Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication

Autotuning Sparse Matrix-Vector Multiplication for Multicore

Sparse matrix-vector multiplication (SpMV) is an important kernel in scientific and engineering computing. Straightforward parallel implementations of SpMV often perform poorly, and with the increasing variety of architectural features in multicore processors, it is getting more difficult to determine the sparse matrix data structure and corresponding SpMV implementation that optimize performan...

Autotuning Divide-and-Conquer Matrix-Vector Multiplication

Divide and conquer is an important concept in computer science. It is used ubiquitously to simplify and speed up programs. However, it needs to be optimized, with respect to parameter settings for example, in order to achieve the best performance. The problem boils down to searching for the best implementation choice on a given set of requirements, such as which machine the program is running o...

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-...

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...

Optimization Methods for Sparse Matrix-Vector Multiplication