sipherhex

Disclosure: I work at NVIDIA.

This is a developing area with a number of research and development efforts ongoing looking at basic packet filtering flows, for example: https://ieeexplore.ieee.org/document/7019193 https://arxiv.org/pdf/1312.4188.pdf

and AI-driven approaches like CyBERT showing promise: https://medium.com/rapids-ai/cybert-28b35a4c81c4

One of the main system challenges has been efficient high-bandwidth and low-latency delivery of packet flows between NICs and GPUs. NVIDIA has their cuVNF library (part of the Aerial SDK) that works with GPU Direct NICs and extends the DPDK toolkit to accomplish this: https://developer.nvidia.com/aerial-sdk

However, as for integrated devices, I'm familiar with at least one that claims to be on the market: http://www.h3c.com/en/About_Us/News___Events/News/201907/121...

The kicker is the winds never arrived.

Winds over 60mph on the ridges. https://www.wunderground.com/dashboard/pws/KCALOSGA177/graph...

We had 40+mph winds at our home all night. Based on previous experience the power would have gone out anyhow.

In the spring we had a tree take down the power line in front of the house and continue arcing. Thank goodness it was a day after rain and calm wind. On a night like this recent night, the mountain would have been ash.

For those that live in rural, fire-prone areas power outages are often fairly normal and the big danger is burning. If someone hears 4th of July fireworks being set off where I live there's a real risk of a vigilante mob. The tradeoff of power cuts seems reasonable.

There has been reporting that preventive power cuts are generally a good idea, but highly unpopular unless they are really targeted. This is what San Diego Gas & Electric learned: put in equipment to enable more targeted cuts and monitoring. I expect this is where PG&E will need to invest next.

There are many good reasons to embrace better integration with open source. I think the benefits like helping users with custom/broad distro needs and increasing the velocity of collaboration with the ecosystem and partners outweighs factors like 'competition.'

The cogs do turn, albeit slower than people prefer.

https://www.phoronix.com/scan.php?page=news_item&px=NVIDIA-O...

The first part of your statement is generally accurate. However, the article is discussing tariffs the US has chosen. This is not something China is doing.

Be careful with the non-CUDA 9 AMIs.

CUDA 8 programs will run, but terribly slowly as they JIT their GPU code without optimization for Volta. You want the CUDA 9 AMI version (https://aws.amazon.com/marketplace/pp/B076TGJHY1?qid=1509090...), but it currently only has MXNet and TF.

If you need other frameworks there's the NVIDIA AMI (https://aws.amazon.com/marketplace/pp/B076K31M1S?qid=1509090...) and Volta optimized containers for NVCaffe, Caffe2, CNTK, Digits, MXNet, PyTorch, TensorFlow, Theano, Torch, CUDA 9/CuDNN7/NCCL.

Chris from NV here. You can also get a full compliment of DL framework containers, as well as CUDA 9/CuDNN 7/NCCL 2 base container, optimized for Volta by NVIDIA via this AMI https://aws.amazon.com/marketplace/pp/B076K31M1S?qid=1509089...

Given the description of your workaround, you may be running into an issue of long GPU driver load times for the nvidia driver. If that is the root cause the recommended solution for this is nvidia-persistenced, though it seems you may have rolled your own equivalent.

https://github.com/NVIDIA/nvidia-persistenced

Docs here http://docs.nvidia.com/deploy/driver-persistence/index.html

"With independent, parallel integer and floating point datapaths, the Volta SM is also much more efficient on workloads with a mix of computation and addressing calculations"

https://devblogs.nvidia.com/parallelforall/inside-volta/

Under "New SM" in "Key Features" section

nvprof is still a command line profiler, and has even more features.

Disclaimer: I work in the GPU industry.

If you're interested in the architecture of a GPU this Berkeley ParLab presentation by Andy Glew from 2009 covers the basics of how the compute cores in modern GPUs handle threading. It's a subtle, but powerful, difference from SIMD or vector machines.

http://parlab.eecs.berkeley.edu/sites/all/parlab/files/20090...

If you want to get into the details of how a GPU interfaces with the system and OS software, which is almost an entirely other animal, you may want to look at the Nouveau project to get oriented.

http://nouveau.freedesktop.org/wiki/