jboner

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

hellerbarde

Latency numbers every programmer should know

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

biera

docker rm -f `docker ps -aq`

ramn

#!/bin/bash

socat -v -T0.05 tcp-l:8081,reuseaddr,fork system:"echo 'HTTP/1.1 200 OK'; echo 'Connection: close'; echo; cat"

cubarco

package main

import (
	"flag"
	"fmt"
	"net/http"
)

func main() {
	var port string

carlessanagustin

YAML Syntax for Ansible

• http://docs.ansible.com/ansible/YAMLSyntax.html
• http://docs.ansible.com/

Lists

• Option 1:

wolf1986

Tensorflow Serving Tutorial - 01 - Public Inception Model

In this tutorial:

• Start with a blank Ubuntu
• Install requirements
• Download code & pre-tranied inception-v3 model from Google
• Export the downloaded "checkpoint" format to a TF Graph that is servable with Tensorflow Model Serving
• Query the server with images of a cat and a dog

General Notes

• Compiling Tensorflow Serving from source (on docker with their official instructions) produces an internal gcc error (probably specific to tensorflow commit / gcc version used when writing this: tf serving commit: c1ec43508ee57a5d6269116aba82d2a16d383c8a)

nadavrot

High-Performance Matrix Multiplication

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).

Intro

Matrix multiplication is a mathematical operation that defines the product of

mcandre

#!/bin/bash
brew list | while read cask; do echo -ne "\x1B[1;34m $cask \x1B[0m"; brew uses $cask --installed | awk '{printf(" %s ", $0)}'; echo ""; done

mcarilli

Ordinary launch commands (no profiling):

Single-process:

python main_amp.py -a resnet50 --b 224 --deterministic --workers 4 --opt-level O1 ./bare_metal_train_val/

Multi-process:

python -m torch.distributed.launch  --nproc_per_node=2 main_amp.py -a resnet50 --b 224 --deterministic --workers 4 --opt-level O1 ./bare_metal_train_val/