Scalable Vector Extensions (SVE) is ARM’s latest SIMD extension to their instruction set, which was announced back in 2016. A follow-up SVE2 extension was announced in 2019, designed to incorporate all functionality from ARM’s current primary SIMD extension, NEON (aka ASIMD).
Despite being announced 5 years ago, there is currently no generally available CPU which supports any form of SVE (which excludes the [Fugaku supercomputer](https://www.fujitsu.com/global/about/innovation/
Whenever the topic of Bitcoin's energy usage comes up, there's always a flood of hastily-constructed comments by people claiming that their favourite cryptocurrency isn't like Bitcoin, that their favourite cryptocurrency is energy-efficient and scalable and whatnot.
They're wrong, and are quite possibly trying to scam you. Let's look at why.
There are plenty of intricate and complex articles trying to convince you that cryptocurrencies are the future. They usually heavily use jargon and vague terms, make vague promises, and generally give you a sense that there must be something there, but you always come away from them more confused than you were before.
You might want to read this to get an introduction to armel vs armhf.
If the below is too much, you can try Ubuntu-ARMv7-Qemu but note it contains non-free blobs.
First, cross-compile user programs with GCC-ARM toolchain. Then install qemu-arm-static so that you can run ARM executables directly on linux
If there's no qemu-arm-static in the package list, install qemu-user-static instead
| # Download installers | |
| mkdir ~/Downloads/nvidia | |
| cd ~/Downloads/nvidia | |
| wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run | |
| wget http://us.download.nvidia.com/XFree86/Linux-x86_64/384.59/NVIDIA-Linux-x86_64-384.59.run | |
| sudo chmod +x NVIDIA-Linux-x86_64-384.59.run | |
| sudo chmod +x cuda_8.0.61_375.26_linux-run | |
| ./cuda_8.0.61_375.26_linux-run -extract=~/Downloads/nvidia/ | |
| # Uninstall old stuff | |
| sudo apt-get --purge remove nvidia-* |
| #include <tf/tf.h> | |
| #include <nav_msgs/Odometry.h> | |
| #include <geometry_msgs/Pose2D.h> | |
| ros::Publisher pub_pose_; | |
| void odometryCallback_(const nav_msgs::Odometry::ConstPtr msg) { | |
| geometry_msgs::Pose2D pose2d; | |
| pose2d.x = msg->pose.pose.position.x; | |
| pose2d.y = msg->pose.pose.position.y; |
This was tested on a ThinkPad P70 laptop with an Intel integrated graphics and an NVIDIA GPU:
lspci | egrep 'VGA|3D'
00:02.0 VGA compatible controller: Intel Corporation Device 191b (rev 06)
01:00.0 VGA compatible controller: NVIDIA Corporation GM204GLM [Quadro M3000M] (rev a1)
A reason to use the integrated graphics for display is if installing the NVIDIA drivers causes the display to stop working properly.
In my case, Ubuntu would get stuck in a login loop after installing the NVIDIA drivers.
This happened regardless if I installed the drivers from the "Additional Drivers" tab in "System Settings" or the ppa:graphics-drivers/ppa in the command-line.
| """ | |
| A Python implementation of NNLS algorithm | |
| References: | |
| [1] Lawson, C.L. and R.J. Hanson, Solving Least-Squares Problems, Prentice-Hall, Chapter 23, p. 161, 1974. | |
| Contributed by Klaus Schuch (schuch@igi.tugraz.at) | |
| based on MATLAB's lsqnonneg function | |
| """ |
Orthodox C++ (sometimes referred as C+) is minimal subset of C++ that improves C, but avoids all unnecessary things from so called Modern C++. It's exactly opposite of what Modern C++ suppose to be.
| #!/usr/bin/python | |
| #-*- coding: latin-1 -*- | |
| """This module contains pure Python implementations of the | |
| Levenberg-Marquardt algorithm for data fitting. | |
| """ | |
| import numpy | |
| from numpy import inner, max, diag, eye, Inf, dot | |
| from numpy.linalg import norm, solve |
