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Abstract: "We present a method for simulating the melting and owing of material in burning objects fast enough to be of use in video games where most of the graphical and computational resources are needed elsewhere. The standard practice of us- ing particle engines or uid dynamics for melting are far too costly for use in this environment. We demonstrate that our method, which is based on systematic polygonal expanding and folding, uses only a fraction of the computational power avail- able by implementing the computation on a very modest GPU using CUDA"

Abstract: "Cloud gaming provides game-on-demand (GoD) services over the Internet cloud. The goal is to achieve faster response time and higher QoS. The video game is rendered remotely on the game cloud and decoded on thin client devices such as tablet computer or smartphone. We design a game cloud with a virtualized cluster of CPU/GPU servers at USC GamePipe Laboratory. We enable interactive gaming by taking full advantage of the cloud and local resources for high quality of experience (QoE) gaming. We report preliminary performance results on the game latency and frame rate. We find 109 ~ 131 ms latency in using the game cloud, which is 14% ~ 38% lower than 200 ms latency experienced on a thin local computer. Moreover, the frame rate from the cloud is 25% ~ 35% higher than that of using a client computer alone. Base on these findings, we anticipate game cloud to have a performance gain or QoS improvement of 14% ~ 38% over video gaming on a thin client device such as a smartphone or a tablet computer."

"Objects in 3D games are typically shell models, a polygon mesh representing the shell or skin of the object. While emulation of the behaviour of shell models under combustion is sucient for many game applications and is fairly well studied, solid objects do in fact burn rather dierently than shell objects. We show how to manipulate shell models so that they appear to burn as solid models. Since our burning objects will be only a small part of a video game, computation speed is of the essence. We demonstrate that our method uses only a fraction of the computational power available by implementing the computation on a modest GPU using CUDA."