![]() The importance of this could grow as more applications run on multiple devices, such as tablets or mobile workstations. However, it has the advantage of being an open standard that can execute on all types of GPUs, CPUs and other processors. OpenCL is currently on version 1.2 and is not as mature as CUDA. Some CUDA-based applications can also be accelerated by CPUs, but the performance is usually nowhere near as fast.ĬUDA is not compatible with AMD GPUs. However, it is a proprietary Nvidia technology and requires Nvidia GPUs, which limits the options when buying hardware. CUDA has been in development since 2004, and is by far the more established of the two frameworks. It is important to understand the pros and cons of each as this will have a major influence on how you approach GPU compute. VRAY RT OPENCL BENCHMARK SOFTWARECommercial GPUaccelerated software tends to use one or the other, though there are some applications that can use both. There are currently two competing programming frameworks for GPU compute: CUDA and OpenCL. PSUs will typically be rated at 1,000W or above. The PCI Express slot can’t deliver this much, so additional power needs to be taken direct from the Power Supply Unit (PSU) via a PCIe AUX power connector. GPU compute boards also require lots of power with some drawing close to 300W. They require a spacious ATX workstation chassis and plenty of cooling. they take up two PCI Express slots on the motherboard). High-end GPUs are usually full-length double height PCI Express boards (i.e. The two major manufacturers of these processors are AMD and Nvidia. GPU compute hardwareįor workstations there are two types of GPUs that can be used for compute: those that are that dedicated solely to compute and those can handle both compute and 3D graphics. It may be a cliché, but GPU compute can be like having two workstations in one. With its own processing cores and memory, a GPU can perform complex calculations without hogging the CPU and system memory.Ĭonversely, running a ray trace render on a CPU can sometimes make it virtually impossible to perform other tasks such as 3D modelling. As solve times are reduced it is also possible to explore more alternatives, which can lead to a better design.Ī big advantage of GPU compute inside a workstation is having fingertip access to high performance computing, but at the same time it also frees up CPU resources. It can deliver more accurate results by increasing the mesh density in larger models. In simulation, GPU compute is not just about accelerating solve times. Comparisons between ray trace rendering on the CPU and GPU are harder to quantify as the results are more subjective. Leading Computer Aided Engineering (CAE) software developers claim a top end GPU can solve a simulation problem two to four times faster than a pair of multi-core CPUs. ![]() ![]() VRAY RT OPENCL BENCHMARK CODESimulation or rendering code starts on the CPU and ends on the CPU, but, when appropriate, certain parallel portions get moved to the GPU where they can be calculated faster. ![]()
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