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Real-world networking datasets

Please comment below if you have an update, e.g., with another networking-related dataset.

Dataset repos or tools

Finding datasets: New Google Dataset search
Mendeley Data: https://data.mendeley.com/datasets
Kaggle Data: https://www.kaggle.com/datasets
Google's M-Lab networking performance data sets: https://www.measurementlab.net/data/

Overview

Syncing an Ethereum node is largely reliant on IOPS, I/O Per Second. Budget SSDs will struggle to an extent, and some won't be able to sync at all.

This document aims to snapshot some known good and known bad models.

For size, 4TB comes recommended as of mid 2024. The smaller 2TB drive should last an Ethereum full node until early 2025 or thereabouts, with crystal ball uncertainty. Remy wrote a migration guide to 4TB.

High-level, QLC and DRAMless are far slower than "mainstream" SSDs. QLC has lower endurance as well. Any savings will be gone when the drive fails early and needs to be replaced.

	import sys
	import re
	import pefile
	import string
	import struct
	from capstool import CapsTool
	from capstone import *
	from capstone.x86 import *

	BCC = ["je", "jne", "js", "jns", "jp", "jnp", "jo", "jno", "jl", "jle", "jg",

	In shader programming, you often run into a problem where you want to iterate an array in memory over all pixels in a compute shader
	group (tile). Tiled deferred lighting is the most common case. 8x8 tile loops over a light list culled for that tile.

	Simplified HLSL code looks like this:

	Buffer<float4> lightDatas;
	Texture2D<uint2> lightStartCounts;
	RWTexture2D<float4> output;

	[numthreads(8, 8, 1)]

	#
	# CMake build system for Dear ImGui
	# =================================
	#
	# Build instructions:
	# 1. Install latest CMake
	# * Windows: https://cmake.org/download/ (Tick checkbox to place cmake in system PATH)
	# * Linux: from your favorite package manager
	# * MacOS: brew install cmake
	# 2. Open command prompt in directory containing "imgui" and "imgui_dev" folders

	# install DSPy: pip install dspy
	import dspy

	# Ollam is now compatible with OpenAI APIs
	#
	# To get this to work you must include `model_type='chat'` in the `dspy.OpenAI` call.
	# If you do not include this you will get an error.
	#
	# I have also found that `stop='\n\n'` is required to get the model to stop generating text after the ansewr is complete.
	# At least with mistral.

	import struct

	import fastlz

	file = open("/home/dexter/sc/snoita/save00/world/world_0_0.png_petri", "rb")
	file_content = file.read()

	# This uses little-endian, the rest of the file uses big-endian
	compressed_size, uncompressed_size = struct.unpack("<ii", file_content[0:8])

	# based on the following:
	# http://saintgimp.org/2013/01/22/merging-two-git-repositories-into-one-repository-without-losing-file-history/
	# http://blog.caplin.com/2013/09/18/merging-two-git-repositories/

	git clone repo_main
	git clone repo_sub
	cd repo_main

	git remote add repo_sub ../repo_sub
	git fetch repo_sub