| Commit message (Collapse) | Author | Age |
|---|
| ... | |
| | |
|
| | |
|
| | |
|
| | |
|
| |
|
|
| |
function returns a floating point value when called on a complex input.
|
| | |
|
| |
|
|
| |
the case where it is different from the input type (e.g. abs(complex<float>))
|
| | |
|
| |
|
|
| |
Use mempy to speedup tensor copies whenever possible.
|
| | |
|
| |
|
|
|
|
|
|
| |
efficiently compute convolutions and contractions in the future:
* The scheduling of computation is moved out the the assignment code and into a new TensorExecutor class
* The assignment itself is now a regular node on the expression tree
* The expression evaluators start by recursively evaluating all their subexpressions if needed
|
| |
|
|
|
|
| |
partial template specialization to optimize the strategy of each evaluator for each device type.
Started work on partial evaluations.
|
| |
|
|
|
| |
Updated expression evaluation mechanism to also compute the size of the tensor result
Misc fixes and improvements.
|
| |
|
|
|
|
|
|
| |
* comparison (<, <=, ==, !=, ...)
* selection
* nullary ops such as random or constant generation
* misc unary ops such as log(), exp(), or a user defined unaryExpr()
Cleaned up the code a little.
|
| |
|
|
|
| |
Added the ability to parallelize the evaluation of a tensor expression over multiple cpu cores.
Added the ability to offload the evaluation of a tensor expression to a GPU.
|
| |
|
|
| |
Improved support for tensor expressions.
|
|
|
* Added ability to map a region of the memory to a tensor
* Added basic support for unary and binary coefficient wise expressions, such as addition or square root
* Provided an emulation layer to make it possible to compile the code with compilers (such as nvcc) that don't support cxx11.
|
Benoit Steiner