Week 5 – Innovation In Rendering Technology

All CG technologies ultimately require rendering to present the final image.

To some extent, the development of rendering technology reflects the technological development of the entire CG industry.

Among them, the hardware application of rendering computing has undergone tremendous changes in recent years.

In the early days, rendering was done by CPU. Nowadays, due to the development of graphics card and graphics architecture, rendering can also be done by GPU, and this technology is now very popular, especially for artists or small studios.

1) Difference Between CPU and GPU

The main difference between CPU and GPU architecture is that a CPU is designed to handle a wide range of tasks quickly (as measured by CPU clock speed), but are limited in the concurrency of tasks that can be running. A GPU is designed to quickly render high-resolution images and video concurrently. 

Because GPUs can perform parallel operations on multiple sets of data, they are also commonly used for non-graphical tasks such as machine learning and scientific computation. Designed with thousands of processor cores running simultaneously, GPUs enable massive parallelism where each core is focused on making efficient calculations.

2) CPU vs GPU Processing

While GPUs can process data several orders of magnitude faster than a CPU due to massive parallelism, GPUs are not as versatile as CPUs. CPUs have large and broad instruction sets, managing every input and output of a computer, which a GPU cannot do. In a server environment, there might be 24 to 48 very fast CPU cores. Adding 4 to 8 GPUs to this same server can provide as many as 40,000 additional cores. While individual CPU cores are faster (as measured by CPU clock speed) and smarter than individual GPU cores (as measured by available instruction sets), the sheer number of GPU cores and the massive amount of parallelism that they offer more than make up the single-core clock speed difference and limited instruction sets.

GPUs are best suited for repetitive and highly-parallel computing tasks. Beyond video rendering, GPUs excel in machine learning, financial simulations and risk modelling, and many other types of scientific computations. While in years past, GPUs were used for mining cryptocurrencies such as Bitcoin or Ethereum, GPUs are generally no longer utilized at scale, giving way to specialized hardware such as Field-Programmable Grid Arrays (FPGA) and then Application-Specific Integrated Circuits (ASIC).

3)The Advantages of CPU Rendering

  • Able To Handle Intricate Projects
  • Increased Memory
  • Increased Memory
  • Quality

4) The Advantages of GPU Rendering

  • Faster Processing
  • More For Less
  • Continuous Evolution
  • Resource-Intensive Tasks

The GPU rendering speed is quite fast, and the improvement is amazing. This advantage can greatly speed up the artist’s debugging of materials, lights, and scenes in the 3D workflow, thereby shortening the time period for rendering tests. With the support of high-speed graphics cards, many effects can achieve real-time feedback, which is much more powerful than CPU rendering.

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