Direct methods for sparse matrices
Direct methods for sparse matrices
Solving structural mechanics problems on the CalTech Hypercube machine
Computer Methods in Applied Mechanics and Engineering
Determination of stripe structures for finite element matrics
SIAM Journal on Numerical Analysis
The white dwarf: a high-performance application-specific processor
ISCA '88 Proceedings of the 15th Annual International Symposium on Computer architecture
Introduction to Parallel & Vector Solution of Linear Systems
Introduction to Parallel & Vector Solution of Linear Systems
The minimum degree ordering with constraints
SIAM Journal on Scientific and Statistical Computing
A Systolic Accelerator for the Iterative Solution of Sparse Linear Systems
IEEE Transactions on Computers
Computer architecture: a quantitative approach
Computer architecture: a quantitative approach
Sparse matrix computations: implications for cache designs
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
ACM Transactions on Mathematical Software (TOMS)
Computer Structures: Principles and Examples
Computer Structures: Principles and Examples
Computer Solution of Large Sparse Positive Definite
Computer Solution of Large Sparse Positive Definite
The Effects of Communication Overhead on the Speedup of Parallel 3-D Finite Element Applications
IPPS '92 Proceedings of the 6th International Parallel Processing Symposium
Conjugate gradient methods for partial differential equations.
Conjugate gradient methods for partial differential equations.
Performance coupling: case studies for improving the performance of scientific applications
Journal of Parallel and Distributed Computing
Vector ISA Extension for Sparse Matrix-Vector Multiplication
Euro-Par '99 Proceedings of the 5th International Euro-Par Conference on Parallel Processing
Streaming sparse matrix compression/decompression
HiPEAC'05 Proceedings of the First international conference on High Performance Embedded Architectures and Compilers
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The finite element method is a general and powerful technique for solving partial differential equations. The computationally intensive step of this technique is the solution of a linear system of equations. Very large and very sparse system matrices result from large finite-element applications. The sparsity must be exploited for efficient use of memory and computational components in executing the solution step. In this paper we propose a scheme, called SPAR, for efficiently storing and performing computations on sparse matrices. SPAR consists of an alternate method of representing sparse matrices and an architecture that efficiently executes computations on the proposed data structure. The SPAR architecture has not been built, but we have constructed a register-transfer level simulator and executed the sparse matrix computations used with some large finite element applications. The simulation results demonstrate a 95% utilization of the floating-point units for some 3D applications. SPAR achieves high utilization of memory, memory bandwidth, and floating-point units when executing sparse matrix computations.