有没有更好和更快的方式从CPU内存复制到GPU使用推力？ [英] is there a better and a faster way to copy from CPU memory to GPU using thrust?

问题描述

最近我一直在使用推力很多。我注意到，为了使用推力，必须总是将数据从cpu内存复制到gpu内存。

让我们看下面的例子：

Recently I have been using thrust a lot. I have noticed that in order to use thrust, one must always copy the data from the cpu memory to the gpu memory.
Let's see the following example :

int foo(int *foo)
{
     host_vector<int> m(foo, foo+ 100000);
     device_vector<int> s = m;
}

我不太确定 host_vector 构造函数工作，但似乎我复制初始数据，来自 * foo ，两次 - 一次到host_vector初始化时，和另一次当 device_vector 初始化时。有没有更好的方式从cpu复制到gpu，而不做一个中间数据副本？我知道我可以使用 device_ptr 作为包装，但仍然不能解决我的问题。

谢谢！

I'm not quite sure how the host_vector constructor works, but it seems like I'm copying the initial data, coming from *foo, twice - once to the host_vector when it is initialized, and another time when device_vector is initialized. Is there a better way of copying from cpu to gpu without making an intermediate data copies? I know I can use device_ptras a wrapper, but that still doesn't fix my problem.
thanks!

它足够聪明，能够理解你的示例中的原始指针，所以你可以直接构造一个 device_vector ，避免临时 host_vector ：

One of device_vector's constructors takes a range of elements specified by two iterators. It's smart enough to understand the raw pointer in your example, so you can construct a device_vector directly and avoid the temporary host_vector:

void my_function_taking_host_ptr(int *raw_ptr, size_t n)
{
  // device_vector assumes raw_ptrs point to system memory
  thrust::device_vector<int> vec(raw_ptr, raw_ptr + n);

  ...
}

到CUDA内存，引入 device_ptr ：

If your raw pointer points to CUDA memory, introduce a device_ptr:

void my_function_taking_cuda_ptr(int *raw_ptr, size_t n)
{
  // wrap raw_ptr before passing to device_vector
  thrust::device_ptr<int> d_ptr(raw_ptr);

  thrust::device_vector<int> vec(d_ptr, d_ptr + n);

  ...
}

使用 device_ptr 不分配任何存储;它只是在类型系统中编码指针的位置。

Using a device_ptr doesn't allocate any storage; it just encodes the location of the pointer in the type system.

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