Comments (2)
That function is designed to avoid CUDA's slow malloc where possible, the idea being that doing a GC to free a pointer will be faster than malloc. Of course, for small arrays that may not be true, so you actually want to GC every few allocations (to refill the pool) but not every allocation.
I wrote this allocator by feel, so it would probably be a good idea to get some benchmarks together and get a feel for how it effects them.
from cuarrays.jl.
The new pool allocated is a little more careful about this. It could use some tuning though, please open an issue with a MWE if you hit a pathological case.
from cuarrays.jl.
Related Issues (20)
- similar(PermutedDimsArray(::CuArray)) isa Array HOT 1
- In CuArrays v2.0, GPU operation takes hours to run for the first time HOT 5
- sum!(y::CuVector, x::CuMatrix) throws InvalidIRError error
- Where can I find
- Where can I find All the using instructions of CuArrays? HOT 3
- add implicit float conversion to math functions HOT 4
- Multiplication between mixed types doesn't drop leading dimensions HOT 2
- Very slow 4D broadcast in 2.0.1 HOT 1
- Failed to detect installed CUDA version. HOT 1
- Sum function is slow HOT 8
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- Deadlock during memory free HOT 5
- Indexing CuArrays with Empty Ranges Errors HOT 5
- Sum, any, etc. with function is no longer implemented HOT 7
- Training Halts when Using CuArrarys HOT 6
- CUBLAS initialization HOT 1
- Performance issue with v2.1.0 compared with v1.7.3 HOT 4
- .+ CartesianIndices: InvalidIRError: compiling kernel broadcast HOT 1
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from cuarrays.jl.