4 ms·
Presumably that depends on maximum PCIe bandwidth consumption before your workload bottlenecks elsewhere? A 2018 benchmark (https://www.pugetsystems.com/labs/hp
by Reelin 6y ago
Presumably that depends on maximum PCIe bandwidth consumption before your workload bottlenecks elsewhere? A 2018 benchmark (https://www.pugetsystems.com/labs/hpc/PCIe-X16-vs-X8-with-4-x-Titan-V-GPUs-for-Machine-Learning-1167/ https://www.pugetsystems.com/labs/hpc/PCIe-X16-vs-X8-with-4-...) seems to indicate that x8 isn't generally a bottleneck for common (at the time) workloads. x8 is a far cry from the claimed gigabit ethernet though!
- p1esk 6y agoAWS is tricky in terms of how storage is provisioned - I don't remember details, but it's easy to put your datasets on storage that is connected to your GPU servers over 1Gb link. That could easily become a bottleneck. Datasets should live on Elastic Block Storage or something like that, over high speed links. Again, it's been a while since I looked into that, so I don't remember the details.
- Reelin 6y agoThe earlier comment claimed that the GPUs (!!!) were located elsewhere on the network; I suspect that the scenario you describe is what they intended to refer to. (IIRC AWS offers compute optimized instances with a volume that's guaranteed to be backed by blocks on a local NVMe drive.)
- nl 6y agoI think they are confused with AWS Elastic Inference. That is a different thing which does have network attached accelerators: Amazon Elastic inference accelerators are GPU-powered hardware devices that are designed to work with any EC2 instance, Sagemaker instance, or ECS task to accelerate deep learning inference workloads at a low cost. When you launch an EC2 instance or an ECS task with Amazon Elastic Inference, an accelerator is provisioned and attached to the instance over the network. https://aws.amazon.com/machine-learning/elastic-inference/faqs/ https://aws.amazon.com/machine-learning/elastic-inference/fa...