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Because the functional cure does not prevent HIV from entering, it is not a vaccine and cannot in itself reduce the infection. It is in this sense that it cannot prevent or treat the disease. Nevertheless, in the FoGS study it is shown that an effective type of immune response can be provoked by this cure, and that such a response can be sufficient to effectively combat and eliminate the HIV infection and its spread. FoGS scientists explain that the results obtained with the HIV envelope protein (glycoprotein gp120) show that it is possible to prompt a response from the cellular immune system that eliminates HIV. The cellular immune system does not recognize the virus specifically, but in fact detects and kills any infected cell, which makes this response highly effective. “The functional cure reveals what HIV has been doing to us for the last 40 years,” says Professor Thomas E. Pierson, chief science officer and director of research at the University of Pennsylvania. “The virus is constantly changing and mutating so the immune system is never going to recognize it, which has been the reason why people

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xmind free version download full version xmind full version pro key xmind full version video demo xmind Mac xmind downloadQ:

Is there a stand-alone C/C++ library for Deep Learning?

This question is based on my previous question:

Why does cudaMallocHost need to be called as a host function?

I just read the answer, but I still have some questions:
1) Why cudaMallocHost needs to be called as a host function?
2) Why we can’t start with kernel launching with host function?
If it has something to do with GPU programming, is there a stand-alone library that people used?

A:

AFAIK, cudaMallocHost is called from the kernel, and cudaMallocHost is much slower than the cudaMalloc call.
CUDA provides an abstraction layer for device and host code: the application layer. It contains both the kernel calls and the host calls and makes it easier to code. Thus, the application layer doesn’t care where the device objects or memory is, and all the application logic is abstracted away and placed within the kernel.
The kernel calls are low-level and run in “parallel”. This means that if a kernel call is used, there will always be only one of those calls running on a GPU, whereas you can have up to thousands of host threads in one single host call. Additionally, the rest of the application logic is executed on the host.
You should try to understand the concept of CUDA programming first: there are lots of good tutorials and books about that on the Internet, such as CUDA by Example, Developing Programs for CUDA, CUDA Programming Guide. You can find the “developers guide” to the language on the NVIDIA website.
Of course, here are some examples to demonstrate how to interact with the CUDA API:
Simple CUDA example with the CUDA Toolkit API
CUDA_example with the C API

Wednesday, August 31, 2011

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