FPGA architectures include increasingly complex arithmetic operators and optimized hard IPs, such as memory subsystems and Networks-on-Chip (NoC). This evolution leads to higher compute density also linked with high memory bandwidth. It represents an opportunity to tailor an architecture to niche application needs while being competitive with a costly ASIC implementation. More specifically, scientific computing requires high precision (> 32 bits) floating point computation. However, GPU vendors are progressively favoring low precision performance for AI needs, and are even phasing out support for 64-bit floating point compute. We present an analytical study motivating the need to investigate the implementation of an open source 64-bit GPGPU architecture on a state of the art FPGA, as an alternative to GPUs for scientific computing.