In the search of more energy efficient computing devices that could be assembled to build future exascale systems, this study proposes a chip to chip comparison between a CPU, a GPU and a FPGA, as well as a scalability study on multiple FPGAs from two of the available vendors. The application considered here has been extracted from a production code in material science. This allows for the benchmarking of different implementations to be performed on a production test case and not just theoretical ones. The core algorithm is a matrix free conjugate gradient that computes the total electrostatic energy with an Ewald summation at each iteration. This paper depicts the original MPI implementation of the application, details a numerical accuracy study and explains the methodology followed as well as the resulting FPGA implementation based on MaxCompiler. The FPGA implementation using 40 bits floating point number representation outperforms the CPU implementation both in terms of computing power and energy usage resulting in an energy efficiency more than 15 times better. Compared to the GPU of the same generation, the FPGA reaches 60% of the GPU performance while the ratio of the performance per watt is still better by a factor of 2. Thanks to its low average power usage, the FPGA bests both fully loaded CPU and GPU in terms of number of conjugate gradient iterations per second and per watt. Finally, an implementation using oneAPI is described as well, showcasing a new development environment for FPGA in HPC.