1 and 2 is equivalent.
- 1
sharo@kirima:~$ date "+%Y-%m-%dT%H:%M:%S%:z"
2020-05-24T14:38:26+09:00
- 2
sharo@kirima:~$ date --iso-8601='seconds'
0.0.0.0 api.surfeasy.com | |
0.0.0.0 opera-proxy.net | |
0.0.0.0 api.sec-tunnel.com | |
0.0.0.0 sitecheck2.opera.com | |
0.0.0.0 opera-mini.net | |
0.0.0.0 exchange.opera.com | |
0.0.0.0 features.opera-api.com |
#define _GNU_SOURCE | |
// compile: | |
// gcc -m64 -shared -I../Capture_Linux_v7.1.9/NvFBC/inc -fPIC dl_prog3.c -o nvfbc_preload.64.so -ldl | |
// gcc -m64 -shared -I../Capture_Linux_v7.1.9/NvFBC/inc -fPIC dl_prog3.c -o nvfbc_preload.32.so -ldl | |
// Run: | |
// LD_PRELOAD="$PWD/nvfbc_preload.32.so $PWD/nvfbc_preload.64.so" DISPLAY=:0 ../Capture_Linux_v7.1.9/NvFBC/samples/NvFBCToGLEnc/NvFBCToGLEnc -f 10 | |
#include "NvFBC.h" |
NVIDIA Driver Version: 455.23.05 CUDA Version: 11.1 | |
Credit: r4d1x | |
For benchmarking the card and allowing me to release the benchmarks here | |
There are a handful of algorithms failing, mostly appears related to SCRYPT and | |
is liking a tuning issue or small driver issue that we will need to take a look at. | |
Otherwise, seems fairly stable. | |
1 and 2 is equivalent.
sharo@kirima:~$ date "+%Y-%m-%dT%H:%M:%S%:z"
2020-05-24T14:38:26+09:00
sharo@kirima:~$ date --iso-8601='seconds'
#!/bin/sh | |
fricas -nosman <<EOF | |
E := [_ | |
-- Standard Illumination Model for Computers_ | |
--_ | |
-- Is defined as a system of linear equations, where negative_ | |
-- colors don't exist and is solved by computing the point at_ | |
-- which they all intersect the one which needs to be defined_ | |
-- as the Planckian locus of the illuminant._ |
By default Linux ignores Broadcast and Multicast ICMP messages. That's why you need to enable it first:
sysctl -w net.ipv4.icmp_echo_ignore_broadcasts=0
To join any mutlicast address (e.g. 224.10.10.10/24
) just add it to your active interface (e.g. eth0
) and append the keyword autojoin
at the end:
package main | |
import ( | |
"context" | |
"fmt" | |
"net" | |
"os" | |
"os/signal" | |
"syscall" |
In response to this brief blog entry, @antirez tweeted for some documentation on high-performance techniques for Redis. What I present here are general high-performance computing (HPC) techniques. The examples are oriented to Redis. but they work well for any program designed to be single- or worker-threaded and asynchronous (e.g. uses epoll).
The motivation for using these techniques is to maximize performance of our system and services. By isolating work, controlling memory, and other tuning, you can achieve significant reduction in latency and increase in throughput.
My perspective comes from the microcosm of my own bare-metal (vs VM), on-premises deployment. It might not be suitable for all scenarios, especially cloud deployments, as I have little experience with HPC there. After some discussion, maybe this can be adapted as [redis.io documentation](https://redis.io/do
image: docker:latest | |
services: | |
- docker:dind | |
stages: | |
- build | |
variables: | |
IMAGE: registry.gitlab.com/bamnet/njtdata | |
DOCKER_CLI_EXPERIMENTAL: enabled |