简单的可视化分析任务，如数据检索，而且大多数能够辅助复杂可视化分析的可视化系统仅为桌面设计。因此，关于如何使桌面可视化系统适应移动平台，仍然存在许多悬而未决的问题。在本文中，我们进行了一项研究，讨论在将现有桌面系统适应移动平台的过程中所面临的挑战和权衡。以我们感兴趣的一个具体例子egoSlider为例[Wu et al. 2016]，一个为适应iPhone平台而量身定制的四视图动态自我中心网络可视化系统。我们研究了不同的视图管理技术和交互如何影响呈现多尺度可视化的有效性，包括散点图和故事线可视化。同时，设计了新颖的主视图+缩略图界面布局，以支持移动平台上多个视图之间的平滑链接。我们通过与四位数据可视化专家的专家访谈来评估我们系统的有效性。
{"title":"Mobile situated analytics of ego-centric network data","authors":"Mingqian Zhao, Yijia Su, Jian Zhao, Shaoyu Chen, Huamin Qu","doi":"10.1145/3139295.3139309","DOIUrl":"https://doi.org/10.1145/3139295.3139309","url":null,"abstract":"Situated Analytics has become popular and important with the resurge of Augmented Reality techniques and the prevalence of mobile platforms. However, existing Situated Analytics could only assist in simple visual analytical tasks such as data retrieval, and most visualization systems capable of aiding complex Visual Analytics are only designed for desktops. Thus, there remain lots of open questions about how to adapt desktop visualization systems to mobile platforms. In this paper, we conduct a study to discuss challenges and trade-offs during the process of adapting an existing desktop system to a mobile platform. With a specific example of interest, egoSlider [Wu et al. 2016], a four-view dynamic ego-centric network visualization system is tailored to adapt the iPhone platform. We study how different view management techniques and interactions influence the effectiveness of presenting multi-scale visualizations including Scatterplot and Storyline visualizations. Simultaneously, a novel Main view+Thumbnails interface layout is devised to support smooth linking between multiple views on mobile platforms. We assess the effectiveness of our system through expert interviews with four experts in data visualization.","PeriodicalId":92446,"journal":{"name":"SIGGRAPH Asia 2017 Symposium on Visualization. SIGGRAPH Asia Symposium on Visualization (2017 : Bangkok, Thailand)","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88208097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Drawing a large graph into the limited display space often raises visual clutter and overlapping problems. The complex structure hinders the exploration of significant patterns of connections. For time-varying graphs, it is difficult to reveal the evolution of structures. In this paper, we group nodes and links into partitions, where objects within a partition are more closely related. Besides, partitions maintain stable across time steps. The complex structure of a partition is simplified by mapping to a pattern and the evolution is exposed by comparing patterns of two consecutive time steps. We created various visual designs to present different scenarios of changes. In order to achieve a smooth animation of time-varying graphs, we extract the graph layout at each time step from a super-layout which is based on the super-graph and super-community. The effectiveness of our approach is verified with two datasets, one is a synthetic dataset, and the other is the DBLP dataset.
{"title":"Smooth animation of structure evolution in time-varying graphs with pattern matching","authors":"Yunzhe Wang, G. Baciu, Chenhui Li","doi":"10.1145/3139295.3139302","DOIUrl":"https://doi.org/10.1145/3139295.3139302","url":null,"abstract":"Drawing a large graph into the limited display space often raises visual clutter and overlapping problems. The complex structure hinders the exploration of significant patterns of connections. For time-varying graphs, it is difficult to reveal the evolution of structures. In this paper, we group nodes and links into partitions, where objects within a partition are more closely related. Besides, partitions maintain stable across time steps. The complex structure of a partition is simplified by mapping to a pattern and the evolution is exposed by comparing patterns of two consecutive time steps. We created various visual designs to present different scenarios of changes. In order to achieve a smooth animation of time-varying graphs, we extract the graph layout at each time step from a super-layout which is based on the super-graph and super-community. The effectiveness of our approach is verified with two datasets, one is a synthetic dataset, and the other is the DBLP dataset.","PeriodicalId":92446,"journal":{"name":"SIGGRAPH Asia 2017 Symposium on Visualization. SIGGRAPH Asia Symposium on Visualization (2017 : Bangkok, Thailand)","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90964826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visualizing the group structure of graphs is important in analyzing complex networks. The group structure referred to here includes not only community structures defined in terms of modularity and the like but also group divisions based on node attributes. Group-In-a-Box (GIB) is a graph-drawing method designed for visualizing the group structure of graphs. Using a GIB layout, it is possible to simultaneously visualize group sizes and both within-group and between-group structures. However, conventional GIB layouts do not optimize display of between-group relations, causing many long edges to appear in the graph area and potentially reducing graph readability. This paper focuses on the tree structure of treemap used in GIB layouts as a basis for proposing a tree-reordered GIB (TRGIB) layout with a procedure for replacing sibling nodes in the tree structure. Group proximity is defined in terms of between-group distances and connection weights, and an optimal tree reordering problem (OTRP) that minimizes group proximity is formulated as a mixed-integer linear programming (MILP) problem. Through computational experiments, we show that optimal layout generation is possible in practical time by solving the OTRP using a general mathematical programming solver.
图群结构的可视化在复杂网络分析中具有重要意义。这里所指的组结构不仅包括根据模块化等定义的社区结构，还包括基于节点属性的组划分。group - in -a- box (GIB)是一种图形绘制方法，旨在将图形的组结构可视化。使用GIB布局，可以同时可视化组大小以及组内和组间结构。然而，传统的GIB布局并没有优化组间关系的显示，导致许多长边出现在图形区域，并可能降低图形的可读性。本文重点研究了用于GIB布局的树状图的树状结构，并以此为基础提出了一种树状重排序GIB (TRGIB)布局，该布局具有替换树状结构中的兄弟节点的过程。根据组间距离和连接权重定义组邻近度，并将最小化组邻近度的最优树重排序问题(OTRP)表述为混合整数线性规划(MILP)问题。通过计算实验，我们证明了使用通用数学规划求解器求解OTRP在实际时间内可以生成最优布局。
{"title":"Optimal tree reordering for group-in-a-box graph layouts","authors":"Yosuke Onoue, K. Koyamada","doi":"10.1145/3139295.3139308","DOIUrl":"https://doi.org/10.1145/3139295.3139308","url":null,"abstract":"Visualizing the group structure of graphs is important in analyzing complex networks. The group structure referred to here includes not only community structures defined in terms of modularity and the like but also group divisions based on node attributes. Group-In-a-Box (GIB) is a graph-drawing method designed for visualizing the group structure of graphs. Using a GIB layout, it is possible to simultaneously visualize group sizes and both within-group and between-group structures. However, conventional GIB layouts do not optimize display of between-group relations, causing many long edges to appear in the graph area and potentially reducing graph readability. This paper focuses on the tree structure of treemap used in GIB layouts as a basis for proposing a tree-reordered GIB (TRGIB) layout with a procedure for replacing sibling nodes in the tree structure. Group proximity is defined in terms of between-group distances and connection weights, and an optimal tree reordering problem (OTRP) that minimizes group proximity is formulated as a mixed-integer linear programming (MILP) problem. Through computational experiments, we show that optimal layout generation is possible in practical time by solving the OTRP using a general mathematical programming solver.","PeriodicalId":92446,"journal":{"name":"SIGGRAPH Asia 2017 Symposium on Visualization. SIGGRAPH Asia Symposium on Visualization (2017 : Bangkok, Thailand)","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72846093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to elucidate the developmental mechanisms of multicellular organisms, it is important to quantify the spatiotemporal features (phenotypic characteristics) of cells appearing during cell division and to analyze their relationships (correlations). Many analytical techniques have been proposed, including graph visualization technology. However, in addition to specifying interesting characteristics from large data, obtaining biological interpretations is difficult and time-consuming. To solve such problems, we developed a visual analysis system that enables exploratory analysis by linking the phenotypic characteristics of nematodes to the spatiotemporal shape of the cell nucleus. Through our experiments, we performed user evaluations for experts who research the developmental dynamics of the cells. This system enabled the users to analyze these dynamics thoroughly and develop novel concepts.
{"title":"Development of a visual analytics system for cell division dynamics in early C.elegans embryos","authors":"Sayaka Nagai, Naohisa Sakamoto","doi":"10.1145/3139295.3139310","DOIUrl":"https://doi.org/10.1145/3139295.3139310","url":null,"abstract":"In order to elucidate the developmental mechanisms of multicellular organisms, it is important to quantify the spatiotemporal features (phenotypic characteristics) of cells appearing during cell division and to analyze their relationships (correlations). Many analytical techniques have been proposed, including graph visualization technology. However, in addition to specifying interesting characteristics from large data, obtaining biological interpretations is difficult and time-consuming. To solve such problems, we developed a visual analysis system that enables exploratory analysis by linking the phenotypic characteristics of nematodes to the spatiotemporal shape of the cell nucleus. Through our experiments, we performed user evaluations for experts who research the developmental dynamics of the cells. This system enabled the users to analyze these dynamics thoroughly and develop novel concepts.","PeriodicalId":92446,"journal":{"name":"SIGGRAPH Asia 2017 Symposium on Visualization. SIGGRAPH Asia Symposium on Visualization (2017 : Bangkok, Thailand)","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77158385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Distribution models are widely used for data reduction applications. The Gaussian mixture model (GMM) is a powerful tool to capture multiple-peak distributions. For distribution-based vector field datasets represented by GMM, there are still loss of information which sometimes causes too much error when performing flow line tracing tasks. As a compensation, we analyze the vector transition pattern between consecutive vector directions. The vector transition is depicted by distributions of winding angles. When performing streamline and pathline tracing, we utilize the winding angle to estimate a conditional distribution of local vectors, using the Bayes Theorem. The conditional distribution can be used for both Monte Carlo flow line tracing, and single flow line tracing. We applied our distribution model on data reduction applications, and demonstrated that improved flow line tracing quality was achieved.
{"title":"Winding angle assisted particle tracing in distribution-based vector field","authors":"Cheng Li, Han-Wei Shen","doi":"10.1145/3139295.3139297","DOIUrl":"https://doi.org/10.1145/3139295.3139297","url":null,"abstract":"Distribution models are widely used for data reduction applications. The Gaussian mixture model (GMM) is a powerful tool to capture multiple-peak distributions. For distribution-based vector field datasets represented by GMM, there are still loss of information which sometimes causes too much error when performing flow line tracing tasks. As a compensation, we analyze the vector transition pattern between consecutive vector directions. The vector transition is depicted by distributions of winding angles. When performing streamline and pathline tracing, we utilize the winding angle to estimate a conditional distribution of local vectors, using the Bayes Theorem. The conditional distribution can be used for both Monte Carlo flow line tracing, and single flow line tracing. We applied our distribution model on data reduction applications, and demonstrated that improved flow line tracing quality was achieved.","PeriodicalId":92446,"journal":{"name":"SIGGRAPH Asia 2017 Symposium on Visualization. SIGGRAPH Asia Symposium on Visualization (2017 : Bangkok, Thailand)","volume":"115 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75716489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The SIGGRAPH Asia Symposium on Visualization is an ideal platform for attendees to explore the opportunities and challenges of cutting-edge visualization techniques which facilitates human being to understand the data sets. The program aims to cover the development, technology, and demonstration of visualization techniques and their interactive applications.
{"title":"SIGGRAPH Asia 2017 Symposium on Visualization","authors":"K. Koyamada, Puripant Ruchikachorn","doi":"10.1145/3139295","DOIUrl":"https://doi.org/10.1145/3139295","url":null,"abstract":"The SIGGRAPH Asia Symposium on Visualization is an ideal platform for attendees to explore the opportunities and challenges of cutting-edge visualization techniques which facilitates human being to understand the data sets. The program aims to cover the development, technology, and demonstration of visualization techniques and their interactive applications.","PeriodicalId":92446,"journal":{"name":"SIGGRAPH Asia 2017 Symposium on Visualization. SIGGRAPH Asia Symposium on Visualization (2017 : Bangkok, Thailand)","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80704218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There are many tasks that humans perform that involve observing video streams, as well as tracking objects or quantities related to the events depicted in the video, that can be made more transparent by the addition of appropriate drawings to a video, e.g., tracking the behavior of autonomous robots or following the motion of players across a soccer field. We describe a specification of a general means of describing groups of time-varying discrete visualizations, as well as a demonstration of overlaying those visualizations onto videos in an augmented reality manner so as to situate them in a real-world context, when such a context is available and meaningful. Creating such videos can be especially useful in the case of autonomous agents operating in the real world; we demonstrate our visualization procedures on two example robotic domains. We take the complex algorithms controlling the robots' actions in the real world and create videos that are much more informative than the original plain videos.
{"title":"A multi-layered visualization language for video augmentation","authors":"Danny Zhu, M. Veloso","doi":"10.1145/3139295.3139307","DOIUrl":"https://doi.org/10.1145/3139295.3139307","url":null,"abstract":"There are many tasks that humans perform that involve observing video streams, as well as tracking objects or quantities related to the events depicted in the video, that can be made more transparent by the addition of appropriate drawings to a video, e.g., tracking the behavior of autonomous robots or following the motion of players across a soccer field. We describe a specification of a general means of describing groups of time-varying discrete visualizations, as well as a demonstration of overlaying those visualizations onto videos in an augmented reality manner so as to situate them in a real-world context, when such a context is available and meaningful. Creating such videos can be especially useful in the case of autonomous agents operating in the real world; we demonstrate our visualization procedures on two example robotic domains. We take the complex algorithms controlling the robots' actions in the real world and create videos that are much more informative than the original plain videos.","PeriodicalId":92446,"journal":{"name":"SIGGRAPH Asia 2017 Symposium on Visualization. SIGGRAPH Asia Symposium on Visualization (2017 : Bangkok, Thailand)","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80858721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Steve Petruzza, Aniketh Venkat, Attila Gyulassy, Giorgio Scorzelli, Valerio Pascucci, Frederick Federer, Alessandra Angelucci, Peer-Timo Bremer
Modern science is inundated with ever increasing data sizes as computational capabilities and image acquisition techniques continue to improve. For example, simulations are tackling ever larger domains with higher fidelity, and high-throughput microscopy techniques generate larger data that are fundamental to gather biologically and medically relevant insights. As the image sizes exceed memory, and even sometimes local disk space, each step in a scientific workflow is impacted. Current software solutions enable data exploration with limited interactivity for visualization and analytic tasks. Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability. We present a software infrastructure that simplifies end-to-end visualization and analysis of massive data. First, a hierarchical streaming data access layer enables interactive exploration of remote data, with fast data fetching to test analytics on subsets of the data. Second, a library simplifies the process of developing new analytics algorithms, allowing users to rapidly prototype new approaches and deploy them in an HPC setting. Third, a scalable runtime system automates mapping analysis algorithms to whatever computational hardware is available, reducing the complexity of developing scaling algorithms. We demonstrate the usability and performance of our system using a use case from neuroscience: filtering, registration, and visualization of tera-scale microscopy data. We evaluate the performance of our system using a leadership-class supercomputer, Shaheen II.
{"title":"ISAVS: Interactive Scalable Analysis and Visualization System.","authors":"Steve Petruzza, Aniketh Venkat, Attila Gyulassy, Giorgio Scorzelli, Valerio Pascucci, Frederick Federer, Alessandra Angelucci, Peer-Timo Bremer","doi":"10.1145/3139295.3139299","DOIUrl":"https://doi.org/10.1145/3139295.3139299","url":null,"abstract":"<p><p>Modern science is inundated with ever increasing data sizes as computational capabilities and image acquisition techniques continue to improve. For example, simulations are tackling ever larger domains with higher fidelity, and high-throughput microscopy techniques generate larger data that are fundamental to gather biologically and medically relevant insights. As the image sizes exceed memory, and even sometimes local disk space, each step in a scientific workflow is impacted. Current software solutions enable data exploration with limited interactivity for visualization and analytic tasks. Furthermore analysis on HPC systems often require complex hand-written parallel implementations of algorithms that suffer from poor portability and maintainability. We present a software infrastructure that simplifies end-to-end visualization and analysis of massive data. First, a hierarchical streaming data access layer enables interactive exploration of remote data, with fast data fetching to test analytics on subsets of the data. Second, a library simplifies the process of developing new analytics algorithms, allowing users to rapidly prototype new approaches and deploy them in an HPC setting. Third, a scalable runtime system automates mapping analysis algorithms to whatever computational hardware is available, reducing the complexity of developing scaling algorithms. We demonstrate the usability and performance of our system using a use case from neuroscience: filtering, registration, and visualization of tera-scale microscopy data. We evaluate the performance of our system using a leadership-class supercomputer, Shaheen II.</p>","PeriodicalId":92446,"journal":{"name":"SIGGRAPH Asia 2017 Symposium on Visualization. SIGGRAPH Asia Symposium on Visualization (2017 : Bangkok, Thailand)","volume":"2017 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1145/3139295.3139299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36432295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：