Something NVIDIA didn't object to, seeing it as an opportunity to propagate PhysX and maybe highlight better performance on GeForce GPUs. This development could also have its implications on the industry, as not very long ago developers at successfully ran PhysX on ATI Radeon GPUs.
#CUDA EMULATOR WHATS IT FOR INSTALL#
To get PhysX to run, one needs to install older versions of PhysX System Software (version 8.09.04 WHQL being the latest) from its standalone installer (installs PhysX libraries without looking for NVIDIA GPUs). It comes in the form of a loader application that injects itself into the executing process. The software works as a translation layer, exchanging calls between CUDA and OpenCL or the CPU if OpenCL is not available. * Possibly better scaling of PhysX on multi-core CPUs (over OpenCL), as the regular PhysX CPU acceleration is infamous for bad multi-core scaling in performance * Letting PhysX run on ATI GPUs as PhysX middleware uses CUDA for GPU acceleration * Letting CUDA-accelerated software such as Badaboom make use of ATI GPUs This move lets CUDA work on ATI Radeon GPUs that support OpenCL, as well as x86 CPUs, since OpenCL specs allow the API to run on CPUs for development purposes. A chinese freelance developer has coded a means to get CUDA work as a middleware on OpenCL. The vCUDA stub also manages actual physical resource allocation.NVIDIA's CUDA GPU compute API could be making its way to practically every PC, with an NVIDIA GPU in place, or not. The vCUDA stub receivesĪnd interprets remote requests and creates a corresponding execution context for the API calls from the guest OS, then returns the results to the guest OS. The functionality of a vGPU is threefold: It abstracts the GPU structure and gives applications a uni-form view of the underlying hardware when a CUDA application in the guest OS allocates a device’s mem-ory the vGPU can return a local virtual address to the application and notify the remote stub to allocate the real device memory, and the vGPU is responsible for storing the CUDA API flow.
Besides these tasks, vCUDA also creates vGPUs and manages them. It is responsible for intercepting and redirecting API calls from the client to the stub. The vCUDA library resides in the guest OS as a substitute for the standard CUDA library. It consists of three user space components: the vCUDA library, a virtual GPU in the guest OS (which acts as a client), and the vCUDA stub in the host OS (which acts as a server). The vCUDA employs a client-server model to implement CUDA virtualization. Figure 3.4 shows the basic concept of the vCUDA architecture. When CUDA applications run on a guest OS and issue a call to the CUDA API, vCUDA intercepts the call and redirects it to the CUDA API running on the host OS. vCUDA virtualizes the CUDA library and can be installed on guest OSes. However, it is difficult to run CUDA applications on hardware-level VMs directly. It leverages the high performance of GPUs to run compute-intensive applications on host operating systems.
The vCUDA is explained in Example 3.2 with a graphical illustration in Figure 3.4.Įxample 3.2 The vCUDA for Virtualization of General-Purpose GPUsĬUDA is a programming model and library for general-purpose GPUs.
#CUDA EMULATOR WHATS IT FOR WINDOWS#
Visual MainWin offers a compiler support system to develop Windows appli-cations using Visual Studio to run on some UNIX hosts.
Similarly, Wine offers library support for virtualizing x86 processors to run Windows appli-cations on UNIX hosts. Lxrun is really a system call emulator that enables Linux applications written for x86 hosts to run on UNIX systems. The WABI offers middleware to convert Windows system calls to Solaris system calls. This section provides an overview of several library-level virtualization systems: namely the Windows Application Binary Interface (WABI), lxrun, WINE, Visual MainWin, and vCUDA, which are summarized in Table 3.4. API call interception and remapping are the key functions performed. This type of virtualization can create execution environments for running alien programs on a platform rather than creating a VM to run the entire operating system. Library-level virtualization is also known as user-level Application Binary Interface (ABI) or API emulation.
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