| Abstract |
Previously in this seminar series, I presented a Finite State Machine model of hemoglobin which I have designed and implemented. I will elaborate upon the algorithms used to instantiate this Finite State Automaton into 2-D and 3-D cellular automata along with other biological molecules. The mathematical underpinnings of Diffusion-Reaction systems modeling in cellular automata will be detailed. These algorithms and their implementation in hardware are based on the works of Toffoli and Margulis (who built the CAM processor) and Hillis (who built the Connection Machine) and electronic digital signal processing hardware for carrying out these computations with massively parallel processors.
In the second part of this talk, I will describe the historical errors made in the implementation of these algorithms and the trade-offs that will be required in re-implementing these designs in new hardware. The specific implementation of the algorithms depends upon the hardware on which they will run: the linear Von Neumann architecture, hyperthreaded cores, multiple core processors, or massively parallel hardware architectures.
The optimal implementation of complex algorithms upon these massively parallel hardware machines will be defined as existing in the trade-space of various descriptors of algorithmic complexity. The trade-offs will be described in terms of computational complexity, algorithmic complexity, space complexity, time complexity, and power consumption. The maximal, average, and optimal requirements for power, memory, and time usage will be described theoretically, along with one approach to dividing an arbitrary algorithm across multiple processing resources. |