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24 records · Page 2

MMPP Traffic Generator for the Testing of the SCAR 2 Fast Packet Switch

A prototype MWP Traffic Generator (TG) has been designed for testing of the COMSAT-supplied SCAR II Fast Packet Switch. By generating packets distributed according to a Markov-Modulated Poisson Process (MMPP) model. it allows the assessment of the switch performance under traffic conditions that are more realistic than could be generated using the COMSAT-supplied Traffic Generator Module. The MMPP model is widely believed to model accurately real-world superimposed voice and data communications traffic. The TG was designed to be as much as possible of a "drop-in" replacement for the COMSAT Traffic Generator Module. The latter fit on two Altera EPM7256EGC 192-pin CPLDs and produced traffic for one switch input port. No board changes are necessary because it has been partitioned to use the existing board traces. The TG, consisting of parts "TGDATPROC" and "TGRAMCTL" must merely be reprogrammed into the Altera devices of the same name. However, the 040 controller software must be modified to provide TG initialization data. This data will be given in Section II.

Chren, William A., Jr.↗

Estimating gravity wave parameters from oblique high-frequency backscatter: Modeling and analysis

A new technique for estimating electron density perturbation amplitudes of traveling ionospheric disturbances (TIDs), using HF radar data, is presented. TIDs are observed in HF radar data as enhancements of the ground-scattered power which propagate through the radar's field of view. These TIDs are the ionospheric manifestation of atmospheric acoustic-gravity waves. TID electron density perturbation amplitudes were estimated by simulating the radar returns, using HF ray tracing through a model ionosphere perturbed by a model gravity wave. The simulation determined the return power in the ground-scattered portion of the signal as a function of range, and this was compared to HF radar data from the Goose Bay HF radar at a time when evidence of gravity waves was present in the data. By varying the amplitude of the electron density perturbation in the model it was possible to estimate the perturbation of the actual wave. It was found that the perturbations that are observed by the Goose Bay HF radar are of the order of 20% to 35%. It was also found that the number of observable power enhancements, and the relative amplitudes of these enhancements, depended on the vertical thickness of the gravity wave's source region. From the simulations and observations it was estimated that the source region for the case presented here was approximately 20 km thick. In addition, the energy in the wave packet was calculated and compared to an estimate of the available energy in the source region. It was found that the wave energy was about 0.2% of the estimated available source region energy.

Bristow, W. A.↗

A trace-driven analysis of name and attribute caching in a distributed system

This paper presents the results of simulating file name and attribute caching on client machines in a distributed file system. The simulation used trace data gathered on a network of about 40 workstations. Caching was found to be advantageous: a cache on each client containing just 10 directories had a 91 percent hit rate on name look ups. Entry-based name caches (holding individual directory entries) had poorer performance for several reasons, resulting in a maximum hit rate of about 83 percent. File attribute caching obtained a 90 percent hit rate with a cache on each machine of the attributes for 30 files. The simulations show that maintaining cache consistency between machines is not a significant problem; only 1 in 400 name component look ups required invalidation of a remotely cached entry. Process migration to remote machines had little effect on caching. Caching was less successful in heavily shared and modified directories such as /tmp, but there weren't enough references to /tmp overall to affect the results significantly. We estimate that adding name and attribute caching to the Sprite operating system could reduce server load by 36 percent and the number of network packets by 30 percent.

Shirriff, Ken W.↗

Ion Mobility Spectrometry with High Ion Utilization Efficiency Using Traveling Wave-Based Structures for Lossless Ion Manipulations

Ion packets introduced from gates, ion funnel traps, and other conventional ion injection mechanisms produce ion pulse widths typically around a few microseconds or less for ion mobility spectrometry (IMS)-based separations on the order of 100 milliseconds. When such ion injection techniques are coupled with ultralong path length traveling wave (TW)-based IMS separations (i.e., on the order of seconds) using structures for lossless ion manipulations (SLIMs), typically very low ion utilization efficiency is achieved for continuous ion sources [e.g., electrospray ionization (ESI)]. Even with the ability to trap and accumulate much larger populations of ions than being conventionally feasible over longer time periods in SLIM devices, the subsequent long separations lead to overall low ion utilization. In this study, we report the use of a highly flexible SLIM arrangement, enabling concurrent ion accumulation and separation and achieving near-complete ion utilization with ESI. We characterize the ion accumulation process in SLIM, demonstrate >98% ion utilization, and show both increased signal intensities and measurement throughput. This approach is envisioned to have broad utility to applications, for example, involving the fast detection of trace chemical species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Automated Instrumentation, Monitoring and Visualization of PVM Programs Using AIMS

We present views and analysis of the execution of several PVM codes for Computational Fluid Dynamics on a network of Sparcstations, including (a) NAS Parallel benchmarks CG and MG (White, Alund and Sunderam 1993); (b) a multi-partitioning algorithm for NAS Parallel Benchmark SP (Wijngaart 1993); and (c) an overset grid flowsolver (Smith 1993). These views and analysis were obtained using our Automated Instrumentation and Monitoring System (AIMS) version 3.0, a toolkit for debugging the performance of PVM programs. We will describe the architecture, operation and application of AIMS. The AIMS toolkit contains (a) Xinstrument, which can automatically instrument various computational and communication constructs in message-passing parallel programs; (b) Monitor, a library of run-time trace-collection routines; (c) VK (Visual Kernel), an execution-animation tool with source-code clickback; and (d) Tally, a tool for statistical analysis of execution profiles. Currently, Xinstrument can handle C and Fortran77 programs using PVM 3.2.x; Monitor has been implemented and tested on Sun 4 systems running SunOS 4.1.2; and VK uses X11R5 and Motif 1.2. Data and views obtained using AIMS clearly illustrate several characteristic features of executing parallel programs on networked workstations: (a) the impact of long message latencies; (b) the impact of multiprogramming overheads and associated load imbalance; (c) cache and virtual-memory effects; and (4significant skews between workstation clocks. Interestingly, AIMS can compensate for constant skew (zero drift) by calibrating the skew between a parent and its spawned children. In addition, AIMS' skew-compensation algorithm can adjust timestamps in a way that eliminates physically impossible communications (e.g., messages going backwards in time). Our current efforts are directed toward creating new views to explain the observed performance of PVM programs. Some of the features planned for the near future include: (a) ConfigView, showing the physical topology of the virtual machine, inferred using specially formatted IP (Internet Protocol) packets; and (b) LoadView, synchronous animation of PVM-program execution and resource-utilization patterns.

Mehra, Pankaj↗

Automated Instrumentation, Monitoring and Visualization of PVM Programs Using AIMS

We present views and analysis of the execution of several PVM (Parallel Virtual Machine) codes for Computational Fluid Dynamics on a networks of Sparcstations, including: (1) NAS Parallel Benchmarks CG and MG; (2) a multi-partitioning algorithm for NAS Parallel Benchmark SP; and (3) an overset grid flowsolver. These views and analysis were obtained using our Automated Instrumentation and Monitoring System (AIMS) version 3.0, a toolkit for debugging the performance of PVM programs. We will describe the architecture, operation and application of AIMS. The AIMS toolkit contains: (1) Xinstrument, which can automatically instrument various computational and communication constructs in message-passing parallel programs; (2) Monitor, a library of runtime trace-collection routines; (3) VK (Visual Kernel), an execution-animation tool with source-code clickback; and (4) Tally, a tool for statistical analysis of execution profiles. Currently, Xinstrument can handle C and Fortran 77 programs using PVM 3.2.x; Monitor has been implemented and tested on Sun 4 systems running SunOS 4.1.2; and VK uses XIIR5 and Motif 1.2. Data and views obtained using AIMS clearly illustrate several characteristic features of executing parallel programs on networked workstations: (1) the impact of long message latencies; (2) the impact of multiprogramming overheads and associated load imbalance; (3) cache and virtual-memory effects; and (4) significant skews between workstation clocks. Interestingly, AIMS can compensate for constant skew (zero drift) by calibrating the skew between a parent and its spawned children. In addition, AIMS' skew-compensation algorithm can adjust timestamps in a way that eliminates physically impossible communications (e.g., messages going backwards in time). Our current efforts are directed toward creating new views to explain the observed performance of PVM programs. Some of the features planned for the near future include: (1) ConfigView, showing the physical topology of the virtual machine, inferred using specially formatted IP (Internet Protocol) packets: and (2) LoadView, synchronous animation of PVM-program execution and resource-utilization patterns.

Mehra, Pankaj↗