RAPIDS RAFT: Reusable Accelerated Functions and Tools#
RAFT contains fundamental widely-used algorithms and primitives for scientific computing, data science and machine learning. The algorithms are CUDA-accelerated and form building-blocks for rapidly composing analytics.
By taking a primitives-based approach to algorithm development, RAFT
accelerates algorithm construction time
reduces the maintenance burden by maximizing reuse across projects, and
centralizes core reusable computations, allowing future optimizations to benefit all algorithms that use them.
While not exhaustive, the following general categories help summarize the accelerated building blocks that RAFT contains:
Category |
Examples |
|---|---|
Data Formats |
sparse & dense, conversions, data generation |
Dense Operations |
linear algebra, matrix and vector operations, slicing, norms, factorization, least squares, svd & eigenvalue problems |
Sparse Operations |
linear algebra, eigenvalue problems, slicing, norms, reductions, factorization, symmetrization, components & labeling |
Spatial |
pairwise distances, nearest neighbors, neighborhood graph construction |
Basic Clustering |
spectral clustering, hierarchical clustering, k-means |
Solvers |
combinatorial optimization, iterative solvers |
Statistics |
sampling, moments and summary statistics, metrics |
Tools & Utilities |
common utilities for developing CUDA applications, multi-node multi-gpu infrastructure |