This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).
Matrix multiplication is a mathematical operation that defines the product of
| HTTPPort 8090 | |
| HTTPBindAddress 0.0.0.0 | |
| MaxHTTPConnections 200 | |
| MaxClients 100 | |
| MaxBandWidth 500000 | |
| CustomLog - | |
| <Feed camera.ffm> | |
| File /tmp/camera.ffm | |
| FileMaxSize 200M |
| #Evolution Strategies with Keras | |
| #Based off of: https://blog.openai.com/evolution-strategies/ | |
| #Implementation by: Nicholas Samoray | |
| #README | |
| #Meant to be run on a single machine | |
| #APPLY_BIAS is currently not working, keep to False | |
| #Solves Cartpole as-is in about 50 episodes | |
| #Solves BipedalWalker-v2 in about 1000 |
| See my DASH-IF presentation from October, 2014: | |
| https://s3.amazonaws.com/misc.meltymedia/dash-if-reveal/index.html#/ | |
| 1. encode multiple bitrates with keyframe alignment: | |
| ffmpeg -i ~/Movies/5D2_Portrait.MOV -s 1280x720 -c:v libx264 -b:v 1450k -bf 2 \ | |
| -g 90 -sc_threshold 0 -c:a aac -strict experimental -b:a 96k -ar 32000 out.mp4 | |
| My input was 30 fps = 3000 ms. If it were 29.97, then a GOP size of 90 frames will yield a base segment | |
| size of 3003 milliseconds. You can make the segment size some multiple of this, e.g.: 6006, 9009, 12012. |
This tutorial guides you through creating your first Vagrant project.
We start with a generic Ubuntu VM, and use the Chef provisioning tool to:
Afterwards, we'll see how easy it is to package our newly provisioned VM
| # 0 is too far from ` ;) | |
| set -g base-index 1 | |
| # Automatically set window title | |
| set-window-option -g automatic-rename on | |
| set-option -g set-titles on | |
| #set -g default-terminal screen-256color | |
| set -g status-keys vi | |
| set -g history-limit 10000 |
