![]() In this case, each thread executes the instructions independently.Īt the same time, the number of GPU threads is tens or hundreds of times greater, since these processors use the SIMT (single instruction, multiple threads) programming model. The number of threads on a CPU and GPUĬPU architecture is designed in such a way that each physical CPU core can execute two threads on two virtual cores. Let's have a look at the key differences between GPU and CPU.ġ. ![]() CPUs have rather modest latency, since parallelism in a CPU is implemented at the level of frames, tiles, or image lines. Multilevel optimization is especially important for resource-intensive algorithms and it gets essential performance benefits. After the multilevel optimization is applied, advanced algorithms will return results within a reasonable time period, comparable to the speed of fast but crude algorithms.Ī GPU has an architecture that allows parallel pixel processing, which leads to a reduction in latency (the time it takes to process a single image). There may be several algorithms used for the exact same image processing operation that differ in resource intensity and the quality of the result. It’s possible to realize the full potential of a GPU only with parallelization and thorough multilevel (both low-level and high-level) algorithm optimization.Īnother important criterion is the image processing quality. When comparing the capabilities of GPU and CPU, GPU outperforms CPU in the price-to-performance ratio. ![]() Maximum performance of fast image processing can be achieved in two ways: either by increasing hardware resources (specifically, the number of processors), or by optimizing the software code. The key criteria which are important for fast image processing are: Important criteria for fast image processing Typically, 32-bit floating point arithmetic is sufficient for image processing and a 16-bit integer data type is sufficient for storage. Each pixel does not depend on the data from the other processed pixels, so tasks can be processed in parallel. Each pixel is calculated based on a limited number of neighboring pixels. Here’s a brief description of each characteristic: ABOUT FAST IMAGE PROCESSING ALGORITHMSįor the purposes of this article, we’ll focus specifically on fast image processing algorithms that have such characteristics as locality, parallelizability, and relative simplicity. In addition, we will go through some common misconceptions that prevent people from using a GPU for fast image processing tasks. We will compare two processors and show the advantages of GPU over CPU, as well as explain why image processing on a GPU can be more efficient when compared to similar CPU-based solutions. In this article, we will discuss the capabilities of GPUs and CPUs for performing fast image processing tasks. Over the past decade, there have been many technical advances in GPUs (graphics processing units), so they can successfully compete with established solutions (for example, CPUs, or central processing units) and be used for a wide range of tasks, including fast image processing.
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