access: v.22, no.01, Spring 2009

published or submitted for publicatio

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex

CFD Vision 2030 Study: A Path to Revolutionary Computational Aerosciences

This report documents the results of a study to address the long range, strategic planning required by NASA's Revolutionary Computational Aerosciences (RCA) program in the area of computational fluid dynamics (CFD), including future software and hardware requirements for High Performance Computing (HPC). Specifically, the "Vision 2030" CFD study is to provide a knowledge-based forecast of the future computational capabilities required for turbulent, transitional, and reacting flow simulations across a broad Mach number regime, and to lay the foundation for the development of a future framework and/or environment where physics-based, accurate predictions of complex turbulent flows, including flow separation, can be accomplished routinely and efficiently in cooperation with other physics-based simulations to enable multi-physics analysis and design. Specific technical requirements from the aerospace industrial and scientific communities were obtained to determine critical capability gaps, anticipated technical challenges, and impediments to achieving the target CFD capability in 2030. A preliminary development plan and roadmap were created to help focus investments in technology development to help achieve the CFD vision in 2030

Failure avoidance techniques for HPC systems based on failure prediction

A increasingly larger percentage of computing capacity in today's large high-performance computing systems is wasted due to failures and recoveries. Moreover, it is expected that high performance computing will reach exascale within a decade, decreasing the mean time between failures to one day or even a few hours, making fault tolerance a major challenge for the HPC community. As a consequence, current research is focusing on providing fault tolerance strategies that aim to minimize fault's effects on applications. By far, the most popular and used techniques from this field are rollback-recovery protocols. However, existing rollback-recovery techniques have severe scalability limitations and without further optimizations the use of current protocols is put under serious questions for future exascale systems. A way of reducing the overhead induced by these strategies is by combining them with failure avoidance methods. Failure avoidance is based on a prediction model that detects fault occurrences ahead of time and allows preventive measures to be taken, such as task migration or checkpointing the application before failure. The same methodology can be generalized and applied to anomaly avoidance, where anomaly can mean anything from system failures to performance degradation at the application level. For this, monitoring systems require a reliable prediction system to give information on when failures will occur and at what location. Thus far, research in this field used ideal predictors that do not have any implementation in real HPC systems. This thesis focuses on analyzing and characterizing anomaly patterns at both the application and system levels and on offering solutions to prevent anomalies from affecting applications running in the system. Currently, there is no good characterization of normal behavior for system state data or how different components react to failures within HPC systems. For example, in case a node experiences a network failure and is incapable of generating log messages, the failure is announced in the log files by a lack of generated messages. Conversely, some component failures may cause logging a large numbers of notifications. For example, memory failures can result in a single faulty component generating hundreds or thousands of messages in less than a day. It is important to be able to capture the behavior of each event type and understand what is the normal behavior and how each failure type affects it. This idea represents the building block of a novel way of characterizing the state of the system in time by analyzing the properties of each event described in different system metrics, considering its own trend and behavior. The method introduces the integration between signal processing concepts and data mining techniques in the context of analysis for large-scale systems. By shaping the normal and faulty behavior of each event and of the whole system, appropriate models and methods for descriptive and forecasting purposes are proposed. After having an accurate overview of the whole system, the thesis analyzes how the prediction model impacts current fault tolerance techniques and in the end integrates it into a fault avoidance solution. This hybrid protocol optimizes the overhead that current fault tolerance strategies impose on applications and presents a viable solution for future large-scale systems

Simulating Large Scale Parallel Applications Using Statistical Models for Sequential Execution Blocks

Predicting sequential execution blocks of a large scale parallel application is an essential part of accurate prediction of the overall performance of the application. When simulating a future machine that is not yet fabricated, or a prototype system only available at a small scale, it becomes a significant challenge. Using hardware simulators may not be feasible due to excessively slowed down execution times and insufficient resources. These challenging issues become increasingly difficult in proportion to scale of the simulation. In this paper, we propose an approach based on statistical models to accurately predict the performance of the sequential execution blocks that comprise a parallel application. We de-ployed these techniques in a trace-driven simulation framework to capture both the detailed behavior of the application as well as the overall predicted performance. The technique is validated using both synthetic benchmarks and the NAMD application. Index Terms—parallel simulator, performance prediction, trace-driven, machine learning, statistical model I

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

Resiliency of high-performance computing systems: A fault-injection-based characterization of the high-speed network in the blue waters testbed

Supercomputers have played an essential role in the progress of science and engineering research. As the high-performance computing (HPC) community moves towards the next generation of HPC computing, it faces several challenges, one of which is reliability of HPC systems. Error rates are expected to significantly increase on exascale systems to the point where traditional application-level checkpointing may no longer be a viable fault tolerance mechanism. This poses serious ramifications for a system's ability to guarantee reliability and availability of its resources. It is becoming increasingly important to understand fault-to-failure propagation and to identify key areas of instrumentation in HPC systems for avoidance, detection, diagnosis, mitigation, and recovery of faults. This thesis presents a software-implemented, prototype-based fault injection tool called HPCArrow and a fault injection methodology as a means to investigate and evaluate HPC application and system resiliency. We demonstrate HPCArrow's capabilities through four fault injection campaigns on a Cray XE/XK hybrid testbed, covering single injections, time-varying or delayed injections, and injections during recovery. These injections emulate failures on network and compute components. The results of these campaigns provide insight into application-level and system-level resiliencies. Across various HPC application frameworks, there are notable deficiencies in fault tolerance. Our experiments also revealed a failure phenomenon that was previously unobserved in field data: application hangs, in which forward progress is not made, but jobs are not terminated until the maximum allowed time has elapsed. At the system level, failover procedures prove highly robust on small-scale systems, able to handle both single and multiple faults in the network