We examine the effect of using complexity-reducing relations [Kirby et al. 2006] to generate optimized code for the evaluation of finite-element variational forms. The optimizations are implemented in a prototype code named FErari, which has been integrated as an optimizing backend to the FEniCS form compiler, FFC [Kirby and Logg 2006; 2007]. In some cases, FErari provides very little speedup, while in other cases we obtain reduced local operation counts by a factor of as much as 7.9 and speedups for the assembly of the global sparse matrix by as much as a factor of 2.8 (see Figure 9). © 2008 ACM
How do we build maintainable, robust, and performance-portable scientific applications? This thesi...
A normal Finite Element code typically takes about 30-40 % of the total time to cal-culate and assem...
Finite differencing is a program optimization method that generalizes strength reduction, and provid...
We examine the effect of using complexity-reducing relations [Kirby et al. 2006] to generate op-timi...
As a key step towards a complete automation of the finite element method, we present a new algorithm...
One of the key features of FEniCS is automated code generation for the general and efficient solutio...
One of the key features of FEniCS is automated code generation for the general and efficient 7018 so...
In Chapter 8, we presented a framework for efficient evaluation of multilinear forms based on 7311 e...
This chapter addresses the conventional run-time quadrature approach for the numerical integration o...
We investigate the compilation of general multilinear variational forms over affines simplices and p...
Much of the FEniCS software is devoted to the formulation of variational forms (UFL), the discretiza...
We examine aspects of the computation of finite element matrices and vectors which are made possible...
The tensor contraction structure for the computation of the element tensor AT obtained in Chapter 8,...
AbstractWe argue that producing maintainable high-performance implementations of finite element meth...
At the heart of any finite element simulation is the assembly of matrices and vectors from discrete ...
How do we build maintainable, robust, and performance-portable scientific applications? This thesi...
A normal Finite Element code typically takes about 30-40 % of the total time to cal-culate and assem...
Finite differencing is a program optimization method that generalizes strength reduction, and provid...
We examine the effect of using complexity-reducing relations [Kirby et al. 2006] to generate op-timi...
As a key step towards a complete automation of the finite element method, we present a new algorithm...
One of the key features of FEniCS is automated code generation for the general and efficient solutio...
One of the key features of FEniCS is automated code generation for the general and efficient 7018 so...
In Chapter 8, we presented a framework for efficient evaluation of multilinear forms based on 7311 e...
This chapter addresses the conventional run-time quadrature approach for the numerical integration o...
We investigate the compilation of general multilinear variational forms over affines simplices and p...
Much of the FEniCS software is devoted to the formulation of variational forms (UFL), the discretiza...
We examine aspects of the computation of finite element matrices and vectors which are made possible...
The tensor contraction structure for the computation of the element tensor AT obtained in Chapter 8,...
AbstractWe argue that producing maintainable high-performance implementations of finite element meth...
At the heart of any finite element simulation is the assembly of matrices and vectors from discrete ...
How do we build maintainable, robust, and performance-portable scientific applications? This thesi...
A normal Finite Element code typically takes about 30-40 % of the total time to cal-culate and assem...
Finite differencing is a program optimization method that generalizes strength reduction, and provid...