Most of the existing scientific visualizations toward interpretive grammar aim to enhance customizability in either the computation stage or the rendering stage or both, while few approaches focus on the data presentation stage. Besides, most of these approaches leverage the existing components from the general-purpose programming languages (GPLs) instead of developing a standalone compiler, which pose a great challenge about learning curves for the domain experts who have limited knowledge about programming. In this paper, we propose IGScript, a novel script-based interaction grammar tool, to help build scientific data presentation animations for communication. We design a dual-space interface and a compiler which converts natural language-like grammar statements or scripts into a data story animation to make an interactive customization on script-driven data presentations, and then develop a code generator (decompiler) to translate the interactive data exploration animations back into script codes to achieve statement parameters. IGScript makes the presentation animations editable, e.g., it allows to cut, copy, paste, append, or even delete some animation clips. We demonstrate the usability, customizability, and flexibility of IGScript by a user study, four case studies conducted by using four types of commonly-used scientific data, and performance evaluations.

We propose an interactive visual analytical approach to exploring and diagnosing the dynamic load balance (data and task partition) process of parallel particle tracing in flow visualization. To understand the complex nature of the parallel processes, it is necessary to integrate the information of the behaviors and patterns of the computing processes, data changes and movements, task status and exchanges, and gain the insight of the relationships among them. In our proposed approach, the data and task behaviors are visualized through a graph with a fine-designed layout, in which node glyphs are dedicated to showing the status of processes and the links represent the data or task transfer between different computation rounds and processes. User interactions are supported to facilitate the exploration of performance analysis. We provide a case study to demonstrate that the proposed approach enables users to identify the bottlenecks during this process, and thus help optimize the related algorithms.

In this work, we present a novel access pattern estimation approach for parallel particle tracing in flow field visualization based on deep neural networks. With strong generalization ability, we develop a Long Short-term Memory (LSTM)-based model, which is capable of learning accurate access patterns with only a few training samples and representing the learned patterns with small storage overhead. Equipped with prediction and prefetching functions driven by the developed model, our parallel particle tracing framework employs CPUs and GPUs together for particle tracing tasks. We demonstrate the accuracy and time efficiency of our approach with various flow visualization applications in three different flow datasets.

We present a novel dynamic load-balancing algorithm based on data repartitioning for parallel particle tracing in flow visualization. Instead of static data assignment, we dynamically repartition the data into blocks and reassign the blocks to processes to balance the workload distribution among the processes. Block repartitioning is performed based on a dynamic workload estimation method that predicts the workload in the flow field on the fly as the input. In our approach, we allow data duplication in the repartitioning, enabling the same data blocks to be assigned to multiple processes. Load balance is achieved by regularly exchanging the blocks (together with the particles in the blocks) among processes according to the output of the data repartitioning. Compared with other load-balancing algorithms, our approach does not need any preprocessing on the raw data and does not require any dedicated process for work scheduling, while it has the capability to balance uneven workload efficiently. Results show improved load balance and high efficiency of our method on tracing particles in both steady and unsteady flow.

We propose a dynamically load-balanced algorithm for parallel particle tracing, which periodically attempts to evenly redistribute particles across processes based on k-d tree decomposition. Each process is assigned with (1) a statically partitioned, axis-aligned data block that partially overlaps with neighboring blocks in other processes and (2) a dynamically determined k-d tree leaf node that bounds the active particles for computation; the bounds of the k-d tree nodes are constrained by the geometries of data blocks. Given a certain degree of overlap between blocks, our method can balance the number of particles as much as possible. Compared with other load-balancing algorithms for parallel particle tracing, the proposed method does not require any preanalysis, does not use any heuristics based on flow features, does not make any assumptions about seed distribution, does not move any data blocks during the run, and does not need any master process for work redistribution. Based on a comprehensive performance study up to 8K processes on a Blue Gene/Q system, the proposed algorithm outperforms baseline approaches in both load balance and scalability on various flow visualization and analysis problems.

We propose a longest common subsequence (LCSS)-based approach to compute the distance among vector field ensembles. By measuring how many common blocks the ensemble pathlines pass through, the LCSS distance defines the similarity among vector field ensembles by counting the number of shared domain data blocks. Compared with traditional methods (e.g., pointwise Euclidean distance or dynamic time warping distance), the proposed approach is robust to outliers, missing data, and the sampling rate of the pathline timesteps. Taking advantage of smaller and reusable intermediate output, visualization based on the proposed LCSS approach reveals temporal trends in the data at low storage cost and avoids tracing pathlines repeatedly. We evaluate our method on both synthetic data and simulation data, demonstrating the robustness of the proposed approach.

We present a novel high-order access dependencies-based model for efficient pathline computation in unsteady flow visualization. By taking longer access sequences into account to model more sophisticated data access patterns in particle tracing, our method greatly improves the accuracy and reliability in data access prediction. In our work, high-order access dependencies are calculated by tracing uniformly seeded pathlines in both forward and backward directions in a preprocessing stage. The effectiveness of our approach is demonstrated through a parallel particle tracing framework with high-order data prefetching. Results show that our method achieves higher data locality and hence improves the efficiency of pathline computation.

In this paper, we present a novel scalable approach for visualizing multivariate unsteady flow data with Lagrangian-based Attribute Space Projection (LASP). The distances between spatial temporal samples are evaluated by their attribute values along the advection directions in the flow field. The massive samples are then projected into 2D screen space for feature identification and selection. A hybrid parallel system, which tightly integrates a MapReduce-style particle tracer with a scalable algorithm for massive projection, is designed to support the large scale analysis. Results show that the proposed methods and system are capable of visualizing features in the unsteady flow, which couples multivariate analysis of vector and scalar attributes with projection.

When computing integral curves and integral surfaces for large-scale unsteady flow fields, a major bottleneck is the widening gap between data access demands and the available bandwidth (both I/O and in-memory). In this work, we explore a novel advection-based scheme to manage flow field data for both efficiency and scalability. The key is to first partition flow field into blocklets (e.g. cells or very fine-grained blocks of cells), and then (pre)fetch and manage blocklets on-demand using a parallel key-value store. The benefits are (1) greatly increasing the scale of local-range analysis (e.g. source-destination queries, streak surface generation) that can fit within any given limit of hardware resources; (2) improving memory and I/O bandwidth-efficiencies as well as the scalability of naive task-parallel particle advection. We demonstrate our method using a prototype system that works on workstation and also in supercomputing environments. Results show significantly reduced I/O overhead compared to accessing raw flow data, and also high scalability on a supercomputer for a variety of applications.