Scientific Performance Testing

After loading into Windows 10 and ensuring all the drivers were up to date, I verified that the hardware was what was promised and also running correctly. The relevant screenshots of CPU-Z, GPU-Z, and HWiNFO confirm as much. Some quick benchmarks followed to see if the cooling system was up to par, which we will get to on the next page, before I packed up the system to take to my university for more rigorous tests and comparisons. After all, the GRANDO RM-S in the test configuration is for and justified by demanding loads that are supposed to run for longer periods of time. To do this, the same Windows build was loaded onto an external NVMe SSD along with some programs of interest, including COMSOL, protein folding, Aspen HYSIS, a game and graphics rendering engine, and a machine learning tool with emphasis on real-time language translation coupled with two-way encryption and decryption. These actual tests can not be reproduced by anyone given the sensitive nature of the data sets, and I was thus able to add two other systems for comparison whose specifications are seen below.

Note: This is a supercomputer cluster with several compute and GPU nodes, I selected 10x compute and 2x graphics nodes.



Unsurprisingly, the GRANDO RM-S slots itself firmly between the work PC and supercomputer every single time. Performance also clearly does not scale linearly here, with different tests favoring different configurations. Note that every single test here was chosen to scale with available compute performance, and in some mock situations, differences between the three were minimal. But those are also unlikely to be used by clients of the GRANDO RMS-S, more of which will become apparant through the genereal benchmarks on the following page.