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At least 19 records

Performance Data Gathering and Representation from Fixed-Size Statistical Data

The two commonly-used performance data types in the super-computing community, statistics and event traces, are discussed and compared. Statistical data are much more compact but lack the probative power event traces offer. Event traces, on the other hand, are unbounded and can easily fill up the entire file system during program execution. In this paper, we propose an innovative methodology for performance data gathering and representation that offers a middle ground. Two basic ideas are employed: the use of averages to replace recording data for each instance and 'formulae' to represent sequences associated with communication and control flow. The user can trade off tracing overhead, trace data size with data quality incrementally. In other words, the user will be able to limit the amount of trace data collected and, at the same time, carry out some of the analysis event traces offer using space-time views. With the help of a few simple examples, we illustrate the use of these techniques in performance tuning and compare the quality of the traces we collected with event traces. We found that the trace files thus obtained are, indeed, small, bounded and predictable before program execution, and that the quality of the space-time views generated from these statistical data are excellent. Furthermore, experimental results showed that the formulae proposed were able to capture all the sequences associated with 11 of the 15 applications tested. The performance of the formulae can be incrementally improved by allocating more memory at runtime to learn longer sequences.

Yan, Jerry C.

Stability Statistics Data Base System

Stability Statistics Data Base System is written entirely in FORTRAN and does not require use of outside data-management routines. Is used with directly-accessible data base containing simple card images. Four FORTRAN modules implement edit, sort, report-generation, and other functions.

Kwok, G. Y.

Constructing Space-Time Views from Fixed Size Statistical Data: Getting the Best of Both Worlds

Many performance monitoring tools are currently available to the super-computing community. The performance data gathered and analyzed by these tools fall under two categories: statistics and event traces. Statistical data is much more compact but lacks the probative power event traces offer. Event traces, on the other hand, can easily fill up the entire file system during execution such that the instrumented execution may have to be terminated half way through. In this paper, we propose an innovative methodology for performance data gathering and representation that offers a middle ground. The user can trade-off tracing overhead, trace data size vs. data quality incrementally. In other words, the user will be able to limit the amount of trace collected and, at the same time, carry out some of the analysis event traces offer using spacetime views for the entire execution. Two basic ideas are employed: the use of averages to replace recording data for each instance and "formulae" to represent sequences associated with communication and control flow. With the help of a few simple examples, we illustrate the use of these techniques in performance tuning and compare the quality of the traces we collected vs. event traces. We found that the trace files thus obtained are, in deed, small, bounded and predictable before program execution and that the quality of the space time views generated from these statistical data are excellent. Furthermore, experimental results showed that the formulae proposed were able to capture 100% of all the sequences associated with 11 of the 15 applications tested. The performance of the formulae can be incrementally improved by allocating more memory at run-time to learn longer sequences.

Schmidt, Melisa

Constructing Space-Time Views from Fixed Size Statistical Data: Getting the Best of both Worlds

Many performance monitoring tools are currently available to the super-computing community. The performance data gathered and analyzed by these tools fall under two categories: statistics and event traces. Statistical data is much more compact but lacks the probative power event traces offer. Event traces, on the other hand, can easily fill up the entire file system during execution such that the instrumented execution may have to be terminated half way through. In this paper, we propose an innovative methodology for performance data gathering and representation that offers a middle ground. The user can trade-off tracing overhead, trace data size vs. data quality incrementally. In other words, the user will be able to limit the amount of trace collected and, at the same time, carry out some of the analysis event traces offer using space-time views for the entire execution. Two basic ideas arc employed: the use of averages to replace recording data for each instance and formulae to represent sequences associated with communication and control flow. With the help of a few simple examples, we illustrate the use of these techniques in performance tuning and compare the quality of the traces we collected vs. event traces. We found that the trace files thus obtained are, in deed, small, bounded and predictable before program execution and that the quality of the space time views generated from these statistical data are excellent. Furthermore, experimental results showed that the formulae proposed were able to capture 100% of all the sequences associated with 11 of the 15 applications tested. The performance of the formulae can be incrementally improved by allocating more memory at run-time to learn longer sequences.

Schmidt, Melisa

A statistical data analysis and plotting program for cloud microphysics experiments

The analysis software developed for atmospheric cloud microphysics experiments conducted in the laboratory as well as aboard a KC-135 aircraft is described. A group of four programs was developed and implemented on a Hewlett Packard 1000 series F minicomputer running under HP's RTE-IVB operating system. The programs control and read data from a MEMODYNE Model 3765-8BV cassette recorder, format the data on the Hewlett Packard disk subsystem, and generate statistical data (mean, variance, standard deviation) and voltage and engineering unit plots on a user selected plotting device. The programs are written in HP FORTRAN IV and HP ASSEMBLY Language with the graphics software using the HP 1000 Graphics. The supported plotting devices are the HP 2647A graphics terminal, the HP 9872B four color pen plotter, and the HP 2608A matrix line printer.

Jordan, A. J.

Spatial Statistical Data Fusion (SSDF)

As remote sensing for scientific purposes has transitioned from an experimental technology to an operational one, the selection of instruments has become more coordinated, so that the scientific community can exploit complementary measurements. However, tech nological and scientific heterogeneity across devices means that the statistical characteristics of the data they collect are different. The challenge addressed here is how to combine heterogeneous remote sensing data sets in a way that yields optimal statistical estimates of the underlying geophysical field, and provides rigorous uncertainty measures for those estimates. Different remote sensing data sets may have different spatial resolutions, different measurement error biases and variances, and other disparate characteristics. A state-of-the-art spatial statistical model was used to relate the true, but not directly observed, geophysical field to noisy, spatial aggregates observed by remote sensing instruments. The spatial covariances of the true field and the covariances of the true field with the observations were modeled. The observations are spatial averages of the true field values, over pixels, with different measurement noise superimposed. A kriging framework is used to infer optimal (minimum mean squared error and unbiased) estimates of the true field at point locations from pixel-level, noisy observations. A key feature of the spatial statistical model is the spatial mixed effects model that underlies it. The approach models the spatial covariance function of the underlying field using linear combinations of basis functions of fixed size. Approaches based on kriging require the inversion of very large spatial covariance matrices, and this is usually done by making simplifying assumptions about spatial covariance structure that simply do not hold for geophysical variables. In contrast, this method does not require these assumptions, and is also computationally much faster. This method is fundamentally different than other approaches to data fusion for remote sensing data because it is inferential rather than merely descriptive. All approaches combine data in a way that minimizes some specified loss function. Most of these are more or less ad hoc criteria based on what looks good to the eye, or some criteria that relate only to the data at hand.

Braverman, Amy J.

Systems implications of L-band fade data statistics for LEO mobile systems

This paper examines and analyzes research data on the role of foliage attenuation in signal fading between a satellite transmitter and a terrestrial vehicle-mounted receiver. The frequency band of measurement, called L-Band, includes the region 1610.0 to 1626.5 MHz. Data from tests involving various combinations of foliage and vehicle movement conditions clearly show evidence of fast fading (in excess of 0.5 dB per millisecond) and fade depths as great or greater than 16 dB. As a result, the design of a communications link power control that provides the level of accuracy necessary for power sensitive systems could be significantly impacted. Specific examples of this include the communications links that employ Code Division Multiple Access (CDMA) as a modulation technique.

Devieux, Carrie L.

Markovian Statistical Data Analysis of Single-Event Upsets Triggered by High Intensity Neutrons

This paper analyzes data from a single-event upset experiment conducted at the Los Alamos National Laboratory. Statistical tools, based on well-known x(sup 2) hypothesis testing theory, are used to determine if sequences of upsets can be modeled as a homogeneous Markov chain of a specific order. The experiment consisted of radiating a new experimental flight control computer (FCC) with a high intensity neutron beam while the FCC controlled a simulation of a Boeing 737. The analyzed data is a sequence of states that indicates when the FCC is under an upset condition.

Lakdawala, Anushka V.

Computer program for prediction of fuel consumption statistical data for an upper stage three-axes stabilized on-off control system

A FORTRAN coded computer program and method to predict the reaction control fuel consumption statistics for a three axis stabilized rocket vehicle upper stage is described. A Monte Carlo approach is used which is more efficient by using closed form estimates of impulses. The effects of rocket motor thrust misalignment, static unbalance, aerodynamic disturbances, and deviations in trajectory, mass properties and control system characteristics are included. This routine can be applied to many types of on-off reaction controlled vehicles. The pseudorandom number generation and statistical analyses subroutines including the output histograms can be used for other Monte Carlo analyses problems.

Source record

The intercrater plains of Mercury and the Moon: Their nature, origin and role in terrestrial planet evolution. Crater statistical data

The total number of craters within a bin of mean diameter, and the number of craters of each degradational type within that bin are tabulated. Rim-to-rim diameters were measured at arbitrary azimuths for rectified photos or photos taken at vertical incidence (most lunar photos), and at azimuths paralleling a local tangent to the limb for oblique images.

Leake, M. A.

Spatial Statistical Data Fusion for Remote Sensing Applications

Data fusion is the process of combining information from heterogeneous sources into a single composite picture of the relevant process, such that the composite picture is generally more accurate and complete than that derived from any single source alone. Data collection is often incomplete, sparse, and yields incompatible information. Fusion techniques can make optimal use of such data. When investment in data collection is high, fusion gives the best return. Our study uses data from two satellites: (1) Multiangle Imaging SpectroRadiometer (MISR), (2) Moderate Resolution Imaging Spectroradiometer (MODIS).

fusion techniques