Comments (10)
yes, we still need to investigate the bottleneck for (or give an statics about ) GPU computing, such as moving data via PCI v.s. computing gradients. However, the multiprocessor feature will definitely helping accelerate the computing process.
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according to benchmarks in xgboost project, GPU can speed-up building tree severel times (i7-6700K vs. Titan X (pascal)), what is impresive..
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huanzhang12/lightgbm-gpu#1, a community version of gpu
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@huanzhang12, glad to see you have the plan to create PR back. we can have discussion here, and work together to enable GPU training
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@chivee Thanks! I am working on finalizing my GPU code. One thing I need to do right now is to update my branch to the upstream LightGBM master. Since there are a lot of changes in v2 (commit 4f77bd2), I need to make sure I don't break anything. I will let you guys know if I have any questions or concerns. Thanks!
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@huanzhang12 , Sure, feel to ask if you need any help.
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@guolinke @chivee Some quick updates: I have mostly finished upgrading my GPU code to v2: https://github.com/huanzhang12/lightgbm-gpu/tree/v2. I am currently working on implementing the Dense4bitsBin on GPU (which is new in v2), and doing more testing. I hope I can get the code ready to review and merge by the end of this week.
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@huanzhang12 awesome, looking forward to your contribution.
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Refer to #368
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This issue has been automatically locked since there has not been any recent activity since it was closed. To start a new related discussion, open a new issue at https://github.com/microsoft/LightGBM/issues including a reference to this.
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Related Issues (20)
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