I screwed up using git ("git checkout --" on the wrong file) and managed to delete the code I had just written... but it was still running in a process in a docker container. Here's how I got it back, using https://pypi.python.org/pypi/pyrasite/ and https://pypi.python.org/pypi/uncompyle6
apt-get update && apt-get install gdb
FWIW: I (@rondy) am not the creator of the content shared here, which is an excerpt from Edmond Lau's book. I simply copied and pasted it from another location and saved it as a personal note, before it gained popularity on news.ycombinator.com. Unfortunately, I cannot recall the exact origin of the original source, nor was I able to find the author's name, so I am can't provide the appropriate credits.
Product: Sagitta Brutalis 1080 (PN S3480-GTX-1080-2697-128)
Software: Hashcat v3.00-beta-145-g069634a, Nvidia driver 367.18
Accelerator: 8x Nvidia GTX 1080 Founders Edition
This is a bash script, as an example, on how to do click-testing GUI based on finding components based on how they look.
... or Why Pipelining Is Not That Easy
Golang Concurrency Patterns for brave and smart.
By @kachayev
As of version 3.3, python includes the very promising concurrent.futures module, with elegant context managers for running tasks concurrently. Thanks to the simple and consistent interface you can use both threads and processes with minimal effort.
For most CPU bound tasks - anything that is heavy number crunching - you want your program to use all the CPUs in your PC. The simplest way to get a CPU bound task to run in parallel is to use the ProcessPoolExecutor, which will create enough sub-processes to keep all your CPUs busy.
We use the context manager thusly:
with concurrent.futures.ProcessPoolExecutor() as executor:
L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns
Compress 1K bytes with Zippy ............. 3,000 ns = 3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns = 20 µs
SSD random read ........................ 150,000 ns = 150 µs
Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs