cu | package cu provides an idiomatic interface | GPU library

Your import statement is wrong, try something like this;

import cu from '../../img/cu.svg';

I'm assuming that admin and img both are on same level, and Price.js is inside admin/price.

I was able to resolve your issue and update the query to fulfil your need.

The issue is passing the array into the 'params' parameter. It thinks that you are passing a list of parameters into the bql query instead of a single array as a parameter.

If you cast it as follows it should work: .Select(Base, (object)applicableTovalues);

If you take a look at the intl-tel-input documentation regarding Initialisation Options. There is an option called excludeCountries.

We can modify your initialisation code to include this option to exclude China:

Try:

TL;DR:

How can I assemble .ptx file and host code file and make a executable file?

You use the CUDA driver API. Relevant sample codes are vectorAddDrv (or perhaps any other driver API sample code) as well as ptxjit.

Do I need a .cu file to run it or is there another way to run it?

You do not need a .cu file (nor do you need nvcc) to use the driver API method, if you start with device code in PTX form.

Details:

The remainder of this answer is not intended to be a tutorial on driver API programming (use the references already given and the API reference manual here), nor is it intended to be a tutorial on PTX programming. For PTX programming I refer you to the PTX documentation.

To start with, we need an appropriate PTX kernel definition. (For that, rather than writing my own kernel PTX code, I will use the one from the vectorAddDrv sample code, from the CUDA 11.1 toolkit, converting that CUDA C++ kernel definition to an equivalent PTX kernel definition via nvcc -ptx vectorAdd_kernel.cu):

vectorAdd_kernel.ptx:

Okay I got it!

This guy goes over the required libraries

https://www.youtube.com/watch?v=2IWtEkAHXFI

I was missing swig, and afterwards I was also missing liborc-0.4-dev

They can be installed via:

cuGraph supports multi-GPU by leveraging Dask. I encourage you to read the Dask cuGraph documentation that shows an example using PageRank.

For a Louvain example, I recommend looking at the docstring of the cugraph.dask.louvain function.

For completeness, under the hood cuGraph is using RAFT to manage underlying NCCL and UCX communication.

Currently, it's not possible to call cudaMallocManaged in CUDA device code. It has never been possible. I don't believe there is NVIDIA training material that demonstrates using cudaMallocManaged in device code.

If you wish to make an in-kernel allocation, I suggest using the methods described in the programming guide. Also, new and delete work similarly to malloc() and free(), for in-kernel usage.