Grid generation for reduction in computation scale
时间: 2023-07-12 15:04:00 浏览: 79
网格生成是计算流体力学(CFD)和有限元分析(FEA)等领域中的一项重要技术,用于将复杂的物理区域离散成简单的几何形状,以便进行数值模拟和计算。
在减少计算规模方面,网格生成的目标是尽可能地减少网格数,同时保持足够的准确性和精度。这可以通过以下几个步骤来实现:
1. 网格划分:将物理区域划分成若干个较小的区域,以便于进行离散化计算。
2. 网格生成:根据物理模型和计算要求,生成合适的网格形状和大小,以满足计算精度要求。
3. 网格优化:通过对网格进行优化,如调整网格大小、形状和位置,以及增加或减少网格数量,达到减少计算规模的目的。
4. 网格质量评估:评估网格质量,如判断网格是否存在扭曲、死角、奇点等问题,以确保计算精度和稳定性。
5. 网格适应性:根据计算结果和模型变化,自动调整网格大小和形状,以达到更好的计算效果。
通过以上步骤,可以生成适合于复杂物理模型的网格,同时减少计算规模,提高计算效率和准确性。
相关问题
for reduction( :)s
"for reduction(:)" is a syntax used in parallel programming to specify a reduction operation on a set of values. It is commonly used in languages such as OpenMP and MPI to perform parallel reductions on arrays, lists, and other data structures.
The syntax "for reduction(oper: var)" specifies that the variable "var" will be used in a reduction operation, using the reduction operator "oper". The reduction operator can be any arithmetic or logical operator, such as +, *, &, |, etc.
For example, consider the following code snippet:
```
int sum = 0;
#pragma omp parallel for reduction(+:sum)
for (int i = 0; i < N; i++) {
sum += array[i];
}
```
This code uses OpenMP to perform a parallel sum of an array "array" of size N. The "reduction(+:sum)" syntax specifies that each thread will maintain a private copy of the variable "sum", and at the end of the loop, all private copies will be combined using the "+" operator to produce the final sum.
The "for reduction(:)" syntax is a shorthand for specifying a reduction operation on all variables in a loop. For example, consider the following code:
```
int sum = 0, prod = 1;
#pragma omp parallel for reduction(+:sum) reduction(*:prod)
for (int i = 0; i < N; i++) {
sum += array[i];
prod *= array[i];
}
```
This code performs both a sum and a product of the array "array" in parallel, using the "reduction(+:sum)" and "reduction(*:prod)" syntaxes to specify the reduction operations.
ValueError: None is not a valid value for reduction
这是一个技术问题,我可以回答。这个错误通常出现在使用 PyTorch 进行深度学习训练时,损失函数的 reduction 参数设置为 None,但是在计算损失时没有指定 reduction 方法,导致出现错误。解决方法是将 reduction 参数设置为 'mean' 或 'sum',或者在计算损失时指定 reduction 方法。