The Computation Of Direct Volume Rendering And Genetic Algorithm On GPU

High performance computing are needed by many scientific fields, but this cann't be obtained easily. Recently, the rapidly development of graphics processing unit (GPU) opens another possibility for high performance computing. In this paper, we explore how to apply commodity available graphics cards to computing problems. We develop two programs on graphic hardware, one is a GA program to solve 3-SAT problem, another is a volume rendering program. Through these two programs, we show that GPU can be used as a general purpose process. Comparing the speed of our graphics hardware implementations to standard CPU implementations, we demonstrated great performance improvements.Driven by the game industry, GPU performance has approximately doubled every 6 months since the mid-1990s, which is much faster than the growth rate of CPU performance that doubles every 18 months on average, and this trend is expected to continue. The computational power of today's commodity GPUs has exceeded that of PC-based CPUs. For example, NVIDIA recently released GeForce 6800 Ultra, it has been observed to reach 40 Gflops in fragment processing. In comparison, the theoretical peak performance of the Intel 3GHz Pentium4 using SSE instructions is only 6 GFlops. And with the fixed-pipeline changed to programmable, general purpose computation on GPU become possible.3-SAT problem is an NP-hard problem, if the problem is big enough it is hard to solve. Using genetic algorithm can make it easy to solve it, but usually still need high performance computing. We develop a GA program on GPU to solve 3-SAT problem. The result shows that, in 1000 passes, it takes 2207ms on P4 2.4G(CPU), and 890ms on NVIDIA 6200(GPU). The efficiency on GPU is 2.48 times than that on CPU.In the direct volume rendering area, GPU computes even faster. We developed a program of direct volume rendering based GPU computing , by running this program on NVIDIA 6200(GPU), it take 4234ms for 300 passes ,the same program run on P4 2.4G(CPU) ,it take 442660ms for 300 passes. The efficiency on GPU is 105 times faster than that on CPU. This result is probably for the sake of volume rendering can be more adapt to graphics card's parallel pipeline hardware structure.We also develop a DEMO for volume rendering based on GPU computing. By using this...