Optimization by runtime specialization for sparse matrix-vector multiplication

Optimization of Sparse Matrix-Vector Multiplication by Specialization

Program specialization is the process of generating optimized programs based on available inputs. It is particularly applicable when some input data are used repeatedly while other input data vary. Specialization can be employed at compile-time as well as at run-time, depending on when the inputs become available. In this paper we explore the potential for obtaining speed-ups for sparse matrix-...

Optimization Methods for Sparse Matrix-Vector Multiplication

Optimization by Run-time Specialization for Sparse Matrix-Vector Multiplication (Submitted for publication)

Run-time specialization is the process of generating programs based on information available only at run time. This technique has the potential of generating highly efficient codes, at the expense of the overheads of the run-time code generation. It is applicable when some input data is used repeatedly while other input data varies. In this paper we explore the potential for obtaining speedups ...

A lightweight optimization selection method for Sparse Matrix-Vector Multiplication

In this paper, we propose an optimization selection methodology for the ubiquitous sparse matrix-vector multiplication (SpMV) kernel. We propose two models that attempt to identify the major performance bottleneck of the kernel for every instance of the problem and then select an appropriate optimization to tackle it. Our first model requires online profiling of the input matrix in order to det...

Sparse Matrix-Vector Multiplication for Circuit Simulation

Sparse Matrix-Vector Multiplication (SpMV) plays an important role in numerical algorithm in circuit simulation. In this report, we utilize Message Passing Interface (MPI) to parallelize the SpMV. In addition, resulting from the circuit simulation matrix formulation, the circuit systems are often represented as unstructured, not evenly-distributed sparse matrices. Therefore, we automatically de...