Collaborative Scientific Data Visualization

We have designed a collaborative scientific visualization package that will aid researchers from distant, diverse locations to work together in developing scientific codes, providing them with a system to analyze their scientific data. We have utilized Java to develop this infrastructure. Two important areas which we have concentrated on developing are 1) a collaborative framework from which the scientific data is interpreted and utilized, and 2) a framework, which is customizable to the suit the needs of a particular task and/or scientific group

On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective

We implement and benchmark parallel I/O methods for the fully-manycore driven particle-in-cell code PIConGPU. Identifying throughput and overall I/O size as a major challenge for applications on today's and future HPC systems, we present a scaling law characterizing performance bottlenecks in state-of-the-art approaches for data reduction. Consequently, we propose, implement and verify multi-threaded data-transformations for the I/O library ADIOS as a feasible way to trade underutilized host-side compute potential on heterogeneous systems for reduced I/O latency.Comment: 15 pages, 5 figures, accepted for DRBSD-1 in conjunction with ISC'1

Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma

A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blob-filaments in real time in fusion plasma. On a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.Comment: 14 pages, 40 figure

Runtime I/O Re-Routing + Throttling on HPC Storage

Abstract Massively parallel storage systems are becoming more and more prevalent on HPC systems due to the emergence of a new generation of data-intensive applications. To achieve the level of I/O throughput and capacity that is demanded by data intensive applications, storage systems typically deploy a large number of storage devices (also known as LUNs or data stores). In doing so, parallel applications are allowed to access storage concurrently, and as a result, the aggregate I/O throughput can be linearly increased with the number of storage devices, reducing the application's end-to-end time. For a production system where storage devices are shared between multiple applications, contention is often a major problem leading to a significant reduction in I/O throughput. In this paper, we describe our efforts to resolve this issue in the context of HPC using a balanced re-routing + throttling approach. The proposed scheme re-routes I/O requests to a less congested storage location in a controlled manner so that write performance is improved while limiting the impact on read

Unraveling Diffusion in Fusion Plasma: A Case Study of In Situ Processing and Particle Sorting

This work starts an in situ processing capability to study a certain diffusion process in magnetic confinement fusion. This diffusion process involves plasma particles that are likely to escape confinement. Such particles carry a significant amount of energy from the burning plasma inside the tokamak to the diverter and damaging the diverter plate. This study requires in situ processing because of the fast changing nature of the particle diffusion process. However, the in situ processing approach is challenging because the amount of data to be retained for the diffusion calculations increases over time, unlike in other in situ processing cases where the amount of data to be processed is constant over time. Here we report our preliminary efforts to control the memory usage while ensuring the necessary analysis tasks are completed in a timely manner. Compared with an earlier naive attempt to directly computing the same diffusion displacements in the simulation code, this in situ version reduces the memory usage from particle information by nearly 60% and computation time by about 20%

A Lightweight I/O Scheme to Facilitate Spatial and Temporal Queries of Scientific Data Analytics

In the era of petascale computing, more scientific applications are being deployed on leadership scale computing platforms to enhance the scientific productivity. Many I/O techniques have been designed to address the growing I/O bottleneck on large-scale systems by handling massive scientific data in a holistic manner. While such techniques have been leveraged in a wide range of applications, they have not been shown as adequate for many mission critical applications, particularly in data post-processing stage. One of the examples is that some scientific applications generate datasets composed of a vast amount of small data elements that are organized along many spatial and temporal dimensions but require sophisticated data analytics on one or more dimensions. Including such dimensional knowledge into data organization can be beneficial to the efficiency of data post-processing, which is often missing from exiting I/O techniques. In this study, we propose a novel I/O scheme named STAR (Spatial and Temporal AggRegation) to enable high performance data queries for scientific analytics. STAR is able to dive into the massive data, identify the spatial and temporal relationships among data variables, and accordingly organize them into an optimized multi-dimensional data structure before storing to the storage. This technique not only facilitates the common access patterns of data analytics, but also further reduces the application turnaround time. In particular, STAR is able to enable efficient data queries along the time dimension, a practice common in scientific analytics but not yet supported by existing I/O techniques. In our case study with a critical climate modeling application GEOS-5, the experimental results on Jaguar supercomputer demonstrate an improvement up to 73 times for the read performance compared to the original I/O method