Comparing tensors and identifying their (dis)similar structures is fundamental in understanding the underlying phenomena for complex data. Tensor decomposition methods help analysts extract tensors' essential characteristics and aid in visual analytics for tensors. In contrast to dimensionality reduction (DR) methods designed only for analyzing a matrix (i.e., second-order tensor), existing tensor decomposition methods do not support flexible comparative analysis. To address this analysis limitation, we introduce a new tensor decomposition method, named tensor unified linear comparative analysis (TULCA), by extending its DR counterpart, ULCA, for tensor analysis. TULCA integrates discriminant analysis and contrastive learning schemes for tensor decomposition, enabling flexible comparison of tensors. We also introduce an effective method to visualize a core tensor extracted from TULCA into a set of 2D visualizations. We integrate TULCA's functionalities into a visual analytics interface to support analysts in interpreting and refining the TULCA results. We demonstrate the efficacy of TULCA and the visual analytics interface with computational evaluations and two case studies, including an analysis of log data collected from a supercomputer.

One advancing machine-learning-based analysis approach for multivariate time-series data is representing data as a third-order tensor and then applying dimensionality reduction (DR) methods. In this work, we introduce a visual analytics method that employs multiple interactive DR methods to support both extraction and interpretation of latent patterns of multivariate time-series data. Our method first allows analysts to select an analysis focus from three axes: instance, variable, and time axes. Then, the method applies a multi-step DR method to produce a 2D scatterplot that depicts latent patterns of the selected axis’s elements (e.g., time points). Afterward, the analysts interactively investigate data groups that appeared in the plot with a DR method designed for comparative analysis. The method can be further applied iteratively to perform more precise and detailed analyses. We implement a prototype system and demonstrate the effectiveness of our method by analyzing supercomputer log data.

With the recent developments in computational science and HPC technology, large-scale numerical simulations have become common in various scientific and technological fields. The output volume data from these simulations have also become larger and more complex, creating a problem for the time-consuming input/output to/from the HPC storage system. To solve this problem, in-situ visualization has been used. However, the output data for posterior analysis is usually a large set of image data obtained from the visualization, and there is a lack of interactivity compared to the conventional analysis, which loads the volume data from the storage, after the simulation, and executes interactive visual exploration. To compensate for these problems, in-situ visualization often places multiple viewpoints in the simulation space and generates images from all of them. However, this may result in a huge number of images, and as a result, this can require time and effort to locate important visualization images that can provide clues to obtain knowledge during the analysis. To solve this problem, this study estimates the regions where important changes occur in the simulation, based on information entropy calculated from the visualization images, and generates a sequence of animated images focusing on these regions. In-situ visualization has widely been recognized as an effective approach for analyzing large-scale simulation outputs from modern HPC systems by reducing the inherent I/O bottleneck problem. However, batch-based in-situ visualization, such as the image- and video-based approaches, can produce large amounts of rendering results for the subsequent offline visual analysis. Therefore, this can make it difficult to gain rapid insight into the simulation results during post-hoc visual analysis. To minimize this problem, we have worked on a smart visualization approach focusing on extracting a set of images that may facilitate the rapid understanding of the underlying simulated phenomena as an alternative to accelerate the process of obtaining scientific knowledge. In this work, we present a method for automatically adjusting the camera focus point and zoom level during in-situ visualization in an attempt to obtain the most suitable rendering images for facilitating visual analysis. We integrated the proposed method with the existing in-situ smooth camera path estimation framework, for evaluation purposes, and used two CFD simulation codes and two HPC systems (x86-based server system and Arm-based Fugaku supercomputer) for the evaluations. We obtained encouraging results from the preliminary evaluations, and we are planning further improvements by working closely with domain expert collaborators.

Energy efficiency has become a serious concern when running applications on HPC systems. Although these systems were designed to mainly run simulation codes as fast as possible, due to the ever-increasing size of the simulation outputs, the in situ visualization has gained increasing attention. In situ visualization uses the same HPC system to execute a part or even the entire visualization processing, and there are currently a variety of tools and libraries, that facilitate domain scientists to integrate them with their simulation codes. Among different approaches, image- and video-based in situ visualization has been widely adopted as an effective approach for the subsequent offline visual analysis. In this approach, a large number of renderings are required at every visualization time step and can consume a considerable computational resource. Fugaku adopted PowerAPI which enables the users to set the power mode for their jobs. However, simulation and visualization codes may have different processing behaviors requiring different power settings for obtaining the most energy-efficient runnings. In this work, we tried to shed light on the energy efficiency of the visualization portion that was not considered before. We investigated the computational cost and energy consumption of some rendering techniques by using the PowerAPI and KVS (Kyoto Visualization System) on the Fugaku, and hope that the obtained findings will be useful for potential users looking to run in situ visualization on the Fugaku and other PowerAPI-enabled HPC systems.

Many real-world applications involve analyzing time-dependent phenomena, which are intrinsically functional, consisting of curves varying over a continuum (e.g., time). When analyzing continuous data, functional data analysis (FDA) provides substantial benefits, such as the ability to study the derivatives and to restrict the ordering of data. However, continuous data inherently has infinite dimensions, and for a long time series, FDA methods often suffer from high computational costs. The analysis problem becomes even more challenging when updating the FDA results for continuously arriving data. In this paper, we present a visual analytics approach for monitoring and reviewing time series data streamed from a hardware system with a focus on identifying outliers by using FDA. To perform FDA while addressing the computational problem, we introduce new incremental and progressive algorithms that promptly generate the magnitude-shape (MS) plot, which conveys both the functional magnitude and shape outlyingness of time series data. In addition, by using an MS plot in conjunction with an FDA version of principal component analysis, we enhance the analyst's ability to investigate the visually-identified outliers. We illustrate the effectiveness of our approach with two use scenarios using real-world datasets. The resulting tool is evaluated by industry experts using real-world streaming datasets.

Data-driven problem solving in many real-world applications involves analysis of time-dependent multivariate data, for which dimensionality reduction (DR) methods are often used to uncover the intrinsic structure and features of the data. However, DR is usually applied to a subset of data that is either single-time-point multivariate or univariate time-series, resulting in the need to manually examine and correlate the DR results out of different data subsets. When the number of dimensions is large either in terms of the number of time points or attributes, this manual task becomes too tedious and infeasible. In this paper, we present MulTiDR, a new DR framework that enables processing of time-dependent multivariate data as a whole to provide a comprehensive overview of the data. With the framework, we employ DR in two steps. When treating the instances, time points, and attributes of the data as a 3D array, the first DR step reduces the three axes of the array to two, and the second DR step visualizes the data in a lower-dimensional space. In addition, by coupling with a contrastive learning method and interactive visualizations, our framework enhances analysts' ability to interpret DR results. We demonstrate the effectiveness of our framework with four case studies using real-world datasets.

The hot water cooling technique has been widely accepted as one of the standard techniques to improve the energy efficiency for the HPC and Data Centers. However, the higher operating temperature may impact the CPU power consumption due to the leakage current. Moreover, it may degrade the computational performance due to the DVFS mechanism activated to maintain the power and temperature within the TDP limit. In that sense, to fairly evaluate the efficiency of the hot water cooling technique, it becomes important to take into consideration not only the energy reduction on the HPC facility side (cooling system) but also the impact on the power consumption and the performance degradation on the HPC system side. In this paper, we utilized the Oakforest-PACS system and its facility, jointly administrated by University of Tsukuba and The University of Tokyo, in order to execute a quantitative and systematic analysis on the impact of the cooling water temperature onto the HPC system and its facility. For this purpose, we utilized lower (9 °C) and higher (18 °C) cooling water temperature other than the regular operational temperature (12 °C). Contrary to the gain in the energy consumption, on the HPC facility side, when using higher cooling water temperature, we observed an increase in the number of nodes suffering from performance degradation on the HPC system side. As a result, it can directly increase the probability of including low-performance nodes on multiple node jobs, and thus affecting their overall performance, especially during barrier synchronizations.

Liquid cooling has been rapidly becoming the de facto standard cooling method for high performance/density racks of modern HPC Data Centers. Semiconductor technology development has made it possible to operate processors (CPU, GPU, and Accelerators) at more higher temperature ranges without compromising the reliability and static power consumption, and these contributed in part to increase the attention over the “hot-water cooling” as one of the main approaches for energy efficient system design. The 2011 ASHRAE Class W4 allows water supply temperature up to 45°C, and even higher temperature for the Class W5. A clear understanding of the temperature impact on the processors would be valuable for assisting the HPC operational staffs for their strategic planning and decision makings. In this short paper, we present our experience in using a simple and cost effective bench testing environment for analyzing the operational behavior of the processors in such high temperature conditions. Although it is far from ideal, since we are not using the same building blocks of the current running HPC system, we consider a valuable alternative for observing the operational behavior of the processors in such temperature environment, and may obtain supportive evidence for assisting strategic planning and decision makings.

In order to meet the increasing performance demand of applications, the amount of cores in a single chip package has been increasing. However, the heat has been rising at a higher scale, which accelerates the aging process in modern processors. Therefore, wisely balancing the use of resources is important to extend its longevity. Frequency performance stagnates after a certain amount of concurrent threads starts executing. In such cases, the only result is a temperature rise that directly influences the aging process, reducing the processor lifetime. This unbalance between threads can be originated from many factors, which includes the way threads communicate and synchronize. Considering that those characteristics are related to the Parallel Programming Interface (PPI) used to parallelize the application, this work proposes to evaluate three widely used PPIs executing on an embedded multicore. We show that, depending on the characteristic of the application, by only switching from one PPI to another, it is possible to reduce the effects of aging. For that, we have developed a model based on the Arrhenius equation. We show that OpenMP has a lower impact on the processor aging for memory-bound applications: up to 38% and 68% lower than PThreads and MPI, respectively. On the other hand, PThreads presents the lowest impact on the processor aging for CPU-bound applications.

In this paper, we present a study on the available open-source software (OSS) for large-scale data visualization on the SPARC64fx based HPC systems, such as the K computer and also the Fujitsu PRIMEHPC FX family of supercomputers (FX10 and FX100), which are commonly available throughout Japan. It is widely known that these HPC systems have been generating a vast amount of simulation results in a wide range of science and engineering fields. However, there was no much information regarding the large-scale data visualization software and approaches in such HPC infrastructure. In this work, we focused on the visualization approaches where the HPC hardware resources are directly used for the visualization processing, which can be helpful to minimize the large data transfer issue for the visualization and analysis purposes. This study includes both OpenGL (Open Graphics Library) and non-OpenGL based visualization approaches, and also the availability of the GLSL (OpenGL Shading Language) handling functionalities. Although it is a short survey focusing only on the post-processing issue, we expect that this study can be useful and helpful for the current and future potential users of the SPARC64fx CPU based HPC systems, which are still in active use throughout Japan.

An increasing volume of data is produced by computational science applications executing on flagship-class supercomputers, such as the K computer. Most of these huge datasets would later pass through post-processing for visualization and analysis in order to derive meaningful information. Particular characteristics of the computing environment, application, and the dataset itself, can make efficiently exploring the performance capabilities of large-scale storage systems supporting these supercomputer a challenging task. This paper presents a characterization of the I/O and storage activity of jobs executed on the K computer focusing on post-processing purposes, based upon nine months of production operation recorded. Results demonstrate the intensive data demand of K computer applications, both in terms of volume of file I/O carried out during job execution, amount of data staged-in and staged-out, and number of files produced per job. These aspects shed light on challenges and opportunities for specialized data management libraries for posthoc data visualization and analysis.

Due to the continuous increase in the scale of numerical simulations, research on visualization has shifted to in-situ/in-transit approaches. The interactivity of largescale visualization has also become increasingly important. In order to observe largescale visualization data in detail, high-resolution displays, such as those with 8K or 16K resolutions, give an opportunity to inspire new discovery. With the commoditization of high-resolution displays, tiled display walls (TDWs) have facilitated their use for the collaborative research, where a large screen size is required for sharing the content among multiple sites. In this paper, we propose a remote collaboration method that utilizes a TDW driver (ChOWDER), which enables content sharing among multiple sites even with different display configurations, and a visualization application (HIVE) for dynamic content sharing of interactive visualization results.

In the present paper, we introduce a novel point-based volume rendering technique based on particle generation from user-specified transfer function. In the proposed technique, a set of tiny particles is generated from a given 3D scalar field. This particle generation process is based on a user-specified transfer function and rejection method. These particles are then projected onto the image plane to generate the final image. The main characteristic of the proposed technique is that the particle projection order is independent and unfixed because the transparency values of the particles are not taken into account. Therefore, only the depth-order comparison between the particles is required during the projection stage, which can greatly facilitate the distributed processing. When the quantity of projected particles is small, for instance, a maximum of one per pixel area, it becomes difficult to achieve semi-transparency, which is the main characteristic of volume rendering. To overcome this problem, sub-pixel processing is applied in order to allow the projection of multiple particles onto each of the pixel areas. The final pixel value is then obtained by averaging the contribution from each of these projected particles. The use of the Metropolis method for particle generation is also investigated as an alternative method for further improving the image quality