GPU-accelerated Kendall distance computation for large or sparse data.

ABSTRACT:

Background

Current experimental practices typically produce large multidimensional datasets. Distance matrix calculation between elements (e.g., samples) for such data, although being often necessary in preprocessing for statistical inference or visualization, can be computationally demanding. Data sparsity, which is often observed in various experimental data modalities, such as single-cell sequencing in bioinformatics or collaborative filtering in recommendation systems, may pose additional algorithmic challenges.

Results

We present GPU-Assisted Distance Estimation Software (GADES), a graphical processing unit (GPU)-enhanced package that allows for massively paralleled Kendall-$\tau$ distance matrices computation. The package's architecture involves specific memory managem

SUBMITTER: Akhtyamov P 

PROVIDER: S-EPMC11631066 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature