MatX
ParallelReductionsBenchmark
MatX | ParallelReductionsBenchmark | |
---|---|---|
7 | 2 | |
1,117 | 59 | |
1.2% | - | |
9.1 | 4.6 | |
3 days ago | 5 months ago | |
C++ | C++ | |
BSD 3-clause "New" or "Revised" License | - |
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MatX
- An efficient C++17 GPU numerical computing library with Python-like syntax
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MatX: Efficient C++17 GPU numerical computing library with Python-like syntax
Hi, what specifically are you looking to benchmark on the K80? Users are free to contribute and we've had many external PRs.
Contribution guide is here: https://github.com/NVIDIA/MatX/blob/main/CONTRIBUTING.md
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Blaze: High Performance Mathematics In C++
For GPU support take a look at our library:
https://github.com/NVIDIA/MatX
If anything is missing we're happy to take feature requests.
- C++ for scientific programming?
ParallelReductionsBenchmark
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Failing to Reach 204 GB/S DDR4 Bandwidth
For the single threaded version, they have a data hazard on the sums that could be smoothed out with a little loop unrolling and separate variables.
But in the [threaded version](https://github.com/unum-cloud/ParallelReductions/blob/fd16d9...) they have separate slots for an accumulator but it's still in a shared vector, which most likely has the issue I described.
What are some alternatives?
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