sudo rsync -avxHAX --progress --exclude={"/dev/*","/proc/*","/sys/*","/tmp/*","/run/*","/mnt/*","/media/*","/lost+found","/home/*/.cache/*","/var/cache/*","/var/tmp/*"} / /path/to/backup/destination/Let's break down this rsync command:
sudo: Run the command with root privileges, necessary for accessing all system files.
rsync: The command itself.
| /* This file is written by chatgpt */ | |
| #include <math.h> | |
| #include <stdio.h> | |
| #include <stdlib.h> | |
| #include <string.h> | |
| #define MAX_SEQUENCE_LENGTH 512 ///< Maximum sequence length for input data | |
| #define MAX_EMBEDDING_DIM 512 ///< Maximum embedding dimension for the model |
$ watch ps -e -o pid,user,%mem,cmd --sort=-%mem
| #pragam once | |
| // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |
| // Conversion algorithm from: https://en.wikipedia.org/wiki/YUV | |
| // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ | |
| constexpr int ConvertYUYV_2_BGR(const int nWidth, const int nHeight, | |
| u_char *pPixSrc, u_char *pPixDst) { | |
| if (nullptr == pPixSrc || nullptr == pPixDst) { | |
| std::cerr << "FAIL: Cannot convert YUYV to BGR from/to NULL pixel buffers" | |
| << std::endl; | |
| return -1; |
| #!/bin/bash | |
| # | |
| # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved. | |
| # | |
| # NVIDIA Corporation and its licensors retain all intellectual property | |
| # and proprietary rights in and to this software, related documentation | |
| # and any modifications thereto. Any use, reproduction, disclosure or | |
| # distribution of this software and related documentation without an express | |
| # license agreement from NVIDIA Corporation is strictly prohibited. | |
| # |
First your OpenCV should be compiled with CUDA (and OpenGL) support to test all this features. Detect your CUDA hardware with OpenCV CUDA by:
#include <iostream>
using namespace std;
#include <opencv2/core.hpp>
using namespace cv;
#include <opencv2/cudaarithm.hpp>
using namespace cv::cuda;
| #include <atomic> | |
| #include <iostream> | |
| #include <thread> | |
| #include <unistd.h> | |
| // A simple timer that use atomic bool to identify it's status. | |
| struct Timer { | |
| bool overdue() { return flag_.load() ? true : false; } | |
| void reset_timer() { |
from https://www.brendangregg.com/Articles/Netflix_Linux_Perf_Analysis_60s.pdf
uptime # Load averages to identify if load is increasing or decreasing (compare 1-, 5-, and 15-minute averages).
dmesg -T | tail # Kernel errors including OOM events.
vmstat -SM 1 # System-wide statistics: run queue length, swapping, overall CPU usage.
mpstat -P ALL 1 # Per-CPU balance: a single busy CPU can indicate poor thread scaling.
pidstat 1 # Per-process CPU usage: identify unexpected CPU consumers, and user/system CPU time for each process.
iostat -sxz 1 # Disk I/O statistics: IOPS and throughput, average wait time, percent busy.
In this article I’ll tell you about my pure functional library for Software Transactional Memory (STM) that I’ve built in C++. I adopted some advanced functional programming concepts that make it composable and convenient to use. Its implementation is rather small and robust, which differentiates the library from competitors. Let’s discuss what STM is and how to use it.
| - Boost C++ Libraries (https://www.boost.org/) | |
| - Facebook's Folly (https://github.com/facebook/folly) | |
| - Electronic Arts' EASTL (https://github.com/electronicarts/EASTL) | |
| - Bloomberg's BDE (https://github.com/bloomberg/bde) | |
| - Google's Abseil (https://abseil.io/) | |
| - The Awesome Cpp list (https://github.com/fffaraz/awesome-cpp) with | |
| dozens more |