aws-ip-ranges
cl-cuda
aws-ip-ranges | cl-cuda | |
---|---|---|
14 | 5 | |
258 | 270 | |
- | - | |
9.9 | 0.0 | |
6 days ago | almost 3 years ago | |
Python | Common Lisp | |
GNU General Public License v3.0 or later | MIT License |
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.
aws-ip-ranges
- AWS: IPv4 addresses cost too much, so you’re going to pay
-
AWS outages analysis: Debunking 3 myths and revealing the least reliable region
Specifically, I used a little script I have that made this animation, which is part of my AWS IP Range's repo.
-
How does aws manage public IPv4 addresses allocation
And yeah, this means lots of EC2 instances have their own IP address. This is why AWS hash over 74 million public IPv4 addresses, and why the mass majority of them, something like 75%, are used for EC2.
-
AWS IP Ranges increase of 4,718,592 IPs
Just a heads up, AWS just added 4,718,592 new IPs in their second largest expansion to date.
-
Amazon, Verizon found using IPv4 240/4 addresses
> Moreover, we did not find any 240/4 prefix in the official prefix list shared by Amazon
Yeah, so about that:
https://github.com/seligman/aws-ip-ranges/commit/2e0d9d87d4f...
They did briefly list 252.0.0.0/10 in their published list of IP ranges. The people I spoke with about this at the time either claimed it was a mistake, or the state of the world that I should get used to (it broke some surprisingly fragile scripts on my side for silly reasons).
Given they removed it from their list of IPs 27 hours later, I'm guessing I wasn't the only person freaking out. But yeah, they use it internally, and it leaks from time to time in surprising ways.
-
AWS running out of IPs?
They have north of 66 million IP addresses, so if they're running out, that's an impressive feat.
-
AWS, Azure, GCP region / instance type data
History of AWS's IP Ranges, and a more recent history/comparison of the size of IP Pools of different Cloud Providers
-
[OC] Amazon's AWS size based off IP allocation
This is sourced from a document that AWS publishes, called ip-ranges.json, as archived in my GitHub repo, with a little help from the Internet Archive's Wayback Machine for the first year or so of data before I was archiving changes via a script.
-
AWS just bought 5.5 Million IP addresses
According to https://github.com/seligman/aws-ip-ranges, they have acquired 5,505,024 IPv4 address on Aug 12. This apparently their largest purchase to date, and puts them in control of 1.75% of all IPv4 address. In the world.
-
Hacker News top posts: Aug 14, 2021
AWS adds an extra 5.5M IPv4 addresses\ (146 comments)
cl-cuda
-
Why Lisp? (2015)
> You can write a lot of macrology to get around it, but there's a point where you want actual compiler writers to be doing this
this is not the job of compiler writers (although writing macros is akin to writing a compiler but i do not think that this is what you mean). in julia the numerical programming packages are not part of the standard library and a lot of it is wrappers around C++ code especially when the drivers to the underlining hardware are closed-source [0]. also here is the similar library in common lisp [1]
[0] https://github.com/JuliaGPU/CUDA.jl
[1] https://github.com/takagi/cl-cuda
- Fast and Elegant Clojure: Idiomatic Clojure without sacrificing performance
-
Hacker News top posts: Aug 14, 2021
A Common Lisp Library to Use Nvidia CUDA\ (0 comments)
- A Common Lisp Library to Use Nvidia CUDA
-
Machine Learning in Lisp
Personally, I've been relying on the stream-based method using py4cl/2, mostly because I did not - and perhaps do not - have the knowledge and time to dig into the CFFI based method. The limitation is that this would get you less than 10000 python interactions per second. That is sufficient if you will be running a long running python task - and I have successfully run trivial ML programs using it, but any intensive array processing gets in the way. For this later task, there are a few emerging libraries like numcl and array-operations without SIMD (yet), and numericals using SIMD. For reasons mentioned on the readme, I recently cooked up dense-arrays. This has interchangeable backends and can also use cl-cuda. But barring that, the developer overhead of actually setting up native-CFFI ecosystem is still too high, and I'm back to py4cl/2 for tasks beyond array processing.
What are some alternatives?
rewrite - Automated mass refactoring of source code.
numcl - Numpy clone in Common Lisp
cloud_sizes - Track the sizes of different cloud providers
criterium - Benchmarking library for clojure
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
numericals - CFFI enabled SIMD powered simple-math numerical operations on arrays for Common Lisp [still experimental]
cloud - cloud region / instance type data
py4cl - Call python from Common Lisp
devops-exercises - Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions
hash-array-mapped-trie - A hash array mapped trie implementation in c.
LoopVectorization.jl - Macro(s) for vectorizing loops.