x86 Paging Tutorial TLB replacement policy Updated 2025-07-16
When TLB is filled up, older addresses are overwritten. Just like CPU cache, the replacement policy is a potentially complex operation, but a simple and reasonable heuristic is to remove the least recently used entry (LRU).
With LRU, starting from state:adding
valid linear physical
----- ------ --------
> 1 00003 00005
1 00007 00009
1 00009 00001
1 0000B 000030000D -> 0000A would give: valid linear physical
----- ------ --------
1 0000D 0000A
> 1 00007 00009
1 00009 00001
1 0000B 00003 Internet privacy organizations Updated 2025-07-16
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Amazing self-directed learning direction:
world.hey.com/gwyn/no-excuses-bc4152fb mentions that the founder was inspired by other schools: High Tech High and Expeditionary Learning.
Lots of focus on showcase student work.
The founder Gwyn ap Harri is quite dirty mouthed, which is also cool.
Ciro Santilli tried to contact them in 2021 at: twitter.com/cirosantilli/status/1448924419016036353 and on website contact form to see if we could do some project together, but no reply.
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The main reason Ciro Santilli never touched it is that it feels that every public data set has already been fully mined or has already had the most interesting algorithms developed for it, so you can't do much outside of big companies.
This is why Ciro started Ciro's 2D reinforcement learning games to generate synthetic data and thus reduce the cost of data.
The other reason is that it is ugly.
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- 2020: Traininum in 2020, e.g. techcrunch.com/2020/12/01/aws-launches-trainium-its-new-custom-ml-training-chip/
- 2018: AWS Inferentia, mentioned at en.wikipedia.org/wiki/Annapurna_Labs
Amazon EC2 GPU Updated 2025-07-16
As of December 2023, the cheapest instance with an Nvidia GPU is g4nd.xlarge, so let's try that out. In that instance, lspci contains:so we see that it runs a Nvidia T4 GPU.
00:1e.0 3D controller: NVIDIA Corporation TU104GL [Tesla T4] (rev a1)Be careful not to confuse it with g4ad.xlarge, which has an AMD GPU instead. TODO meaning of "ad"? "a" presumably means AMD, but what is the "d"?
Some documentation on which GPU is in each instance can seen at: docs.aws.amazon.com/dlami/latest/devguide/gpu.html (archive) with a list of which GPUs they have at that random point in time. Can the GPU ever change for a given instance name? Likely not. Also as of December 2023 the list is already outdated, e.g. P5 is now shown, though it is mentioned at: aws.amazon.com/ec2/instance-types/p5/
When selecting the instance to launch, the GPU does not show anywhere apparently on the instance information page, it is so bad!
Also note that this instance has 4 vCPUs, so on a new account you must first make a customer support request to Amazon to increase your limit from the default of 0 to 4, see also: stackoverflow.com/questions/68347900/you-have-requested-more-vcpu-capacity-than-your-current-vcpu-limit-of-0, otherwise instance launch will fail with:
You have requested more vCPU capacity than your current vCPU limit of 0 allows for the instance bucket that the specified instance type belongs to. Please visit aws.amazon.com/contact-us/ec2-request to request an adjustment to this limit.
When starting up the instance, also select:Once you finally managed to SSH into the instance, first we have to install drivers and reboot:and now running:shows something like:
- image: Ubuntu 22.04
- storage size: 30 GB (maximum free tier allowance)
sudo apt update
sudo apt install nvidia-driver-510 nvidia-utils-510 nvidia-cuda-toolkit
sudo rebootnvidia-smi+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.147.05 Driver Version: 525.147.05 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla T4 Off | 00000000:00:1E.0 Off | 0 |
| N/A 25C P8 12W / 70W | 2MiB / 15360MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+If we start from the raw Ubuntu 22.04, first we have to install drivers:
- docs.aws.amazon.com/AWSEC2/latest/UserGuide/install-nvidia-driver.html official docs
- stackoverflow.com/questions/63689325/how-to-activate-the-use-of-a-gpu-on-aws-ec2-instance
- askubuntu.com/questions/1109662/how-do-i-install-cuda-on-an-ec2-ubuntu-18-04-instance
- askubuntu.com/questions/1397934/how-to-install-nvidia-cuda-driver-on-aws-ec2-instance
From there basically everything should just work as normal. E.g. we were able to run a CUDA hello world just fine along:
nvcc inc.cu
./a.outOne issue with this setup, besides the time it takes to setup, is that you might also have to pay some network charges as it downloads a bunch of stuff into the instance. We should try out some of the pre-built images. But it is also good to know this pristine setup just in case.
We then managed to run Ollama just fine with:which gave:so way faster than on my local desktop CPU, hurray.
curl https://ollama.ai/install.sh | sh
/bin/time ollama run llama2 'What is quantum field theory?'0.07user 0.05system 0:16.91elapsed 0%CPU (0avgtext+0avgdata 16896maxresident)k
0inputs+0outputs (0major+1960minor)pagefaults 0swapsAfter setup from: askubuntu.com/a/1309774/52975 we were able to run:which gave:so only marginally better than on P14s. It would be fun to see how much faster we could make things on a more powerful GPU.
head -n1000 pap.txt | ARGOS_DEVICE_TYPE=cuda time argos-translate --from-lang en --to-lang fr > pap-fr.txt77.95user 2.87system 0:39.93elapsed 202%CPU (0avgtext+0avgdata 4345988maxresident)k
0inputs+88outputs (0major+910748minor)pagefaults 0swaps Animal intelligence Updated 2025-07-16
Stephen Hawking Updated 2025-07-16
While learning black-hole stuff is not on top of Ciro Santilli's priorities, Hawking's spirit is to be admired.
To never give up even when everything seems lost, and still have a sense of humour is respectable.
An ex-physicist colleague who had met Hawking told an anecdote. Hawking was around in the department one day, they said hi and all. But then Hawking wanted to tell a joke. It took like 5 minutes of typing, and you can imagine that things were pretty awkward and the joke's timing was "a bit off". But Hawking did tell the joke nonetheless.
This is also suggested in the The Theory of Everything (2014) film, and therefore likely the biographies.
Amazon Web Services Updated 2025-07-16
There are unlisted articles, also show them or only show them.