What Exactly Is It?
GPU Enhanced Multithreading leverages the parallel processing capabilities of modern GPUs to perform computationally intensive tasks more efficiently than traditional CPU-based methods. By utilizing multiple threads and the powerful architecture of GPUs, tasks such as matrix multiplication, data analysis, and scientific simulations can be performed at significantly higher speeds.
What Was The Purpose?
Although this project focuses on matrix multiplication, the overall task was to understand how efficient GPU's were at processing large datasets compared to CPU's. With the sudden surge of Generative AI companies/startups and AI as a whole we have seen NVIDIA skyrocket in popularity as they continue to provide the biggest tech leaders GPU's with extreme superiority over their competition, recent news shows Mark Zuckerberg's 'Meta' is looking to purchase billions of dollars worth of GPU's to train their AI Language Models.
A wise man once said:
During a gold rush, sell shovels.
This project utilized Data Parallel C++ (DPC++) and GPU acceleration to perform high-performance matrix multiplication. By populating and multiplying two large matrices, the program employed effective parallelization techniques to achieve rapid and accurate results. Additionally, it generated a heatmap visualization of the resultant matrix, allowing users to intuitively interpret and analyze the multiplication results.
Want To Browse More Projects?
Benefits Included:
High Performance:
Leveraging the parallel processing power of GPUs resulted in significantly faster computation times.Efficiency:
Improved efficiency in handling large-scale data and complex numerical tasks.Scalability:
The solution can be scaled to handle even larger datasets and more complex computations.Visualization:
The heatmap visualization provided valuable insights through intuitive visual representation of the data.Cost-Effective:
Utilizing GPU resources can be more cost-effective than setting up equivalent CPU-based systems.