Hey everyone, long time lurker here.
I would like to ask for some help with a new workstation build for scientific computing (both CPU and GPU heavy computations). This build is going to be mainly used for FDTD calculations (CPU heavy tasks) and some deep learning with TensorFlow (GPU heavy tasks) at my university. I have some experience with simple builds, but I feel that this is a bit over my head. My current budget at the moment is around $12k-18k.
I did some digging around to find the requirements for such system. Apparently, the total memory bandwidth or overall data transfer rate between the RAM and CPU is crucial for CPU heavy tasks on systems with many cores.
https://kb.lumerical.com/en/index.html?installation_and_setup_hardware-specifications-and-be.html
Another article suggests determining the maximum amount of memory for typical design problems first and designing the system from there.
https://www.lumerical.com/support/whitepaper/parallel_processing_fdtd_solutions.html
Following the advice above, I evaluated that our memory need for the time being is around 128-256GB (with ability to expand it later).
Memory
128-256 GB DDR4 2400MHz
CPU/Motherboard
Currently my biggest struggle. I am looking at Intel Xeon's for a possibly 20-30 core solution.
How do I determine the total memory bandwidth between RAM and CPU to choose the optimum CPU for fastest calculations?
Should I get one processor with more cores or should I go for a double-processor setup with lower number of cores per processor?
GPU - NVIDIA Tesla's are better suited for GPU computing, but since CPU computations are more important for me, I opted out for a GTX 1080 Ti
ASUS ROG STRIX GeForce GTX 1080 TI 11GB $800 (https://www.amazon.com/STRIX-GeForce-Gaming-Graphics-ROG-STRIX-GTX1080TI-11G-GAMING/dp/B06XY25VTC)
Storage
Samsung 850 EVO 2TB $700 (https://www.amazon.com/dp/B010QD6W9I/ref=twister_B00PRDMHLU?_encoding=UTF8&psc=1)
Seagate BarraCuda Pro 10TB $500 (https://www.amazon.com/Seagate-BarraCuda-3-5-Inch-Internal-ST10000DM0004/dp/B01IA9H22Q)
All comments and suggestions are welcome.
