Accelerating System-Level Design Tasks using Graphics Processors
Udeepta D. Bordoloi, Samarjit Chakraborty, Unmesh Dutta Bordoloi · 2011
Summary: Recent years have seen the increasing use of graphics processing units (GPUs) for non‐ graphics related applications. Applications that have harnessed the computational power of GPUs span across numerical algorithms, computational geometry, database processing, image processing, astrophysics and bioinformatics. There are many compelling reasons behind exploiting GPUs for such general‐purpose computing tasks. First, modern GPUs are extremely powerful. For example, high‐ end GPUs such as the NVidia GeForce GTX 480 and ATI Radeon 5870 have 1.35 TFlops and 2.72 TFlops of peak single precision performance, whereas a high‐end general‐purpose processor such as the Intel Core i7‐960 has a peak performance of 102 Gflops. Additionally, the memory bandwidth of these GPUs are more than 5x greater than what is available to a CPU, which allows them to excel even in low compute intensity but high bandwidth usage scenarios. Second, GPUs are now commodity items as their costs have dramatically reduced over the last few years. In spite of a wide variety of computationally expensive system‐level design tasks (in the context of embedded systems design) that are regularly solved by software tools running on desktops and laptops equipped with high‐end GPUs, the use of GPUs for accelerating such problems is still not a conventional practice within the design automation community. As a result, of late, there has been a