GPU vs. CPU for Image Processing: Which One is Better?

Are you wondering whether to choose a GPU or CPU for image processing? You know that choosing the right processing technology is important for delivering high-quality results to your clients.

GPUs and CPUs both have their unique strengths and weaknesses when it comes to image processing, but which one is better?

In this post, we’ll explore the key differences between GPUs and CPUs, and help you make informed decisions on how to optimize your image processing tasks. From hardware to software, algorithms to image formats, we’ll cover everything you need to know to perform fast and efficient image processing in the cloud.

Let’s dive in:

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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?

The main difference between GPU and CPU processing is that GPUs have massive parallel processing power due to many simple processing units, while CPUs have fewer, more complex processing units optimized for general-purpose computing.

However, there are other things that you need to know:

  • 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?

A GPU is suitable for image processing because it has many small processing units that can handle image-related tasks in parallel, resulting in faster processing times for graphics-intensive applications.

However, there are other elements that make GPU ideal 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

GPU Vs. CPU for Image Processing – FAQs

  1. What is the main difference between a CPU and a GPU?
    A CPU is designed for general-purpose computing while a GPU is optimized for parallel processing.
  2. What are the advantages of using a GPU for image processing?
    GPUs can process large amounts of data simultaneously, resulting in faster image processing.
  3. What are the advantages of using a CPU for image processing?
    CPUs are better suited for tasks that require sequential processing, such as certain types of image analysis.
  4. Which is more cost-effective for image processing, a CPU or a GPU?
    GPUs are more cost-effective for image processing tasks that require a lot of processing power.
  5. Are there any limitations to using a GPU for image processing?
    GPUs may not be as effective for tasks that require large amounts of memory or are not easily parallelizable.
  6. Can a CPU and GPU be used together for image processing?
    Yes, many image processing tasks can benefit from using both a CPU and a GPU together for optimal performance.
  7. What types of image processing tasks are better suited for GPUs?
    GPU excels at tasks such as image rendering, video processing, and machine learning that require processing large amounts of data simultaneously.
  8. Can GPUs be used for real-time image processing?
    Yes, GPUs are often used in real-time image processing applications such as video games, virtual reality, and augmented reality due to their ability to process large amounts of data in real-time.

Final Words!

In conclusion, both GPUs and CPUs have their strengths and weaknesses when it comes to image processing. While CPUs are more versatile and can handle a wide range of tasks, GPUs are ideal for graphics-intensive and parallel processing tasks.

With the increasing demand for image and video processing, GPUs have become essential tools for cloud-based image processing applications, providing faster and more efficient processing power. Moreover, with the rapid advancements in GPU technology, cloud-based image processing has become more accessible and affordable for businesses of all sizes.

It is important to understand the differences between GPUs and CPUs and choose the right technology based on the specific image processing needs of the project. Ultimately, leveraging the power of GPUs for image processing can help businesses improve their efficiency and productivity while staying ahead of the competition.

About Nolan Foster

With 20+ years of expertise in building cloud-native services and security solutions, Nolan Foster spearheads Public Cloud and Managed Security Services at Ace Cloud Hosting. He is well versed in the dynamic trends of cloud computing and cybersecurity.
Foster offers expert consultations for empowering cloud infrastructure with customized solutions and comprehensive managed security.

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