GPU implementation of hamming distance #541
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… into numba_hamming
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… into numba_hamming
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…tcrdist distance metrics
… into numba_hamming
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… into numba_hamming
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… into numba_hamming
…curing in all sequences
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…into gpu_hamming
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…into gpu_hamming
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…ceCalculator` in documentation
…into gpu_hamming
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grst
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Feb 9, 2025
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| gpu_n_blocks: | ||
| Number of blocks in which the final result matrix should be computed. Each block reserves GPU memory | ||
| in which the computed result block has to fit in sparse representation. Lower values give better performance | ||
| but increase the risk of running out of reserved memory. This value should be chosen based on the | ||
| estimated sparsity of the result matrix and the size of the GPU device memory. | ||
| gpu_block_width: | ||
| Maximum width of blocks in which the final result matrix should be computed. Each block reserves GPU memory | ||
| in which the computed result block has to fit in sparse representation. Higher values allow for a lower | ||
| number of result blocks (gpu_n_blocks) which increases the performances. This value should be chosen based on | ||
| the GPU device memory. |
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Why do we need both? Can't you infer one based on the other?
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| block_offset = start_column | ||
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| print( |
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I suggest to use logging.info instead of print
| seqs_mat1, seqs_L1 = _seqs2mat_fast(seqs, max_len=max_seq_len) | ||
| seqs_mat2, seqs_L2 = _seqs2mat_fast(seqs2, max_len=max_seq_len) | ||
| except UnicodeError: | ||
| print( |
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I suggest to use logging.info instead of print
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Hamming distance implementation with numba.cuda for GPU support.
This is built on top of the changes in Hamming distance implementation with Numba #512
@grst: