This paper presents a compiler and runtime framework for parallelizing sparse matrix computations that have loop-carried dependences. Our approach automatically generates a runtime inspector to collect data dependence information and achieves wavefront parallelization of the computation, where iterations within a wavefront execute in parallel, and synchronization is required across wavefronts. A key contribution of this paper involves dependence simplification, which reduces the time and space overhead of the inspector. This is implemented within a polyhedral compiler framework, extended for sparse matrix codes. Results demonstrate the feasibility of using automatically-generated inspectors and executors to optimize ILU factorization and sy...
Matrix computations lie at the heart of most scientific computational tasks. The solution of linear ...
Space-efficient data structures for sparse matrices typically yield programs in which not all data d...
This paper describes two portable packages for general-purpose sparse matrix computations: SPARSKIT...
This paper presents a combined compile-time and runtime loop-carried dependence analysis of sparse m...
International audienceIn this paper, we propose a generic method of automatic parallelization for sp...
dissertationSparse matrix codes are found in numerous applications ranging from iterative numerical ...
[[abstract]]We present a generic matrix class facility in Java and an on-going project for a runtime...
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for m...
We have developed a framework based on relational algebra for compiling efficient sparse matrix cod...
Run-time compilation techniques have been shown effective for automating the parallelization of loop...
Algorithms are often parallelized based on data dependence analysis manually or by means of parallel...
Space-efficient data structures for sparse matrices are an important concept in numerical programmin...
Irregular applications such as big graph analysis, material simulations, molecular dynamics simulati...
Abstract—Many sparse matrix computations can be speeded up if the matrix is first reordered. Reorder...
AbstractWe have recently multiprocessed a code for the direct solution of sparse linear equations on...
Matrix computations lie at the heart of most scientific computational tasks. The solution of linear ...
Space-efficient data structures for sparse matrices typically yield programs in which not all data d...
This paper describes two portable packages for general-purpose sparse matrix computations: SPARSKIT...
This paper presents a combined compile-time and runtime loop-carried dependence analysis of sparse m...
International audienceIn this paper, we propose a generic method of automatic parallelization for sp...
dissertationSparse matrix codes are found in numerous applications ranging from iterative numerical ...
[[abstract]]We present a generic matrix class facility in Java and an on-going project for a runtime...
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for m...
We have developed a framework based on relational algebra for compiling efficient sparse matrix cod...
Run-time compilation techniques have been shown effective for automating the parallelization of loop...
Algorithms are often parallelized based on data dependence analysis manually or by means of parallel...
Space-efficient data structures for sparse matrices are an important concept in numerical programmin...
Irregular applications such as big graph analysis, material simulations, molecular dynamics simulati...
Abstract—Many sparse matrix computations can be speeded up if the matrix is first reordered. Reorder...
AbstractWe have recently multiprocessed a code for the direct solution of sparse linear equations on...
Matrix computations lie at the heart of most scientific computational tasks. The solution of linear ...
Space-efficient data structures for sparse matrices typically yield programs in which not all data d...
This paper describes two portable packages for general-purpose sparse matrix computations: SPARSKIT...