Tutorial by Examples: le

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...

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

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...

drop table table01; drop table table02; create table table01 ( code int, name varchar(50), old int ); create table table02 ( code int, name varchar(50), old int ); truncate table table01; insert into table01 values (1, 'A', 10); insert in...

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 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.

export function myDirective($location: ng.ILocationService): ng.IDirective { return { link: (scope: ng.IScope, element: ng.IAugmentedJQuery, attributes: ng.IAttributes): void => { element.text("Current URL: " + $location.url()); ...

By selecting a matrix and choosing "Insert Table" from the menu, you create a table which allows you to pull and insert data in a structured way. Let's say you have named the table "SalesEvents" and given that the first (header) row reads "Salesperson" "Date" ...

When you have an Excel Table, and not only then, it is easy to use data as input in a PivotTable which will provide most of the analysis you would need on it. Learn to use it you won't regret it! It could replaces tons of user designed cell Formulas and it is fast and much easier to document.

Sometimes it is hard to make all the data of your Pivot Table confirm to the reporting format you have to present your crunched data into. Then use GetPivotData! It has an automatic fill in of arguments that you easy can learn from and it lets you through its parameters flexibly choose and pick from...

To strip any number of leading components, use the --strip-components option: --strip-components=NUMBER strip NUMBER leading components from file names on extraction For example to strip the leading folder, use: tar -xf --strip-components=1 archive-name.tar

[MetadataType(typeof(RoleMetaData))] public partial class ROLE { } public class RoleMetaData { [Display(Name = "Role")] public string ROLE_DESCRIPTION { get; set; } [Display(Name = "Username")] public string ROLE_USERNAME { get; set; } } If you us...

The possible types passed to a new instance of SomeClass must inherit SomeBaseClass. This can also be an interface. The characteristics of SomeBaseClass are accessible within this class definition. Public Class SomeClass(Of T As SomeBaseClass) Public Sub DoSomething(newItem As T) new...