-
Notifications
You must be signed in to change notification settings - Fork 197
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Finalizing some more bits of the docs. This has also included cleaning up several header files to make the docs a little more clean. Authors: - Corey J. Nolet (https://github.com/cjnolet) Approvers: - Dante Gama Dessavre (https://github.com/dantegd) URL: #566
- Loading branch information
Showing
142 changed files
with
464 additions
and
2,301 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -1,14 +1,22 @@ | ||
# libraft | ||
|
||
RAFT (RAPIDS Analytics Framework Toolkit) is a library containing building-blocks for rapid composition of RAPIDS Analytics. These building-blocks include shared representations, mathematical computational primitives, and utilities that accelerate building analytics and data science algorithms in the RAPIDS ecosystem. Both the C++ and Python components can be included in consuming libraries, providing building-blocks for both dense and sparse matrix formats in the following general categories: | ||
RAFT contains fundamental widely-used algorithms and primitives for data science, graph 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 functions in RAFT: | ||
|
||
##### | ||
| Category | Description / Examples | | ||
| Category | Examples | | ||
| --- | --- | | ||
| **Data Formats** | tensor representations and conversions for both sparse and dense formats | | ||
| **Data Generation** | graph, spatial, and machine learning dataset generation | | ||
| **Dense Operations** | linear algebra, statistics | | ||
| **Spatial** | pairwise distances, nearest neighbors, neighborhood / proximity graph construction | | ||
| **Sparse/Graph Operations** | linear algebra, statistics, slicing, msf, spectral embedding/clustering, slhc, vertex degree | | ||
| **Solvers** | eigenvalue decomposition, least squares, lanczos | | ||
| **Tools** | multi-node multi-gpu communicator, utilities | | ||
| **Data Formats** | sparse & dense, conversions, data generation | | ||
| **Dense Linear Algebra** | matrix arithmetic, norms, factorization, least squares, svd & eigenvalue problems | | ||
| **Spatial** | pairwise distances, nearest neighbors, neighborhood graph construction | | ||
| **Sparse Operations** | linear algebra, eigenvalue problems, slicing, symmetrization, labeling | | ||
| **Basic Clustering** | spectral clustering, hierarchical clustering, k-means | | ||
| **Solvers** | combinatorial optimization, iterative solvers | | ||
| **Statistics** | sampling, moments and summary statistics, metrics | | ||
| **Distributed Tools** | multi-node multi-gpu infrastructure | |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.