Accelerating Image Filters with oneAPI on Cloud
Marjan Hussein
Nairobi, Nairobi County
- 0 Collaborators
The project aims to leverage Intel's oneAPI toolkit to accelerate image filtering algorithms on cloud-based servers. By utilizing cloud computing resources, the project avoids the need for specialized hardware and enables to focus on software optimization and algorithm development. ...learn more
Project status: Concept
Intel Technologies
DevCloud,
oneAPI,
DPC++,
Intel FPGA,
Intel GPA
Overview / Usage
This project aims to optimize image filtering algorithms using Intel's oneAPI toolkit and deploy them on a cloud-based server. The project leverages parallel processing with oneAPI's Data Parallel C (DPC) to achieve faster and more efficient image filtering. Additionally, it includes a user-friendly web interface to allow users to apply the accelerated image filters on their own images.
Problems Being Solved:
- Performance Bottlenecks: Traditional image filtering algorithms can be computationally intensive, leading to slow processing times for high-resolution images. This project addresses the performance bottlenecks by parallelizing the algorithms with oneAPI, allowing for faster execution on multiple CPU cores.
- Cloud-Based Accessibility: By deploying the project on a cloud server, it eliminates the need for specialized hardware and makes the image filtering acceleration accessible to users without powerful hardware resources.
- Real-Time Image Processing: The project enables real-time image processing, making it useful for applications that require quick image filtering, such as video processing, image editing, and real-time computer vision tasks.
- Scalability: By benchmarking the project's performance with various image sizes and parallelism levels, it demonstrates scalability, ensuring efficient processing regardless of the input image's dimensions.
How This Work/Research is Experienced or Used in Production:
- Image Editing and Enhancements: Image filtering is a fundamental operation in image editing software. The accelerated image filters can be integrated into image editing tools, allowing users to apply real-time enhancements and effects to their images.
- Real-Time Computer Vision: In computer vision applications, fast image filtering is essential for object detection, edge detection, and feature extraction. The accelerated image filters can be used in real-time computer vision systems for faster analysis.
- Video Processing: Video streams consist of a series of images that require real-time processing. The accelerated image filtering techniques are valuable for video processing applications, enabling quick frame-by-frame analysis.
Methodology / Approach
- Project Planning: Defining the project scope, objectives, and deliverables. Seting up a timeline and allocating tasks
- Image Filtering Algorithm Implementation: Developing image filtering algorithms, such as blur, edge detection, and sharpening, using DPC++ programming languages. Ensuring the algorithms are functional and produce accurate results.
- Cloud Environment Setup: Choosing a cloud service provider (e.g., AWS, Azure, or Google Cloud) and set up a cloud-based server. Install the necessary development environment, including oneAPI toolkit, libraries, and compilers.
- Parallelization with oneAPI: Parallelizing the image filtering algorithms using oneAPI's Data Parallel C++. Divide the image processing tasks among multiple CPU cores for faster execution.
- Performance Benchmarking: Implement a sequential CPU-based version of the image filtering algorithms. Benchmark the performance of the parallelized algorithms against the sequential implementation using a variety of image sizes.
- Web Interface Development: Creating a web-based interface for users to upload images and select the desired image filtering algorithm. Utilize web technologies like HTML, CSS, and JavaScript for the front-end development.
- Cloud Integration: Integrating the parallelized image filtering algorithms with the cloud server. Ensuring the algorithms can be executed on the cloud resources efficiently.
- Visualization: Implementing a visual display of the original and filtered images on the web interface. Allow users to interact with the interface to observe the results and compare the performance.
- Scalability Testing: Testing the project's scalability by evaluating its performance with various image sizes and different levels of parallelism.
Technologies Used
- Intel oneAPI Toolkit: The primary technology for this project, oneAPI provides the necessary libraries, tools, and compilers for parallel programming and optimization.
- C and DPC: Used for implementing image filtering algorithms and parallelizing the code using oneAPI's Data Parallel C++.
- Cloud Service Provider: AWS, Azure, or Google Cloud can be utilized to set up the cloud-based server.
- Web Development: HTML, CSS, and JavaScript for creating the web-based user interface.
- Benchmarking Tools: Use appropriate tools to measure the performance of the algorithms and compare the results.
- Visualization Libraries: Libraries like D3.js or Chart.js can be used to visualize the images and their filtered counterparts on the web interface.