Designing a 3D Graphics Processor for Mobile Applications High performance 3D graphics processor is one of the most important hardware modules in modern mobile SoC design. This paper presents the design of a fully programmable 3D graphics processor taking advantage of unified shader architecture for mobile applications. We adopt multithread scheduling to utilize data, instruction parallelism and reduce data dependency stalls. In the unified shader core design, we adopt single instruction multiple data (SIMD) architecture to support four concurrent scalar operation. Moreover, we propose a novel special function unit for floating point transcendental function computation. Using these optimization methods, the proposed mobile 3D graphics processor achieves vertex and fragment processing speed of 600Ms under 40nm technology.

Performance of Floatingpoint Intensive Kernels on Lowpower Processor xe2x80x93 A Case Study with Geodesic Distance Kernel A processor, with a GPU and a CPU integrated on the same chip, is a promising lowpower system for floating point intensive applications. While an integrated GPU is not designed to outperform a discrete GPU due to its power, area, and thermal constraints, there is a need to better understand the performance of a floatingpoint intensive kernel using an integrated GPU. Toward this end, we choose a representative floatingpoint intensive kernel as a case study. We port the kernel with a vendorneutral framework, analyze the compiler optimizations of the kernel at the assembly code, evaluate the relationship between floatingpoint operations per second and arithmetic intensity, and compare the performance and power of the kernel implementations on the CPU and GPU. Our key findings are: 1) Compared to an unoptimized kernel, the floatingpoint optimizations improve the performance of the single and doubleprecision floatingpoint kernels executing on an Intelxc2xae GEN8 Iris Pro GPU by 15.4X and 5.4X, respectively; the optimizations also improve the performance of the two kernels by 5.6X and 3.4X on an Intelxc2xae Xeonxc2xae E3 CPU, respectively. 2) Achieving peak floatingpoint operations per second on the GPU requires much higher arithmetic intensity than that on the CPU. 3) Running the floatingpoint intensive kernel on the processor consumes 48 Watts, which is very close to the thermal power draw of the processor. The floatingpoint optimization can reduce the average GPU power from 35.7 W to 22.7 W for the doubleprecision kernel, and from 33.1 W to 8.8 W for the singleprecision kernel.