Mapping Finite Element Graphs on Hypercubes

In parallel computing, it is important to map a parallel program onto a parallel computer such that the total execution time of a parallel program is minimized. In general, a parallel program and a parallel computer can be represented by a task graph (TG) and a processor graph (PG), respectively. For a TG, nodes represent tasks of a parallel program and edges denote the data communication needed between tasks. The weights associated with nodes and edges represent the computational load and communication cost, respectively. For a PG, nodes and edges denote processors and communication channels, respectively. By using the graph model, the mapping problem becomes a task allocation problem. In the task allocation problem, we try to distribute the computational load of a parallel program to the processors of a parallel computer as evenly as possible (the load balance criterion (LBC) and minimize the communication cost of processors (the minimum communication cost criterion (MCCC)). The optimal assignment of tasks to processors in order to minimize the total execution time is known to be NP-complete [GaJo79]. This means that the optimal solution is intractable. Therefore, satisfactory suboptimal solutions are generally sought. In this paper, we will discuss how to map finite element graphs (FEGs) onto hypercubes. Our schemes are general and are applicable to a wide variety of PGs. The finite element method (FEM) is a widely used method for the structural modeling of physical system [LaPi83]. Due to the properties of compute-intensiveness and compute-locality, it is very attractive to implement this method on parallel computers [BeBo87] [Bokh81] [Jord78] [SaEr87]. The number of nodes in a FEG is usually greater than the number of processors in a parallel computer. It is important to partition a FEG into M modules such that the computational load of modules are equal and the communication cost among modules are minimized, where M is the number of processors of a parallel computer

Mapping Finite Element Graphs on Hypercubes

In parallel computing, it is important to map a parallel program onto a parallel computer such that the total execution time of a parallel program is minimized. In general, a parallel program and a parallel computer can be represented by a task graph (TG) and a processor graph (PG), respectively. For a TG, nodes represent tasks of a parallel program and edges denote the data communication needed between tasks. The weights associated with nodes and edges represent the computational load and communication cost, respectively. For a PG, nodes and edges denote processors and communication channels, respectively. By using the graph model, the mapping problem becomes a task allocation problem. In the task allocation problem, we try to distribute the computational load of a parallel program to the processors of a parallel computer as evenly as possible (the load balance criterion (LBC) and minimize the communication cost of processors (the minimum communication cost criterion (MCCC)). The optimal assignment of tasks to processors in order to minimize the total execution time is known to be NP-complete [GaJo79]. This means that the optimal solution is intractable. Therefore, satisfactory suboptimal solutions are generally sought. In this paper, we will discuss how to map finite element graphs (FEGs) onto hypercubes. Our schemes are general and are applicable to a wide variety of PGs. The finite element method (FEM) is a widely used method for the structural modeling of physical system [LaPi83]. Due to the properties of compute-intensiveness and compute-locality, it is very attractive to implement this method on parallel computers [BeBo87] [Bokh81] [Jord78] [SaEr87]. The number of nodes in a FEG is usually greater than the number of processors in a parallel computer. It is important to partition a FEG into M modules such that the computational load of modules are equal and the communication cost among modules are minimized, where M is the number of processors of a parallel computer

A three-phase parallel algorithm for solving linear recurrences

AbstractWe present in this paper a three-phase parallel algorithm on the unshuffle network for solving linear recurrences. Through a detailed analysis on the special matrix multiplications involved in the computation we show that the first n terms of an mth order linear recurrence can be computed in O(m3 log nm) time using Θ(nm log nm) processors. For the usual case when m is a small constant. the algorithm achieves cost optimality

Temporal and Spatial Properties of Arterial Pulsation Measurement Using Pressure Sensor Array

Conventionally, a pulse taking platform is based on a single sensor, which initiates a feasible method of quantitative pulse diagnosis. The aim of this paper is to implement a pulse taking platform with a tactile array sensor. Three-dimensional wrist pulse signals are constructed, and the length, width, ascending slope, and descending slope are defined following the surface of the wrist pulse. And the pressure waveform of the wrist pulse obtained through proposed pulse-taking platform has the same performance as the single sensor. Finally, the results of a paired samples t-test reveal that the repeatability of the proposal platform is consistent with clinical experience. On the other hand, the results of ANOVA indicate that differences exist among different pulse taking depths, and this result is consistent with clinical experience in traditional Chinese medicine pulse diagnosis (TCMPD). Hence, the proposed pulse taking platform with an array sensor is feasible for quantification in TCMPD

Luteolin Suppresses Inflammatory Mediator Expression by Blocking the Akt/NFκB Pathway in Acute Lung Injury Induced by Lipopolysaccharide in Mice

Acute lung injury (ALI), instilled by lipopolysaccharide (LPS), is a severe illness with excessive mortality and has no specific treatment strategy. Luteolin is an anti-inflammatory flavonoid and widely distributed in the plants. Pretreatment with luteolin inhibited LPS-induced histological changes of ALI and lung tissue edema. In addition, LPS-induced inflammatory responses, including increased vascular permeability, tumor necrosis factor (TNF)-α and interleukin (IL)-6 production, and expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), were also reduced by luteolin in a concentration-dependent manner. Furthermore, luteolin suppressed activation of NFκB and its upstream molecular factor, Akt. These results suggest that the protection mechanism of luteolin is by inhibition of NFκB activation possibly via Akt

Polymorphisms of the XRCC1, XRCC3, & XPD genes, and colorectal cancer risk: a case-control study in Taiwan

BACKGROUND: Recent studies relating to the association between DNA repair-gene polymorphisms and colorectal cancer risk would, to the best of our knowledge, appear to be very limited. This study was designed to examine the polymorphisms associated with three DNA repair genes, namely: XRCC1 Arg399Gln, XRCC3 Thr241Met and XPD Lys751Gln, and investigate their role as susceptibility markers for colorectal cancer. METHODS: We conducted a case-control study including 727 cases of cancer and 736 hospital-based age- and sex-matched healthy controls to examine the role of genetic polymorphisms of three DNA-repair genes (XRCC1, XRCC3 and XPD) in the context of colorectal cancer risk for the Taiwanese population. Genomic DNA isolated from 10 ml whole blood was used to genotype XRCC1 Arg399Gln, XRCC3 Thr241Met and XPD Lys751Gln by means of polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP) analysis. RESULTS: The risk for colorectal cancer did not appear to differ significantly amongst individuals featuring the XRCC1 399Arg/Arg genotype (OR = 1.18; 95% CI, 0.96–1.45), the XRCC3 241Thr/Thr genotype (OR = 1.25; 95% CI, 0.88–1.79) or the XPD 751Gln allele (OR = 1.20; 95% CI, 0.90–1.61), although individuals featuring a greater number of risk genotypes (genotype with OR greater than 1) did experience a higher risk for colorectal cancer when compared to those who didn't feature any risk genotypes (Trend test P = 0.03). Compared with those individuals who didn't express any putative risk genotypes, individuals featuring all of the putative risk genotypes did experience a significantly greater cancer risk (OR = 2.43, 95% CI = 1.21–4.90), particularly for individuals suffering tumors located in the rectum (OR = 3.18, 95% CI = 1.29–7.82) and diagnosed prior to the age of 60 years (OR = 4.90, 95% CI = 1.72–14.0). CONCLUSIONS: Our results suggest that DNA-repair pathways may simultaneously modulate the risk of colorectal cancer for the Taiwanese population, and, particularly for rectal cancer and younger patients

A Polygon Model for Wireless Sensor Network Deployment with Directional Sensing Areas

The modeling of the sensing area of a sensor node is essential for the deployment algorithm of wireless sensor networks (WSNs). In this paper, a polygon model is proposed for the sensor node with directional sensing area. In addition, a WSN deployment algorithm is presented with topology control and scoring mechanisms to maintain network connectivity and improve sensing coverage rate. To evaluate the proposed polygon model and WSN deployment algorithm, a simulation is conducted. The simulation results show that the proposed polygon model outperforms the existed disk model and circular sector model in terms of the maximum sensing coverage rate