GPU Rendering Definition
GPU rendering refers to the use of a Graphics Processing Unit in the automatic generation of two-dimensional or three-dimensional images from a model by means of computer programs.
What is GPU Rendering?
GPU rendering uses a graphics card for rendering in place of a CPU, which can significantly speed up the rendering process as GPUs are primarily designed for quick image rendering. GPUs were introduced as a response to graphically intense applications that burdened CPUs and hindered computing performance.
GPU rendering takes a single set of instructions and runs them across multiple cores on multiple data, emphasizing parallel processing on one specific task while freeing up the CPU to focus on a variety of different sequential serial processing jobs. Rasterization, the rendering method used by all current graphics cards, geometrically projects objects in the scene to an image plane, which is an extremely fast process, but does not include advanced optical effects.
GPU-accelerated rendering is in high demand for a variety of applications, including GPU-accelerated analytics, 3D model graphics, neural graphics processing in gaming, virtual reality, artificial intelligence innovation, and photorealistic rendering in industries such as architecture, animation, film, and product design.
In applications such as smartphone user interfaces with weaker CPUs, force GPU rendering may be enabled for 2D applications to increase frame rates and fluidity. Knowing when to enable force GPU rendering can be determined by using the profile GPU Rendering tool, which identifies bottlenecks by measuring frame rendering times at each stage of the rendering pipeline.
CPU vs GPU Rendering
The way in which data is processed by CPUs and GPUs is fundamentally similar, however where a CPU excels at handling multiple tasks, a GPU is more powerful and can handle a few specific tasks very quickly.
GPUs are markedly faster than CPUs, but only for certain tasks. GPUs may have some limitations in rendering complex scenes due to interactivity issues when using the same graphics card for both rendering and display, or due to insufficient memory. And while CPUs are best suited to single-threaded tasks, the tasks of modern games become too heavy for CPU graphics solution.
Some advantages and disadvantages of CPU rendering include:
- Developing for the CPU is easier most of the time, as it makes adding more features a simpler process. Additionally, developers are generally more familiar with programing on the CPU.
- CPUs can implement algorithms that are not suited to parallelism.
- The CPU has direct access to the hard drives and main system memory, enabling it to hold a greater amount of data as system memory, which is expandable and more cost effective.
- CPU programs tend to be more stable and better tuned due to the maturity of available tools.
- CPUs do not stack well -- their designs change often and a new motherboard is required for upgrades, which can be very costly.
- CPUs are power inefficient, expending high amounts of power to deliver low latency results.
Some advantages and disadvantages of GPU rendering include:
- Scalability in multi-GPU rendering setups.
- GPU rendering solutions consume less power that CPUs.
- Speed boosts - many modern render systems are suited for GPU software and hardware, which are designed for massively parallel tasks and can provide overall better performance.
- Lower hardware costs due to the increase in computation power.
- GPUs do not have direct access to the main system memory or hard drives and must communicate through the CPU.
- GPUs depend on driver updates to ensure compatibility with new hardware.
The use of CPU and GPU Rendering depends entirely on the consumer’s rendering needs. The architectural industry may benefit more from traditional CPU rendering, which takes longer, but generally generates higher quality images, and a VFX house may benefit more from GPU rendering, which is specifically designed to manage complicated, graphics-intensive processing. The best GPU for rendering depends on the intended use and budget.
What is a GPU Renderer?
A GPU render engine, or GPU-accelerated renderer, is an engineered program based on such disciplines as light physics, mathematics, and visual perception. There is a wide variety of GPU renderers on the market today, some of which offer both CPU-based rendering solutions and GPU-based rendering solutions, and the capability to simply switch between the two with a single click.
Popular examples of GPU renderers include: Arion (Random Control), Arnold (Autodesk), FurryBall (Art And Animation Studio), Iray (NVIDIA), Octane (Otoy), Redshift (Redshift Rendering Technologies), and V-Ray RT (Chaos Group).
GPU Rendering vs Software Rendering
Software rendering refers to the process of generating an image from a model via software in the CPU, independent of the constraints of graphics hardware. Software rendering is categorized as either real-time software rendering, which is used to interactively render a scene in such applications as 3D computer games, with each frame being rendered in milliseconds; or as pre-rendering, which is used to create realistic movies and images, in which each frame may take several hours or even days to complete.
The main attraction to software rendering is capability. While hardware in GPU rendering is generally limited to its present capabilities, software rendering is developed with fully customizable programming, can perform any algorithm, and can scale across many CPU cores across several servers.
Software can also expose different rendering paradigms. Software rendering stores the static 3D scene to be rendered in its memory while the renderer samples one pixel at a time. GPU rendering renders the scene one triangle at a time into the frame buffer. Techniques such as ray tracing, which focus on producing lighting effects and are more commonly implemented in software, use software rendering instead of GPU rendering.
Does OmniSci Offer a GPU Rendering Solution?
OmniSci Render leverages server-side GPUs to instantly render interactive charts and geospatial visualizations. Render uses GPU buffer caching, modern graphics APIs, and an interface based on Vega Visualization Grammar to generate custom visualizations, enabling zero-latency visual interaction at any scale. Render enables an immersive data visualization and exploration experience by creating and sending lightweight PNG images to the web browser, avoiding large data volume transfers.
OmniSci also facilitates CPU to GPU. The GPU Open Analytics Initiative (GOAI) and its first project, the GPU Data Frame (GDF), was OmniSci’s first step toward an open ecosystem of end-to-end GPU computing. The principal goal of the GDF is to enable efficient, intra-GPU communication between different processes running on the GPUs. The net result is that the GPU becomes a first class compute citizen and processes can inter-communicate just as easily as processes running on the CPU.