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This paper introduces a research project that aims to speed-up and size-up some gas storage valuations, based on a Stochastic Dynamic Programming algorithm. Such valuations are typically needed by investment projects and yield prices of gas storage spaces and facilities. However, they involve computations which require great amounts of CPU power or memory. As a result, their parallelization on PC clusters or supercomputers becomes highly attractive and sometimes unavoidable despite its complexity. Our parallelization strategy is based on a message passing paradigm, and distributes both computations and data on a cluster, in order to achieve speed-up and size-up. It includes some complex and optimized data exchanges which are dynamically computed, planned and achieved at each computation step. This optimized data distribution and memory management allows to process large problems on a high number of processors. Moreover, our parallel implementation is able to support different price models, and our first experiments on a standard 32 PC cluster show very good performances particularly for complex price models.