To exploit a heterogeneous computing (HC) environment, an application task may be decomposed into subtasks that have data dependencies. Subtask matching and scheduling consists of assigning subtasks to machines, ordering subtask execution for each machine, and ordering intermachine data transfers. The goal is to achieve the minimal completion time for the task. A heuristic approach based on a genetic algorithm is developed to do matching and scheduling in HC environments. It is assumed that the matcher/scheduler is in control of a dedicated HC suite of machines. The characteristics of this genetic-algorithm-based approach include: separation of the matching and the scheduling representations, independence of the chromosome structure from the details of the communication subsystem, and consideration of overlap among all computations and communications that obey subtask precedence constraints. It is applicable to the static scheduling of production jobs and can be readily used to collectively schedule a set of tasks that are decomposed into subtasks. Some parameters and the selection scheme of the genetic algorithm were chosen experimentally to achieve the best performance. Extensive simulation tests were conducted. For small-sized problems (e.g., a small number of subtasks and a small number of machines), exhaustive searches were used to verify that this genetic-algorithm-based approach found the optimal solutions. Simulation results for larger-sized problems showed that this genetic-algorithm-based approach outperformed two nonevolutionary heuristics and a random Search.

Purpose: Fas ligand (FasL) À844T/C polymorphism (rs763110) has a demonstrated association with lung cancer risk. FasL À844CC with higher FasL expression has been suggested to contribute to tumor progression via immune escape. However, the impact of FasL À844T/C polymorphism on the clinical outcome of non-small cell lung cancer (NSCLC) remains to be identified.Experimental Design: A total of 385 adjacent normal lung tissues from patients with NSCLC were collected to determine FasL À844T/C polymorphism by PCR-based restriction fragment length polymorphism. FasL mRNA and protein expression in lung tumors were evaluated by real-time PCR and immunohistochemistry. The prognostic value of FasL À844T/C polymorphism on survival and relapse was determined by Kaplan-Meier analysis and Cox proportional hazards models.Results: The FasL À844CC genotype had higher prevalence in those with advanced tumors than in those with early tumors (P ¼ 0.008). In addition, patients with the FasL À844CC genotype were more prone to tumor relapse than those with the FasL À844TTþTC genotype (62.1% vs. 37.9%, P ¼ 0.001). Multivariate Cox regression analysis showed that patients with the FasL À844CC genotype had poorer survival in terms of overall survival (OS) and relapse-free survival (RFS) than those with the FasL À844TTþTC genotype (24