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Heterogeneous parallel systems including accelerators such as Graphics Processing Units (GPUs), are expected to play a major role in architecting the largest systems in the world, as well as the most powerful embedded devices. Impressive computational speedups have been reported for numerous algorithms in fields of medical image processing, digital signal processing, astrophysics, modeling and simulations...
Diverse IP cores are integrated on a modern system-on-chip and share resources. Off-chip memory bandwidth is often the scarcest resource and requires careful allocation. Two of the most important cores, the CPU and the GPU, can both simultaneously demand high bandwidth. We demonstrate that conventional quality-of-service allocation techniques can severely constrict GPU performance by allowing the...
The rasterization stage in a graphics processing unit (GPU), which consists of triangle setup, rasterization, and parameter interpolation with plane equations, always requires huge operations and is usually the bottleneck of the performance. For real-time applications, a universal rasterizer (UR) with edge equations and a tile-scan triangle traversal algorithm are proposed for low cost graphics rendering...
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