Tutorial by Examples: u

Run repair on a particular partition range. nodetool repair -pr Run repair on the whole cluster. nodetool repair Run repair in parallel mode. nodetool repair -par

Assuming you know the productID: First import StoreKit Then in your code let productID: Set = ["premium"] let request = SKProductsRequest(productIdentifiers: productID) request.delegate = self request.start() and in the SKProductsRequestDelegate: func productsRequest(request: ...

buildscript { repositories { jcenter() } dependencies { classpath 'io.spring.gradle:dependency-management-plugin:0.5.4.RELEASE' } } apply plugin: 'io.spring.dependency-management' apply plugin: 'idea' apply plugin: 'java' dependencyManagement { imp...

int get_l2cap_connection () { First off, all the variables we need, explanation for will follow at the appropriate spot. int ssock = 0; int csock = 0; int reuse_addr = 1; struct sockaddr_l2 src_addr; struct bt_security bt_sec; int result = 0; First, we need to cre...

Usually, J and K move up and down file lines. But when you have wrapping on, you may want them to move up and down the displayed lines instead. set wrap " if you haven't already set it nmap j gj nmap k gk

set mouse=a This will enable mouse interaction in the vim editor. The mouse can change the current cursor's position select text

Python Code import numpy as np import cv2 #loading haarcascade classifiers for face and eye #You can find these cascade classifiers here #https://github.com/opencv/opencv/tree/master/data/haarcascades #or where you download opencv inside data/haarcascades face_cascade = cv2.CascadeClassi...

Image img = new Image(); BitmapImage bitmap = new BitmapImage(new Uri("ms-appx:///Path-to-image-in-solution-directory", UriKind.Absolute)); img.Source = bitmap;

public override async void ViewDidLoad(){ base.ViewDidLoad(); // Perform any additional setup after loading the view, typically from a nib. Title = "Pull to Refresh Sample"; table = new UITableView(new CGRect(0, 20, View.Bounds.Width, View.Bounds.Height - 20)); //table.Aut...

The simplest approach to parallel reduction in CUDA is to assign a single block to perform the task: static const int arraySize = 10000; static const int blockSize = 1024; __global__ void sumCommSingleBlock(const int *a, int *out) { int idx = threadIdx.x; int sum = 0; for (int i ...

Doing parallel reduction for a non-commutative operator is a bit more involved, compared to commutative version. In the example we still use a addition over integers for the simplicity sake. It could be replaced, for example, with matrix multiplication which really is non-commutative. Note, when ...

Multi-block approach to parallel reduction in CUDA poses an additional challenge, compared to single-block approach, because blocks are limited in communication. The idea is to let each block compute a part of the input array, and then have one final block to merge all the partial results. To do t...

Multi-block approach to parallel reduction is very similar to the single-block approach. The global input array must be split into sections, each reduced by a single block. When a partial result from each block is obtained, one final block reduces these to obtain the final result. sumNoncommSin...

Sometimes the reduction has to be performed on a very small scale, as a part of a bigger CUDA kernel. Suppose for example, that the input data has exactly 32 elements - the number of threads in a warp. In such scenario a single warp can be assigned to perform the reduction. Given that warp execut...

Sometimes the reduction has to be performed on a very small scale, as a part of a bigger CUDA kernel. Suppose for example, that the input data has exactly 32 elements - the number of threads in a warp. In such scenario a single warp can be assigned to perform the reduction. Given that warp execut...

Typically, reduction is performed on global or shared array. However, when the reduction is performed on a very small scale, as a part of a bigger CUDA kernel, it can be performed with a single warp. When that happens, on Keppler or higher architectures (CC>=3.0), it is possible to use warp-shu...

Go to Catalog > Filters and select Insert to create a filter group. Assign a filter group name (e.g. Color) and add filter name values (e.g. Blue, Red, Yellow).

Go to Catalog > Products and Edit a product. Under the Links tab add the filters which apply to the product (e.g. Color > Blue). Apply to as many products as applicable.

Go to Extensions > Modules > Filter. If not installed select Install. Click Edit & then Enabled from option & then Save module.

Go to Design > Layout > Edit Category Page and Set whatever position and sort order you would like. and then save.