Parallel computing is a type of computational construction in which multiple processors perform multiple small calculations at once and a whole large and complex set of problems. Dynamic simulation and real-world data modeling are required to achieve a similar level of parallel computation are critical. Co-calculation provides integration and saves time and money. Parallel computation can only be arranged for complex large data sets and his administration. Parallel computers have been used to solve various isolation and continuous optimization problems. Mechanisms such as single level, linear optimization and branch and internal point systems are not restricted, and genetic programming is often used in parallel and effectively. Embedded systems are generally distributed and often face changing demands over time. That said, existing methods that are obsolete or invalid at the time of compilation are unpredictable by classifying optimal computing tasks as the best use of existing resources for Hardware (HW) and Software (SW). Here, investigate a different idiosyncratic algorithm to balance the load of online HW / SW segmentation. Once there are modifications to suit the computing needs, the system must assign dynamic tasks and become necessary when performing tasks with local hardware or software sources and other nodes. The results obtained show that the proposed method significantly shares the load between different nodes and significantly reduces the allowable task's worst response time.

并行计算是一种计算结构，其中多个处理器一次执行多个小计算，并且会处理一系列大而复杂的问题。达到相似水平的并行计算所需的动态仿真和实际数据建模至关重要。协同计算可提供集成并节省时间和金钱。只能为复杂的大型数据集及其管理安排并行计算。并行计算机已用于解决各种隔离和连续优化问题。诸如单级，线性优化以及分支和内部点系统之类的机制不受限制，并且遗传程序设计经常并行有效地使用。嵌入式系统通常是分布式的，并且随着时间的推移经常面临变化的需求。那就是 通过将最佳计算任务分类为对硬件（HW）和软件（SW）的现有资源的最佳利用，无法预测在编译时过时或无效的现有方法。在这里，研究一种不同的特殊算法来平衡在线硬件/软件细分的负载。一旦进行了修改以适合计算需求，系统就必须分配动态任务，并且在使用本地硬件或软件源以及其他节点执行任务时变得很有必要。所得结果表明，该方法在不同节点之间显着分担了负载，并显着减少了允许任务的最差响应时间。