In sparse nonnegative component analysis (sparse NMF) a given dataset is decomposed into a mixing matrix and a feature data set, which are both nonnegative and fulfill certain sparsity constraints. In this paper, we extend the sparse NMF algorithm to allow for varying sparsity in each feature and discuss the uniqueness of an involved projection step. Furthermore, the eligibility of the extended...

We describe how various sparse matrix and distribution formats can be handled using the relational approach to sparse matrix code compilation This approach allows for the development of compilation techniques that are independent of the storage formats by viewing the data structures as relations and abstracting the implementation details as access methods Introduction Sparse matrix computations...

In this paper, we present a new sparse matrix data format that leads to improved memory coalescing and more efficient sparse matrix-vector multiplication (SpMV) for a wide range of problems on high throughput architectures such as a graphics processing unit (GPU). The sparse matrix structure is constructed by sorting the rows based on the row length (defined as the number of non-zero elements i...

The Recursive Sparse Blocks (RSB) is a sparse matrix layout designed for coarse grained parallelism and reduced cache misses when operating with matrices, which are larger than a computer’s cache. By laying out the matrix in sparse, non overlapping blocks, we allow for the shared memory parallel execution of transposed SParse Matrix-Vector multiply (SpMV ), with higher efficiency than the tradi...

In this article, we introduce a cache-oblivious method for sparse matrix vector multiplication. Our method attempts to permute the rows and columns of the input matrix using a hypergraph-based sparse matrix partitioning scheme so that the resulting matrix induces cache-friendly behaviour during sparse matrix vector multiplication. Matrices are assumed to be stored in row-major format, by means ...

Sparse linear least squares problems containing a few relatively dense rows occur frequently in practice. Straightforward solution of these problems could cause catastrophic ll and delivers extremely poor performance. This paper studies a scheme for solving such problems eeciently by handling dense rows and sparse rows separately. How a sparse matrix is partitioned into dense rows and sparse ro...

Compressive Sensing (CS) can use fewer samples to recover a great number of original data, which have a sparse representation in a proper basis. For energy-constrained Wireless Sensor Networks (WSNs), CS provides an effective data gathering approach. Gaussian random matrix satisfies Restricted Isometry Property (RIP) with high probability. The class of matrices is usually selected as the measur...

This paper presents a preliminary experimental study of the performance of basic sparse matrix computations on the CM-200 and the CM-5. We concentrate on examining various ways of performing general sparse matrix-vector operations and the basic primitives on which these are based. We compare various data structures for storing sparse matrices and their corresponding matrix – vector operations. ...

This paper presents a preliminary experimental study of the performance of basic sparse matrix computations on the CM-200 and the CM-5. We concentrate on examining various ways of performing general sparse matrix-vector operations and the basic primitives on which these are based. We compare various data structures for storing sparse matrices and their corresponding matrix – vector operations. ...

Sparse Matrix Vector multiplication (SpMV) is one of the most important operation for exact sparse linear algebra. A lot of research has been done by the numerical community to provide efficient sparse matrix formats. However, when computing over finite fields, one need to deal with multi-precision values and more complex operations. In order to provide highly efficient SpMV kernel over finite ...