CPU and GPU real-time filtering methods for dense surface metrology using general matrix to matrix multiplications

"Wide or tall" and "sparse matrix dense matrix" multiplications

This note explores sparse matrix dense matrix (SMDM) multiplications, useful in block Krylov or block Lanczos methods. SMDM computations are AU , and V A, multiplication of a large sparse matrix m × n matrix A by a matrix V of k rows of length m or a matrix U of k columns of length k, k << m, k << n . In a block Lanczos or Krylov algorithm, matrix matrix multiplications with the ”tall” U and ”w...

Implementing Matrix Multiplications on the Multi-Core CPU Architectures

Recent commercial microprocessors are concentrating on the multi-core CPU architectures, while most parallel and/or distributed computing methods focus on the multi-CPU architectures. Therefore, there are needs to analyze and adapt traditional parallel algorithms for the new multi-core environments. In this paper, we use matrix multiplications as the target problem, and implemented it using var...

Systolic arrays for matrix multiplications

Sparse-matrix vector multiplication on hybrid CPU+GPU platform

Sparse-matrix vector multiplication(Spmv) is a basic operation in many linear algebra kernels.So it is interesting to have a spmv on modern architectures like GPU. As it is a irregular computation CPU also performs compares to GPU. So it is interesting to have this routine in hybrid architectures like CPU+GPU.So we have designed a hybrid algorithm for Spmv which uses a CPU and a GPU. We have ex...

Heterogeneous Sparse Matrix Computations on Hybrid GPU/CPU Platforms

Hybrid GPU/CPU clusters are becoming very popular in the scientific computing community, as attested by the number of such systems present in the Top 500 list. In this paper, we address one of the key algorithms for scientific applications: the computation of sparse matrix-vector products that lies at the heart of iterative solvers for sparse linear systems. We detail how design patterns for sp...