thrust | based cross-platform / cross-language application framework

S3 buckets are region specific. When you create a new bucket you need to select the target region for that bucket.

For DR reasons, you can keep backups in another region. Should the primary region fail in a way that the entire region is affected, then you could restore in the backup region.

Your DR strategy will depend on your use case, and your needs for returning services back to normal in case of region wide failure.

For example, let's say you rely on ec2/ebs to operate your service and those services suffer region wide outage for 5 hours. In order to recover your service you would need to move to a region where the resources are available. Assuming you need S3 data for operational processing you would want to have that data ready in the Target recovery region.

I found the issue. The Intel MKL module wasn't loaded properly and caused the error. After fixing this the compilation worked just fine also with CUDA 11.1 and pytorch 1.8.1!

Other than a few minor mistakes in your regex, you need to use .+? instead of .+, because the second one is "greedy" which means, it will match as much as it can get.

To expand on Ahmed's comment, if you set KeyPreview to true then the form will experience the key events before any selected control on the form does.

forceDirection.Normalize() alters the forceDirection vector and returns void, rather than returning the normalized vector. So you'll need to split the Normalize() call and the multiplication into separate statements:

In spite of its naming, a thrust::device_vector is not directly usable in CUDA device code. The device_vector is an object/container, and it is intended to be usable in host code only. This is why you get the messages about "calling a host function..."

For the example you have shown here, to access the data directly, you would (in host code) extract device pointers to the underlying data in each container (A and B) and pass those pointers to your CUDA kernel.

Something like this:

There's no need to have a separate localColor. You can directly pass in $dataModel.masterColor to the picker.

Forgot to use ceil when calculating grid dimensions.

Thrust does all CUDA API calls for you. So while you can use Thrust algorithms on manually allocated memory or pass the memory from a thrust::device_vector to a kernel, you don't need cudaMalloc and cudaMemcpy, as everything is already included in the standard C++ vector interface.

The memory allocated by thrust::device_vector lives on the GPU (if you are using one. One can use Thrust for parallelizing on the CPU as well). So the constructor calls cudaMalloc for you.

For data transfer you can use different thrust::device_vectors and thrust::host_vectors like normal std::vectors (e.g. constructors and operator= are implemented for the different combinations). Thrust knows what to do with each type of vector and will call cudaMemcpy for you. If this isn't explicit enough for you, you can also use thrust::copy.

Your code could look the following way: