In earlier work, we proposed a Telescopic approach which is a multi-layered approach for extreme-scale parallel mesh generation and adaptation. In this paper, we describe the Parallel Data Refinement (PDR) layer of the Telescopic approach. Namely focus on PDR's: (i) design and implementation and (ii) evaluation using TetGen, an open source mesh generation software, on shared memory machines. We outline lessons learned and future directions for revisiting the PDR layer and making adjustments in the implementation of the remaining layers of the Telescopic approach. Figure 1 Telescopic Approach to parallel mesh generation and adaptation and PDR layer in the middle.

While the epidemiologic trends concerning alprazolam (Xanax) are unknown, the use of benzodiazepines, in general, has increased in popularity among youth within recent years. To shed light on the drug problem, the current pilot study used a qualitative approach to investigate relevant beliefs, norms, and perceived addiction associated with alprazolam initiation among 46 youth who were attending an inpatient drug treatment program during the spring of 2004. Overwhelmingly, most participants stated that addiction to alprazolam occurs as early as initial consumption. Most youth in the study stated that their friends felt it was normal to use alprazolam. In addition, their control beliefs revealed that if someone wanted to stop it would be difficult because of the widespread use in their communities and family social reinforcement involved with its use. In this study, a majority of students stated that medical professionals such as doctors and pharmacists were the greatest facilitator of alprazolam acquisition. Implications for these results are discussed.

No abstract

Preliminary results of a long-term project entailing the parallelization of an industrial strength sequential mesh generator, called Advancing Front Local Reconnection (AFLR), are presented. AFLR has been under development for the last 25 years at the NSF/ERC center at Mississippi State University. The parallel procedure that is presented is called Pseudoconstrained (PsC) Parallel Data Refinement (PDR) and consists of the following steps: (i) use an octree data-decomposition scheme to divide the original geometry into subdomains (octree leaves), (ii) refine each subdomain with the proper adjustments of its neighbors using the given refinement code, and (iii) combine all subdomain data into a single, conforming mesh. Parallelism was achieved by implementing Pseudo-constrained Parallel Data Refinement AFLR (PsC.AFLR) on top of a runtime system called PREMA. During run time, the PsC.AFLR method exposes data decomposition information (number of subdomains waiting to be refined) to the underlying runtime system. In turn, this system facilitates work-load balancing and guides the program's execution towards the most efficient utilization of hardware resources. Preliminary results, on the mesh refinement operation, show that the end-user productivity (measured in terms of elements refined per second) increases as the number of cores in use are increased. When using approximately 16 cores, PsC.AFLR outperforms the serial AFLR code by about 2.5 times. PsC.AFLR also maintains its stability by generating meshes of comparable quality. Although it offers good end-user productivity, PsC.AFLR suffers in its capability to generate meshes with the same level of density or quality as that of the serial AFLR software due to the constraints set by subdomain boundaries that are required to successfully execute AFLR. These constraints demonstrate that it is not ideal to use AFLR in a black box manner when parallelizing the software. Its source code must be modified to a non-trivial extent if one wishes to remove these constraints and maximize the end-user productivity and potential scalability. Future work includes a PDR implementation similar to work done previously in the parallelization of the TetGen meshing software and the integration of PDR.AFLR in the context of a Telescopic Approach meant to achieve scalability in a large-scale framework.

No abstract