Combining in-situ and in-transit processing to enable extreme-scale scientific analysis

pre-printWith the onset of extreme-scale computing, I/O constraints make it increasingly difficult for scientists to save a sufficient amount of raw simulation data to persistent storage. One potential solution is to change the data analysis pipeline from a post-process centric to a concurrent approach based on either in-situ or in-transit processing. In this context computations are considered in-situ if they utilize the primary compute resources, while in-transit processing refers to offloading computations to a set of secondary resources using asynchronous data transfers. In this paper we explore the design and implementation of three common analysis techniques typically performed on large-scale scientific simulations: topological analysis, descriptive statistics, and visualization. We summarize algorithmic developments, describe a resource scheduling system to coordinate the execution of various analysis workflows, and discuss our implementation using the DataSpaces and ADIOS frameworks that support efficient data movement between in-situ and in-transit computations. We demonstrate the efficiency of our lightweight, flexible framework by deploying it on the Jaguar XK6 to analyze data generated by S3D, a massively parallel turbulent combustion code. Our framework allows scientists dealing with the data deluge at extreme scale to perform analyses at increased temporal resolutions, mitigate I/O costs, and significantly improve the time to insight

The Grand Challenge of Managing the Petascale Facility.

This report is the result of a study of networks and how they may need to evolve to support petascale leadership computing and science. As Dr. Ray Orbach, director of the Department of Energy's Office of Science, says in the spring 2006 issue of SciDAC Review, 'One remarkable example of growth in unexpected directions has been in high-end computation'. In the same article Dr. Michael Strayer states, 'Moore's law suggests that before the end of the next cycle of SciDAC, we shall see petaflop computers'. Given the Office of Science's strong leadership and support for petascale computing and facilities, we should expect to see petaflop computers in operation in support of science before the end of the decade, and DOE/SC Advanced Scientific Computing Research programs are focused on making this a reality. This study took its lead from this strong focus on petascale computing and the networks required to support such facilities, but it grew to include almost all aspects of the DOE/SC petascale computational and experimental science facilities, all of which will face daunting challenges in managing and analyzing the voluminous amounts of data expected. In addition, trends indicate the increased coupling of unique experimental facilities with computational facilities, along with the integration of multidisciplinary datasets and high-end computing with data-intensive computing; and we can expect these trends to continue at the petascale level and beyond. Coupled with recent technology trends, they clearly indicate the need for including capability petascale storage, networks, and experiments, as well as collaboration tools and programming environments, as integral components of the Office of Science's petascale capability metafacility. The objective of this report is to recommend a new cross-cutting program to support the management of petascale science and infrastructure. The appendices of the report document current and projected DOE computation facilities, science trends, and technology trends, whose combined impact can affect the manageability and stewardship of DOE's petascale facilities. This report is not meant to be all-inclusive. Rather, the facilities, science projects, and research topics presented are to be considered examples to clarify a point

Scalable Observation, Analysis, and Tuning for Parallel Portability in HPC

It is desirable for general productivity that high-performance computing applications be portable to new architectures, or can be optimized for new workflows and input types, without the need for costly code interventions or algorithmic re-writes. Parallel portability programming models provide the potential for high performance and productivity, however they come with a multitude of runtime parameters that can have significant impact on execution performance. Selecting the optimal set of parameters, so that HPC applications perform well in different system environments and on different input data sets, is not trivial.This dissertation maps out a vision for addressing this parallel portability challenge, and then demonstrates this plan through an effective combination of observability, analysis, and in situ machine learning techniques. A platform for general-purpose observation in HPC contexts is investigated, along with support for its use in human-in-the-loop performance understanding and analysis. The dissertation culminates in a demonstration of lessons learned in order to provide automated tuning of HPC applications utilizing parallel portability frameworks

Doctor of Philosophy

dissertationRay tracing presents an efficient rendering algorithm for scientific visualization using common visualization tools and scales with increasingly large geometry counts while allowing for accurate physically-based visualization and analysis, which enables enhanced rendering and new visualization techniques. Interactivity is of great importance for data exploration and analysis in order to gain insight into large-scale data. Increasingly large data sizes are pushing the limits of brute-force rasterization algorithms present in the most widely-used visualization software. Interactive ray tracing presents an alternative rendering solution which scales well on multicore shared memory machines and multinode distributed systems while scaling with increasing geometry counts through logarithmic acceleration structure traversals. Ray tracing within existing tools also provides enhanced rendering options over current implementations, giving users additional insight from better depth cues while also enabling publication-quality rendering and new models of visualization such as replicating photographic visualization techniques

A Survey of GPU-Based Large-Scale Volume Visualization

This survey gives an overview of the current state of the art in GPU techniques for interactive large-scale volume visualization. Modern techniques in this field have brought about a sea change in how interactive visualization and analysis of giga-, tera-, and petabytes of volume data can be enabled on GPUs. In addition to combining the parallel processing power of GPUs with out-of-core methods and data streaming, a major enabler for interactivity is making both the computational and the visualization effort proportional to the amount and resolution of data that is actually visible on screen, i.e., “output-sensitive” algorithms and system designs. This leads to recent outputsensitive approaches that are “ray-guided,” “visualization-driven,” or “display-aware.” In this survey, we focus on these characteristics and propose a new categorization of GPU-based large-scale volume visualization techniques based on the notions of actual output-resolution visibility and the current working set of volume bricks—the current subset of data that is minimally required to produce an output image of the desired display resolution. For our purposes here, we view parallel (distributed) visualization using clusters as an orthogonal set of techniques that we do not discuss in detail but that can be used in conjunction with what we discuss in this survey.Engineering and Applied Science

A Fortran Kernel Generation Framework for Scientific Legacy Code

Quality assurance procedure is very important for software development. The complexity of modules and structure in software impedes the testing procedure and further development. For complex and poorly designed scientific software, module developers and software testers need to put a lot of extra efforts to monitor not related modules\u27 impacts and to test the whole system\u27s constraints. In addition, widely used benchmarks cannot help programmers with accurate and program specific system performance evaluation. In this situation, the generated kernels could provide considerable insight into better performance tuning. Therefore, in order to greatly improve the productivity of various scientific software engineering tasks such as performance tuning, debugging, and verification of simulation results, we developed an automatic compute kernel extraction prototype platform for complex legacy scientific code. In addition, considering that scientific research and experiment require long-term simulation procedure and the huge size of data transfer, we apply message passing based parallelization and I/O behavior optimization to highly improve the performance of the kernel extractor framework and then use profiling tools to give guidance for parallel distribution. Abnormal event detection is another important aspect for scientific research; dealing with huge observational datasets combined with simulation results it becomes not only essential but also extremely difficult. In this dissertation, for the sake of detecting high frequency event and low frequency events, we reconfigured this framework equipped with in-situ data transfer infrastructure. Through the method of combining signal processing data preprocess(decimation) with machine learning detection model to train the stream data, our framework can significantly decrease the amount of transferred data demand for concurrent data analysis (between distributed computing CPU/GPU nodes). Finally, the dissertation presents the implementation of the framework and a case study of the ACME Land Model (ALM) for demonstration. It turns out that the generated compute kernel with lower cost can be used in performance tuning experiments and quality assurance, which include debugging legacy code, verification of simulation results through single point and multiple points of variables tracking, collaborating with compiler vendors, and generating custom benchmark tests

Rethinking the Delivery Architecture of Data-Intensive Visualization

The web has transformed the way people create and consume information. However, data-intensive science applications have rarely been able to take full benefits of the web ecosystem so far. Analysis and visualization have remained close to large datasets on large servers and desktops, because of the vast resources that data-intensive applications require. This hampers the accessibility and on-demand availability of data-intensive science. In this work, I propose a novel architecture for the delivery of interactive, data-intensive visualization to the web ecosystem. The proposed architecture, codenamed Fabric, follows the idea of keeping the server-side oblivious of application logic as a set of scalable microservices that 1) manage data and 2) compute data products. Disconnected from application logic, the services allow interactive data-intensive visualization be simultaneously accessible to many users. Meanwhile, the client-side of this architecture perceives visualization applications as an interaction-in image-out black box with the sole responsibility of keeping track of application state and mapping interactions into well-defined and structured visualization requests. Fabric essentially provides a separation of concern that decouples the otherwise tightly coupled client and server seen in traditional data applications. Initial results show that as a result of this, Fabric enables high scalability of audience, scientific reproducibility, and improves control and protection of data products

XSEDE: eXtreme Science and Engineering Discovery Environment Third Quarter 2012 Report

The Extreme Science and Engineering Discovery Environment (XSEDE) is the most advanced, powerful, and robust collection of integrated digital resources and services in the world. It is an integrated cyberinfrastructure ecosystem with singular interfaces for allocations, support, and other key services that researchers can use to interactively share computing resources, data, and expertise.This a report of project activities and highlights from the third quarter of 2012.National Science Foundation, OCI-105357