The PHOBOS experiment at RHIC has measured the multiplicity of primary charged particles as a function of centrality and pseudorapidity in Au+Au collisions at {radical}(s{sub NN}) = 19.6, 130 and 200 GeV. Two observations indicate universal behavior of charged particle production in heavy ion collisions. The first is that forward particle production, over a range of energies, follows a universal limiting curve with a non-trivial centrality dependence. The second arises from comparisons with pp/{bar p}p and e{sup +}e{sup -} data. <Nch>/<N{sub part}/2> in nuclear collisions at high energy scales with {radical}s in a similar way as N{sub ch} in e{sup +}e{sup -} collisions and has a very weak centrality dependence. These features may be related to a reduction in the leading particle effect due to the multiple collisions suffered per participant in heavy ion collisions.

The PHOBOS experiment at RHIC has measured the multiplicity of primary charged particles as a function of centrality and pseudorapidity in Au+Au collisions at {radical}(s{sub NN}) = 19.6, 130 and 200 GeV. Two observations indicate universal behavior of charged particle production in heavy ion collisions. The first is that forward particle production, over a range of energies, follows a universal limiting curve with a non-trivial centrality dependence. The second arises from comparisons with pp/{bar p}p and e{sup +}e{sup -} data. <N{sub ch}>/<N{sub part}/2> in nuclear collisions at high energy scales with {radical}s in a similar way as N{sub ch} in e{sup +}e{sup -} collisions and has a very weak centrality dependence. These features may be related to a reduction in the leading particle effect due to the multiple collisions suffered per participant in heavy ion collisions.

Free electron lasers operating in the 0.1 to 1.5 nm wavelength have been proposed for the Stanford Linear Accelerator Center and DESY (Germany). The unprecedented brightness and associated fluence predicted for pulses <300 fs pose new challenges for optical components. A criterion for optical component design is required, implying an understanding of x-ray-matter interactions at these extreme conditions. In our experimental effort, the extreme conditions are simulated by currently available sources ranging from optical lasers, through x-ray lasers (at 14.7 nm) down to K-alpha sources ({approx}0.15 nm). In this paper we present an overview of our research program, including (a) Results from the experimental campaign at a short pulse (100 fs-5 ps) power laser at 800 nm, (b) K-a experiments, and (c) Computer modeling and experimental project using a tabletop high brightness ps x-ray laser at the Lawrence Livermore National Laboratory.

As with all electromagnet radiation, polarization of x-rays is a general phenomenon. Such polarization has been known since the classic experiments of Barkla in 1906. The general implementation of polarization to x-ray analysis had to await the fixed geometry of energy-dispersive systems. The means of optimizing these systems is shown in this review paper. Improved detection limits are the result.

Contemporary microprocessors provide a rich set of integrated performance counters that allow application developers and system architects alike the opportunity to gather important information about workload behaviors. These counters can capture instruction, memory, and operating system behaviors. Current techniques for analyzing data produced from these counters use raw counts, ratios, and visualization techniques to help users make decisions about their application source code. While these techniques are appropriate for analyzing data from one process, they do not scale easily to new levels demanded by contemporary computing systems. Indeed, the amount of data generated by these experiments is on the order of tens of thousands of data points. Furthermore, if users execute multiple experiments, then we add yet another dimension to this already knotty picture. This flood of multidimensional data can swamp efforts to harvest important ideas from these valuable counters. Very simply, this paper addresses these concerns by evaluating several multivariate statistical techniques on these datasets. We find that several techniques, such as statistical clustering, can automatically extract important features from this data. These derived results can, in turn, be feed directly back to an application developer, or used as input to a more comprehensive performance analysis environment, such as …

Although programming models and languages appear to be converging, the computational workloads and communication patterns for scientific applications vary dramatically, depending, in part, on the nature of the problem the applications are solving. In this paper, we investigate the scalability, architectural requirements, and inherent behavioral characteristics of eight scalable scientific applications. We provide a comparative analysis of these applications and isolate their performance characteristics using empirical measurements. We refine our analysis into precise explanations of the factors that influence performance and scalability for each application; we distill these factors into common traits and overall recommendations. Initially, we examine the overall scalability of each application. Then, based on these results, we iteratively investigate the primary factors that affect scalability and performance using a combination of measurement techniques, such as message tracing and monitoring hardware counters, until we can understand each application's primary performance properties and the root causes of those properties.

Traditional techniques for performance analysis provide a means for extracting and analyzing raw performance information from applications. Users then reason about and compare this raw performance data to their performance expectations for important application constructs. This comparison can be tedious, difficult, and error-prone for the scale and complexity of today's architectures and software systems. To address this situation, we present a methodology and prototype that allows users to assert performance expectations explicitly in their source code using performance assertions. As the application executes, each performance assertion in the application collects data implicitly to verify the assertion. By allowing the user to specify a performance expectation with individual code segments, the runtime system can jettison raw data for measurements that pass their expectation, while reacting to failures with a variety of responses. We present several compelling uses of performance assertions with our operational prototype including raising a performance exception, validating a performance model, and adapting an algorithm to an architecture empirically at runtime.

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The evolution of thickened chemical agent released at supersonic velocities, due to a missile defense intercept or a properly functioning warhead, has been misunderstood. Current and historical experimental and modeling efforts have attributed agent breakup to a variety of droplet breakup mechanisms. According to this model, drops of agent fragment into subsequent generations of smaller drops until a stable drop size is reached. Recent experimental data conducted in a supersonic wind tunnel show that agent breakup is not driven by any droplet breakup mechanism. The breakup of agent is instead governed by viscoelastic behavior and aerodynamic history effects. This viscoelastic breakup mechanism results in the formation of threads and sheets of liquid, instead of drops. The evolution and final state of agent released has broad implications not only for aerobreakup models, but also for all atmospheric dispersion models.