Accelerate adjacency matrix to CSR conversion for DBSCAN #4803
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This PR is a follow up to PR #4776, which also involves DBSCAN. |
ahendriksen
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tfeher
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Thanks @ahendriksen for this work! I have marked a few issue that shall be addressed. Overall it looks good, and I am excited to see this improvement.
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For large data sizes, the batch size of the DBSCAN algorithm is small in order to fit the distance matrix in memory. This results in a matrix that has dimensions num_points x batch_size, both for the distance and adjacency matrix. The conversion of the boolean adjacency matrix to CSR format is performed in the 'adjgraph' step. This step was slow when the batch size was small, as described in issue rapidsai#2387. In this commit, the adjgraph step is sped up. This is done in two ways: 1. The adjacency matrix is now stored in row-major batch_size x num_points format --- it was transposed before. This required changes in the vertexdeg step. 2. The csr_row_op kernel has been replaced by the adj_to_csr kernel. This kernel can divide the work over multiple blocks even when the number of rows (batch size) is small. It makes optimal use of memory bandwidth because rows of the matrix are laid out contiguously in memory.
Temporary commit to check if CI passes with new raft warp-aggregated atomics.
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@ahendriksen overall this good to me, minus moving the csr_to_adj back over to RAFT. I also left a little reminder to update the raft pin now that the PR for the atomic utilty function has been moved.
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Codecov Report
@@ Coverage Diff @@
## branch-22.08 #4803 +/- ##
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Coverage 77.62% 77.62%
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Files 180 180
Lines 11382 11384 +2
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+ Hits 8835 8837 +2
Misses 2547 2547
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Fixes issue rapidsai#2387. For large data sizes, the batch size of the DBSCAN algorithm is small in order to fit the distance matrix in memory. This results in a matrix that has dimensions num_points x batch_size, both for the distance and adjacency matrix. The conversion of the boolean adjacency matrix to CSR format is performed in the 'adjgraph' step. This step was slow when the batch size was small, as described in issue rapidsai#2387. In this commit, the adjgraph step is sped up. This is done in two ways: 1. The adjacency matrix is now stored in row-major batch_size x num_points format --- it was transposed before. This required changes in the vertexdeg step. 2. The csr_row_op kernel has been replaced by the adj_to_csr kernel. This kernel can divide the work over multiple blocks even when the number of rows (batch size) is small. It makes optimal use of memory bandwidth because rows of the matrix are laid out contiguously in memory. Authors: - Allard Hendriksen (https://github.com/ahendriksen) - Corey J. Nolet (https://github.com/cjnolet) Approvers: - Corey J. Nolet (https://github.com/cjnolet) - Tamas Bela Feher (https://github.com/tfeher) URL: rapidsai#4803
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Fixes issue #2387.
For large data sizes, the batch size of the DBSCAN algorithm is small in order to fit the distance matrix in memory.
This results in a matrix that has dimensions num_points x batch_size, both for the distance and adjacency matrix.
The conversion of the boolean adjacency matrix to CSR format is performed in the 'adjgraph' step. This step was slow when the batch size was small, as described in issue #2387.
In this commit, the adjgraph step is sped up. This is done in two ways:
The adjacency matrix is now stored in row-major batch_size x num_points format --- it was transposed before. This required changes in the vertexdeg step.
The csr_row_op kernel has been replaced by the adj_to_csr kernel. This kernel can divide the work over multiple blocks even when the number of rows (batch size) is small. It makes optimal use of memory bandwidth because rows of the matrix are laid out contiguously in memory.