To put it simply, a CPU is like the computer’s brain and GPU serves to be its soul. However, over the years, GPU has crossed its limitations and reached horizons which are beyond a personal computer. And, a major AI boom is brought by GPUs making them a crucial part of modern supercomputing.

GPU and CPU have various common points, but the processors also have differentiating factors in their functionalities. Owing to technological developments, GPU has been power-packed enough to stand parallel or better with CPU in specific processing horizons. One such domain is image processing, wherein GPU has proved itself to be better than CPU

This article brings into light the features of GPU, which makes it a better option for fast image processing while stating the critical GPU vs. CPU differences.

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Also Read: NVIDIA A30: The Workhorse of AI and HPC in the Data Center

A few pointers about quick image processing

Keeping the focus of this article on fast image processing, here are a few aspects of the process that one should be aware of before jumping to the GPU vs. CPU performance.

  • Locality: The neighbouring pixels, which are limited in number, are responsible for calculating each pixel.
  • Parallelization: The pixels do not depend on the data from other processed pixels. Hence, the work can be carried out parallelly.
    • 16/32-bit precision- Usually, a 16-bit integer data type is enough for storage, and 32-bit floating type arithmetic is sufficient for image processing.

Important criteria to perform fast image processing

  • Latency: The reduction in latency is possible with GPU architecture because it allows parallel pixel processing. On the other hand, because the CPU has parallel processing but on the levels of tiles, frames, and image lines, the latency tends to remain.
  • Performance: In fast image processing, the ideal level of performance can either be achieved by optimizing the code of the software or by increasing hardware resources, which is the number of processors. Regarding the price-to-performance ratio, GPU stands out to be better than CPU in regards to fast image processing capabilities. However, the real potential of GPU is explored only with multilevel algorithm optimization or parallelization.
  • Quality of image processing: Image processing quality is another important criterion for fast image processing. Though multiple algorithms can be used to perform the same image processing, it will differ in the quality of the result and resource intensity. To gain extra performance benefits, it is essential to have multilevel optimization, especially in the cases of resource-intensive algorithms. With the application of multilevel optimization, the algorithms deliver results in less time than the relatively fast yet simple algorithms.

Also Read: The Evolution of the GPU

What is the difference between GPU and CPU processing?

  • Processing Type: Pertaining to the processor’s architecture, GPU is best suited for parallel instruction processing, and CPU is best suited for serial instruction processing.
  • Thread numbers: In a CPU architecture, each of the physical CPU cores is allowed to execute two threads on two respective virtual cores in a way that an individual core executes an instruction solely. A GPU utilizes the SIMT or single instruction multiple thread architecture in which 32 threads are allocated to work on a single instruction.
  • Core: A CPU has a small but powerful core, while a GPU has thousands of small but weak cores.
  • Implementation of thread: GPU utilizes the real thread rotation for launching instructions from different threads each time. It becomes ideal for implementing hardware and various image processing algorithms, especially in the case of high load and parallel processing. On the contrary, a CPU utilizes the out-of-order execution method.

Why is a GPU suitable for image processing?

  • Shared Memory: Almost every modern GPU has shared memory, making it way better and exponentially faster than the CPU’s cache. It works best with algorithms that have a high degree of locality.
  • Managing Load: In contrast to a CPU, a GPU can considerably reduce the load on a subsystem by modifying the number of registers.
  • Speed: The parallel processing aspect of GPU makes it much faster than a CPU because of the better memory and processing power bandwidth. GPUs are almost 100x quicker in processing than a CPU.
  • Embedded Applications: GPUs are a better alternative to embedded applications like ASICs and FPGAs as they offer more flexibility.
  • Parallel execution of various tasks: The different hardware modules of a GPU allow entirely diverse tasks to be executed simultaneously—for instance, tensor kernels for neural networks.

Also Read: NVIDIA Gears Up For AI-Driven Future with the Tensor Core A100 GPU

Some myths about GPU

With less or no experience with GPU, the users try to solve issues using CPU

One of the major myths associated with GPU is that ten years earlier, GPU was considered to be unfit for carrying out high-performing tasks. Although massive technological advancements are happening and GPU processing integrates with CPU processing, the process of fast image processing is always better when performed on a GPU.

Performance hindered due to multiple data copies to and from GPU

Another misconception regarding the functioning of GPU. In such cases, all instructions should be processed within a single task in a GPU. The data from the source can be replicated once in a GPU, and by the end of the pipeline, the computational results are given to the CPU. And, in cases like these, the intermediate data remains only with the CPU.

96 KB shared memory for every multiprocessor

If the shared memory is managed efficiently, the 96 kb memory remains sufficient even when the GPU memory is small. This is the conclusion for CUDA and OpenCL-related software optimization. The software code cannot be transferred from a CPU to GPU without considering the GPU architecture.

GPU global memory not being enough for complex tasks

This problem is only solvable by the manufacturers when they launch a new GPU variant with increased memory in the market. And one can also use a memory manager to completely utilize the GPU global memory.

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Also Read: Why GPU for Deep Learning

Final Words!

GPU works better than a CPU for fast image processing and complex tasks. Also, it helps in reducing the time taken to complete a task because of its parallel processing architecture. Further, GPU also delivers high efficiency in less hardware and ownership cost. Hence, GPU for fast image processing is the ideal choice.

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