Sentence examples for sparse computation from inspiring English sources

This, combined with sparse computation associated with linear finite elements, results in significant reduction in the computational cost.

In this paper, we propose an efficient data structure named AdELL+ for optimizing the SpMV kernel on GPUs, focusing on performance bottlenecks of sparse computation.

It is shown how computation to determine steady-state conditions in pipeline networks can be greatly facilitated using sparse computation techniques.

For example, in the experiment shown in Figure 4a,b, we set σ= λ/ C and compared the performance at different values of C. We then use an efficient sparse computation to produce the following three sums at each CpG site x i in (x1,…, x n ): The second and third of these sums, S K (i) and S KK (i), depend only on the CpG sites, { x i }, not the statistics, { Y i }.

Both issues are dealt with in depth and in the context of sparse computations (for the first issue) and irregular histogram reductions (for the second issue).

In this work, efficient algorithms for sparse computations (reordering algorithms, storage schemes, symbolic factorization, master degree-of-freedom, L∗D∗U numerical factorization, forward and backward solutions) are developed and integrated into the proposed procedures.

In the next section this problem is automatically solved by combining the genomic genetic effect g with the polygenic effect a. We also note that the determinant equals where A22 - A21 A12 is easily obtained from A-1, and the determinant can be computed using sparse matrix computation.

An efficient computation of a composite length discrete Fourier transform (DFT), as well as a fast Fourier transform (FFT) of both time and space data sequences in uncertain (non-sparse or sparse) computational scenarios, requires specific processing algorithms.

Alternatively, A11 = ((A-1 -1 11 A-1 -1 11mayted using the formula (5) on A-1 and using sparse matrix computation.

In this paper, we propose a generic method of automatic parallelization for sparse matrix computation.

Third, the inverse iteration of the discretized matrix involved in sparse eigenvalue computation is iteratively achieved by utilizing the induced dimension reduction method (IDR s)).