BasicTensorBlock |
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DataTensorBlock |
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DenseBlock |
This DenseBlock is an abstraction for different dense, row-major
matrix formats.
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DenseBlockBool |
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DenseBlockDRB |
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DenseBlockFactory |
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DenseBlockFP32 |
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DenseBlockFP64 |
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DenseBlockInt32 |
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DenseBlockInt64 |
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DenseBlockLBool |
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DenseBlockLDRB |
Dense Large Row Blocks have multiple 1D arrays (blocks), which contain complete rows.
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DenseBlockLFP32 |
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DenseBlockLFP64 |
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DenseBlockLInt32 |
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DenseBlockLInt64 |
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DenseBlockLString |
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DenseBlockString |
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IndexedTensorBlock |
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LibTensorAgg |
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LibTensorBincell |
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LibTensorReorg |
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SparseBlock |
This SparseBlock is an abstraction for different sparse matrix formats.
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SparseBlockCOO |
SparseBlock implementation that realizes a traditional 'coordinate matrix'
representation, where the entire sparse block is stored as triples in three arrays:
row indexes, column indexes, and values, where row indexes and colunm indexes are
sorted in order to allow binary search.
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SparseBlockCSR |
SparseBlock implementation that realizes a traditional 'compressed sparse row'
representation, where the entire sparse block is stored as three arrays: ptr
of length rlen+1 to store offsets per row, and indexes/values of length nnz
to store column indexes and values of non-zero entries.
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SparseBlockFactory |
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SparseBlockMCSR |
SparseBlock implementation that realizes a 'modified compressed sparse row'
representation, where each compressed row is stored as a separate SparseRow
object which provides flexibility for unsorted row appends without the need
for global reshifting of values/indexes but it incurs additional memory
overhead per row for object/array headers per row which also slows down
memory-bound operations due to higher memory bandwidth requirements.
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SparseRow |
Base class for sparse row implementations such as sparse
row vectors and sparse scalars (single value per row).
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SparseRowScalar |
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SparseRowVector |
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TensorBlock |
A TensorBlock is the most top level representation of a tensor.
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TensorIndexes |
This represent the indexes to the blocks of the tensor.
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