"We present a framework for simulating signal propagation in geometric networks (i.e. networks that can be mapped to geometric graphs in some space) and for developing algorithms that estimate (i.e. map) the state and functional topology of complex dynamic geometric net- works. Within the framework we define the key features typically present in such networks and of particular relevance to biological cellular neural networks: Dynamics, signaling, observation, and control. The framework is particularly well-suited for estimating functional connectivity in cellular neural networks from experimentally observable data, and has been implemented using graphics processing unit (GPU) high performance computing. Computationally, the framework can simulate cellular network signaling close to or faster than real time. We further propose a standard test set of networks to measure performance and compare different mapping algorithms."

"Huang at Nvidia has said many times that CUDA is one of the most important advances in computing, and its promise will be evident as programmers learn how to make use of it. Nvidia is already getting lots of its chips designed into supercomputers and servers, thanks to CUDA. And because of CUDA, the 480 chip can do physics processing 2.5 times faster than the previous generation. That means that the environment in a game, such as water in a stream, behaves far more realistically, adding to the overall illusion of a graphics animation. The real question is whether CUDA is really helping or hurting Nvidia’s cause to bring better graphics to the entire world."

"PyGPU is an embedded language in Python, that allow most of Python features (list-comprehensions, higher-order functions, iterators) to be used for constructing GPU algorithms. It uses a image abstraction to abstract away implementation details of the GPU, while still allowing translation to very efficient GPU native-code."