Comments (2)
Possible workaround: Leave it to the user to convert to CPU Arrays if it needs to index the solution.
We can make that be an option (with a val type). But we can also
The probs creation within the DiffEqGPU seems to be necessary, but maybe it could be pulled out of DiffEqGPU? Currently, it was done to adhere to the DiffEqGPU way of handling it. This was not coming in the previous benchmarks because ps was being built separately and passed to the vectorized_solve.
I think for that, we can have a documented lower level API for people who really want to pull as much speed out as possible. On that note, we should make some real docs.
from diffeqgpu.jl.
Sounds good to me. I will start writing some documentation for it. I can help setting up docs page for it, something aligned with SciMLDocs.
from diffeqgpu.jl.
Related Issues (20)
- Out of dynamic GPU memory in EnsembleGPUKernel for higher number of threads
- Out of Dynamic GPU memory in EnsembleGPUKernel for higher number of threads when using ContinuousCallback HOT 6
- `EnsembleGPUKernel `+ Texture Memory Support HOT 20
- codecov not running
- Performance example Parameter-Parallelism with GPU Ensemble Methods HOT 3
- Example in docs fails HOT 5
- GPU locks up at full usage with EnsembleGPUKernel on tspan ensemble study HOT 2
- Support non-array parameter types HOT 1
- Problem with EnsembleGPUArray() when updating library HOT 4
- Add a tutorial example for usage with MPI
- EnsembleGPUArray() incompatibility with SecondOrderODEProblem() HOT 2
- Python support for DiffEqGPU HOT 4
- `EnsembleGPUArray` compatibility with different time intervals
- Missing Stratonovich SDE solvers HOT 1
- Error when running example code HOT 4
- Does DiffEqGPU support adaptive methods? HOT 5
- Jacobian with respecpect to paramters fails HOT 1
- Improve docstrings
- Tutorial fails using Metal.jl HOT 3
- Callbacks don't work with EnsembleCPUArray() HOT 6
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from diffeqgpu.jl.