There exist many storage formats for the in-memory representation of sparse matrices. Choosing the format that yields the quickest processing of any given sparse matrix requires considering the exact non-zero structure of the matrix, as well as the current execution environment. Each of these factors can change at runtime. The matrix structure can vary as computation progresses, while the envir...

Large sparse matrices play important role in many modern information retrieval methods. These methods, such as clustering, latent semantic indexing, performs huge number of computations with such matrices, thus their implementation should be very carefully designed. In this paper we discuss three implementations of sparse matrices. The first one is classical, based on lists. The second is previ...

This paper discusses the implementation of the sparse matrix support with Octave. It address the algorithms that have been used, their implementation, including examples of using sparse matrices in scripts and in dynamically linked code. The octave sparse functions the compared with their equivalent functions with Matlab, and benchmark timings are calculated.

We present the design and implementation of a universal, single-bitstream library for accelerating matrixvector multiplication using FPGAs. Our library handles multiple matrix encodings ranging from dense to multiple sparse formats. A key novelty in our approach is the introduction of a hardware-optimized sparse matrix representation called Compressed Variable-Length Bit Vector (CVBV), which re...

The sparse matrix-vector multiplication (SpMxV) is a kernel operation widely used in iterative linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers. Matrices with irregular sparsity patterns make it difficult to utilize cache locality effectively in SpMxV computations. In this work, we investigate singleand multiple-SpMxV frameworks for exploiting c...