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 |
.. toctree:: :maxdepth: 2 :caption: Contents: quick_start.md build.md developer_guide.md cpp_api.rst pylibraft_api.rst raft_dask_api.rst using_comms.rst contributing.md