Performance of the engineering analysis and data system 2 common file system

The Engineering Analysis and Data System (EADS) was used from April 1986 to July 1993 to support large scale scientific and engineering computation (e.g. computational fluid dynamics) at Marshall Space Flight Center. The need for an updated system resulted in a RFP in June 1991, after which a contract was awarded to Cray Grumman. EADS II was installed in February 1993, and by July 1993 most users were migrated. EADS II is a network of heterogeneous computer systems supporting scientific and engineering applications. The Common File System (CFS) is a key component of this system. The CFS provides a seamless, integrated environment to the users of EADS II including both disk and tape storage. UniTree software is used to implement this hierarchical storage management system. The performance of the CFS suffered during the early months of the production system. Several of the performance problems were traced to software bugs which have been corrected. Other problems were associated with hardware. However, the use of NFS in UniTree UCFM software limits the performance of the system. The performance issues related to the CFS have led to a need to develop a greater understanding of the CFS organization. This paper will first describe the EADS II with emphasis on the CFS. Then, a discussion of mass storage systems will be presented, and methods of measuring the performance of the Common File System will be outlined. Finally, areas for further study will be identified and conclusions will be drawn

Performance evaluation of the Engineering Analysis and Data Systems (EADS) 2

The Engineering Analysis and Data System (EADS)II (1) was installed in March 1993 to provide high performance computing for science and engineering at Marshall Space Flight Center (MSFC). EADS II increased the computing capabilities over the existing EADS facility in the areas of throughput and mass storage. EADS II includes a Vector Processor Compute System (VPCS), a Virtual Memory Compute System (CFS), a Common Output System (COS), as well as Image Processing Station, Mini Super Computers, and Intelligent Workstations. These facilities are interconnected by a sophisticated network system. This work considers only the performance of the VPCS and the CFS. The VPCS is a Cray YMP. The CFS is implemented on an RS 6000 using the UniTree Mass Storage System. To better meet the science and engineering computing requirements, EADS II must be monitored, its performance analyzed, and appropriate modifications for performance improvement made. Implementing this approach requires tool(s) to assist in performance monitoring and analysis. In Spring 1994, PerfStat 2.0 was purchased to meet these needs for the VPCS and the CFS. PerfStat(2) is a set of tools that can be used to analyze both historical and real-time performance data. Its flexible design allows significant user customization. The user identifies what data is collected, how it is classified, and how it is displayed for evaluation. Both graphical and tabular displays are supported. The capability of the PerfStat tool was evaluated, appropriate modifications to EADS II to optimize throughput and enhance productivity were suggested and implemented, and the effects of these modifications on the systems performance were observed. In this paper, the PerfStat tool is described, then its use with EADS II is outlined briefly. Next, the evaluation of the VPCS, as well as the modifications made to the system are described. Finally, conclusions are drawn and recommendations for future worked are outlined

A Sparse Algorithm for Computing the DFT Using Its Real Eigenvectors

Direct computation of the discrete Fourier transform (DFT) and its FFT computational algorithms requires multiplication (and addition) of complex numbers. Complex number multiplication requires four real-valued multiplications and two real-valued additions, or three real-valued multiplications and five real-valued additions, as well as the requisite added memory for temporary storage. In this paper, we present a method for computing a DFT via a natively real-valued algorithm that is computationally equivalent to a N=2k-length DFT (where k is a positive integer), and is substantially more efficient for any other length, N. Our method uses the eigenstructure of the DFT, and the fact that sparse, real-valued, eigenvectors can be found and used to advantage. Computation using our method uses only vector dot products and vector-scalar products

Signal Exclusive Adaptive Average Filter for Impulse Noise Suppression

This paper introduces a novel signal exclusive adaptive average (SEAA) filter that offers good image denoising performance in applications characterized by impulsive or impulse-like noise. The proposed algorithm works well in suppressing impulse noise with noise ratios from 3 % up to 60%. We begin by introducing a digital differentiation preprocessing step to quantify the increments in each local neighborhood of the noisy image. A homogeneity level map is then derived by adaptive thresholding and used to designate pixels as noise candidates. The initial selection is refined using a novel connected components labeling algorithm. Finally, the noise is attenuated by estimating the values of the noisy pixels with a linear filter applied exclusively to those neighborhood pixels not labeled as noise candidates. This approach bears similarity to several nonlinear techniques including alpha-trimmed means, selective averaging, and WMMR filters. Simulation results indicate that SEAA is better able to preserve 2-D edge structures from the original image and delivers better performance with less computational overhead as compared to competing nonlinear denoising algorithms. 1

Analysis of Outcomes in Ischemic vs Nonischemic Cardiomyopathy in Patients With Atrial Fibrillation A Report From the GARFIELD-AF Registry

IMPORTANCE Congestive heart failure (CHF) is commonly associated with nonvalvular atrial fibrillation (AF), and their combination may affect treatment strategies and outcomes