Preeti Malakar

An Adaptive Framework for Simulation and Online Remote Visualization of Critical Climate Applications in Resource-constrained Environments

Critical climate applications like cyclone tracking and earthquake modeling require high-performance simulations and online visualization simultaneously performed with the simulations for timely analysis. Remote visualization of critical climate events enables joint analysis by geographically distributed climate science community. However, resource constraints including limited storage and slow networks can limit the effectiveness of such online visualization. In this work, we have developed an adaptive framework that simultaneously performs numerical simulations and online remote visualization of critical climate applications in resource-constrained environments. Our framework considers both application and resource dynamics to adapt various application and resource parameters including simulation resolutions, resource configurations and amount of data for visualization. We have developed two algorithms for processor allocation for simulations and the frequency of data for visualization. We show that our optimization method is able to provide about 30% higher simulation rate and consumes about 25-50% lesser storage space than the greedy approach.

Optimal scheduling of in-situ analysis for large-scale scientific simulations

Todays leadership computing facilities have enabled the execution of transformative simulations at unprecedented scales. However, analyzing the huge amount of output from these simulations remains a challenge. Most analyses of this output is performed in post-processing mode at the end of the simulation. The time to read the output for the analysis can be significantly high due to poor I/O bandwidth, which increases the end-to-end simulation-analysis time. Simulation-time analysis can reduce this end-to-end time. In this work, we present the scheduling of in-situ analysis as a numerical optimization problem to maximize the number of online analyses subject to resource constraints such as I/O bandwidth, network bandwidth, rate of computation and available memory. We demonstrate the effectiveness of our approach through two application case studies on the IBM Blue Gene/Q system.

A divide and conquer strategy for scaling weather simulations with multiple regions of interest

Accurate and timely prediction of weather phenomena, such as hurricanes and flash floods, require high-fidelity compute intensive simulations of multiple finer regions of interest within a coarse simulation domain. Current weather applications execute these nested simulations sequentially using all the available processors, which is sub-optimal due to their sub-linear scalability. In this work, we present a strategy for parallel execution of multiple nested domain simulations based on partitioning the 2-D processor grid into disjoint rectangular regions associated with each domain. We propose a novel combination of performance prediction, processor allocation methods and topology-aware mapping of the regions on torus interconnects. Experiments on IBM Blue Gene systems using WRF show that the proposed strategies result in performance improvement of up to 33% with topology-oblivious mapping and up to additional 7% with topology-aware mapping over the default sequential strategy.

Performance evaluation and optimization of nested high resolution weather simulations

Weather models with high spatial and temporal resolutions are required for accurate prediction of meso-micro scale weather phenomena. Using these models for operational purposes requires forecasts with sufficient lead time, which in turn calls for large computational power. There exists a lot of prior studies on the performance of weather models on single domain simulations with a uniform horizontal resolution. However, there has not been much work on high resolution nested domains that are essential for high-fidelity weather forecasts. In this paper, we focus on improving and analyzing the performance of nested domain simulations using WRF on IBM Blue Gene/P. We demonstrate a significant reduction (up to 29%) in runtime via a combination of compiler optimizations, mapping of process topology to the physical torus topology, overlapping communication with computation, and parallel communications along torus dimensions. We also conduct a detailed performance evaluation using four nested domain configurations to assess the benefits of the different optimizations as well as the scalability of different WRF operations. Our analysis indicates that the choice of nesting configuration is critical for good performance. To aid WRF practitioners in making this choice, we describe a performance modeling approach that can predict the total simulation time in terms of the domain and processor configurations with a very high accuracy (<8%) using a regression-based model learned from empirical timing data.

InSt: An Integrated Steering Framework for Critical Weather Applications

Online remote visualization and steering of critical weather applications like cyclone tracking are essential for effective and timely analysis by geographically distributed climate science community. A steering framework for controlling the high-performance simulations of critical weather events needs to take into account both the steering inputs of the scientists and the criticality needs of the application including minimum progress rate of simulations and continuous visualization of significant events. In this work, we have developed an integrated user-driven and automated steering framework InSt for simulations, online remote visualization, and analysis for critical weather applications. InSt provides the user control over various application parameters including region of interest, resolution of simulation, and frequency of data for visualization. Unlike existing efforts, our framework considers both the steering inputs and the criticality of the application, namely, the minimum progress rate needed for the application, and various resource constraints including storage space and network bandwidth to decide the best possible parameter values for simulations and visualization.

Hierarchical Read---Write Optimizations for Scientific Applications with Multi-variable Structured Datasets

Large-scale scientific applications spend a significant amount of time in reading and writing data. These simulations run on supercomputers which are architected with high-bandwidth, low-latency, and complex topology interconnects. Yet, few efforts exist that fully exploit the interconnect features for I/O. MPI-IO optimizations suffer from significant network contention at large core counts making I/O a critical bottleneck at extreme scales. We propose HieRO, which leverages the fast interconnect and performs hierarchical optimizations for I/O in scientific applications with structured datasets. HieRO performs reads/writes in multiple stages using carefully chosen leader processes who invoke the MPI-IO calls. Additionally, HieRO considers the application’s domain decomposition and access patterns and fully utilizes the on-chip interconnect at each multicore node. We evaluate the efficacy of our optimizations with two scientific applications, WRF and S3D, with I/O access patterns commonly used in a wide gamut of applications. We evaluate our approaches on two supercomputers, the Edison Cray XC30 and the Mira Blue Gene/Q, representing systems with diverse interconnects and parallel filesystems. We demonstrate that algorithmic changes can lead to significant improvements in parallel read/write. HieRO is able to achieve more than

Optimal execution of co-analysis for large-scale molecular dynamics simulations

40\times

Coupling LAMMPS and the vl3 Framework for Co-Visualization of Atomistic Simulations

40× read time improvements for WRF and achieve up to