Loading...
Please wait, while we are loading the content...
Parallelizing the method of conjugate gradients for shared memory architectures (2004).
| Content Provider | CiteSeerX |
|---|---|
| Author | Löf, Henrik |
| Abstract | Solving Partial Differential Equations (PDEs) is an important problem in many fields of science and engineering. For most real-world problems modeled by PDEs, we can only approximate the solution using numerical methods. Many of these numerical methods result in very large systems of linear equations. A common way of solving these systems is to use an iterative solver such as the method of conjugate gradients. Furthermore, due to the size of these systems we often need parallel computers to be able to solve them in a reasonable amount of time. Shared memory architectures represent a class of parallel computer systems commonly used both in commercial applications and in scientific computing. To be able to provide cost-efficient computing solutions, shared memory architectures come in a large variety of configurations and sizes. From a programming point of view, we do not want to spend a lot of effort optimizing an application for a specific computer architecture. We want to find methods and principles of optimizing our programs that are generally applicable to a large class of architectures. |
| File Format | |
| Publisher Date | 2004-01-01 |
| Access Restriction | Open |
| Subject Keyword | Numerical Method Parallel Computer System Conjugate Gradient Partial Differential Equation Many Field Numerical Method Result Common Way Commercial Application Linear Equation Specific Computer Architecture Scientific Computing Parallel Computer Large Class Iterative Solver Memory Architecture Important Problem Shared Memory Architecture Large System Large Variety Reasonable Amount Real-world Problem |
| Content Type | Text |
