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FPGA has been an emerging field in novel big data architectures and systems, due to its high efficiency and low power consumption. It enables the researchers to deploy massive accelerators within one single chip. In this paper, we present a software defined FPGA based accelerators for big data, named SODA, which could reconstruct and reorganize the acceleration engines according to the requirement of the various dataintensive applications. SODA decomposes large and complex applications into coarse grained single-purpose RTL code libraries that perform specialized tasks in out-of-order hardware. We built a prototyping system with constrained shortest path Finding (CSPF) case studies to evaluate SODA framework. SODA is able to achieve up to 43.75X speedup at 128 node application. Furthermore, hardware cost of the SODA framework demonstrates that it can achieve high speedup with moderate …