Mixed Precision Algorithm Improves Solution of Large Sparse Linear Systems
Analysis
This research explores a mixed-precision implementation of the Generalized Alternating-Direction Implicit (GADI) method for solving large sparse linear systems. The use of mixed precision can significantly improve the performance and reduce the memory footprint when solving these systems, common in scientific and engineering applications.
Key Takeaways
- •Focuses on improving the efficiency of solving large sparse linear systems, which are fundamental to numerous scientific and engineering simulations.
- •Employs mixed-precision arithmetic to optimize computational speed and memory usage.
- •Targets the GADI method, a widely used iterative technique for linear system solutions.
Reference
“The research focuses on the Generalized Alternating-Direction Implicit (GADI) method.”