Today multiple frameworks exist for elevating thetask of writing programs for GPGPUs, which are massively data-parallel execution platforms. These are needed as writing correctand high-performing applications for GPGPUs is notoriouslydifficult due to the intricacies of the underlying architecture.However, the existing frameworks lack a formal foundation thatmakes them difficult to use together with formal verification,testing, and design space exploration. We present in this papera novel software synthesis tool – called f2cc – which is capableof generating efficient GPGPU code from abstract formal modelsbased on the synchronous model of computation. These modelscan be built using high-level modeling methodologies that hidelow-level architecture details from the developer. The correctnessof the tool has been experimentally validated on models derivedfrom two applications. The experiments also demonstrate that thesynthesized GPGPU code yielded a 28× speedup when executedon a graphics card with 96 cores and compared against asequential version that uses only the CPU.