dpctl
Python SYCL bindings and SYCL-based Python Array API library (by IntelPython)
ParallelReductionsBenchmark
Thrust, CUB, TBB, AVX2, CUDA, OpenCL, OpenMP, SyCL - all it takes to sum a lot of numbers fast! (by ashvardanian)
| dpctl | ParallelReductionsBenchmark | |
|---|---|---|
| 1 | 2 | |
| 96 | 60 | |
| - | - | |
| 9.8 | 4.7 | |
| 4 days ago | 16 days ago | |
| C++ | C++ | |
| Apache License 2.0 | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dpctl
Posts with mentions or reviews of dpctl.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-11-24.
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Data Parallel Extensions for Python: near-native speed for scientific computing
Considering how poorly it seems to support cuda as a backend [0], I wouldn't hold my breath about non intel vendor support (amd cpu or gpu). As for less common gpus, there really is no good support in any library. If you ever want to go down a fun rabbit hole, try to use the gpu in a raspberry pi for something. You'll eventually find one guy who reverse engineered the drivers to make a compiler but that's it.
[0] https://github.com/IntelPython/dpctl/discussions/1124
ParallelReductionsBenchmark
Posts with mentions or reviews of ParallelReductionsBenchmark.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-02-02.
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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.