Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000
RDBMS-based job queues have been criticized recently for being unable to handle heavy loads. And they deserve it, to some extent, because the queries used to safely lock a job have been pretty hairy. SELECT FOR UPDATE followed by an UPDATE works fine at first, but then you add more workers, and each is trying to SELECT FOR UPDATE the same row (and maybe throwing NOWAIT in there, then catching the errors and retrying), and things slow down.
On top of that, they have to actually update the row to mark it as locked, so the rest of your workers are sitting there waiting while one of them propagates its lock to disk (and the disks of however many servers you're replicating to). QueueClassic got some mileage out of the novel idea of randomly picking a row near the front of the queue to lock, but I can't still seem to get more than an an extra few hundred jobs per second out of it under heavy load.
So, many developers have started going straight t
Rich Hickey • 3 years ago
Sorry, I have to disagree with the entire premise here.
A wide variety of experiences might lead to well-roundedness, but not to greatness, nor even goodness. By constantly switching from one thing to another you are always reaching above your comfort zone, yes, but doing so by resetting your skill and knowledge level to zero.
Mastery comes from a combination of at least several of the following:
A list of useful commands for the FFmpeg command line tool.
Download FFmpeg: https://www.ffmpeg.org/download.html
Full documentation: https://www.ffmpeg.org/ffmpeg.html
""" | |
Train a neural network to implement the discrete Fourier transform | |
""" | |
from tensorflow.keras.models import Sequential | |
from tensorflow.keras.layers import Dense | |
import numpy as np | |
import matplotlib.pyplot as plt | |
N = 32 | |
batch = 10000 |
There are multiple ways to get a full disk encrypted arch linux system on raspberry. In this tutorial, we will install a 64-bit arch linux armv8 system, using dropbear as ssh server for remote pre-boot unlocking of the root filesystem. However, it will still be possible to unlock and use the pi as usual, with a keyboard and monitor. We will also create an unencrypted partition in the installation process, usable as a rescue system.
Differences to the 32-bit arch linux arm version:
This document contains excerpts from my web server logs collected over a period of 7 years that shows various kinds of recon and attack vectors.
There were a total of 37.2 million lines of logs out of which 1.1 million unique HTTP requests (Method + URI) were found.
$ sed 's/^.* - - \[.*\] "\(.*\) HTTP\/.*" .*/\1/' access.log > requests.txt
This document was originally written several years ago. At the time I was working as an execution core verification engineer at Arm. The following points are coloured heavily by working in and around the execution cores of various processors. Apply a pinch of salt; points contain varying degrees of opinion.
It is still my opinion that RISC-V could be much better designed; though I will also say that if I was building a 32 or 64-bit CPU today I'd likely implement the architecture to benefit from the existing tooling.
Mostly based upon the RISC-V ISA spec v2.0. Some updates have been made for v2.2
The RISC-V ISA has pursued minimalism to a fault. There is a large emphasis on minimizing instruction count, normalizing encoding, etc. This pursuit of minimalism has resulted in false orthogonalities (such as reusing the same instruction for branches, calls and returns) and a requirement for superfluous instructions which impacts code density both in terms of size and