Not satisfied with Intel's speed, could adding more RAM improve it?
Not satisfied with Intel's speed, could adding more RAM improve it?
I'm pretty sure HBM is better than DDR5, it's integrated memory after all. I would assume that just like any other type of workload, more RAM is needed if you're running out of it, otherwise it would mildly slow down the process. Found a Reddit post:
Interesting, I hadn't realized how severe the problem was. It somewhat clarifies why it was included on consumer GPUs. How extensive is this project you're developing? If latency is indeed the main concern, you might consider using DDR5 and letting software decide between HBM and DDR5 pool in flat mode. From the video:
The system features around 128 channels using DDR4-3200MT/s (HBM2e is more aligned with DDR4 than DDR5) across the two CPUs. So far, I've tested CFD and CFD-EM benchmarks from L1Techs, which have memory sizes of about 10GB and 60GB respectively. These amounts seem limited for full parallel utilization, though I expected better performance compared to dual Epycs. My main workload is expected to reach around 120GB, which might yield noticeable gains with bigger tasks. This is the focus I'm aiming for, but I'm hesitant to invest heavily in memory and doubt a significant speed boost. Additionally, I'm uncertain how my application will manage NUMA nodes when no processors are connected to them, especially in flat mode.
I was really worried about overheating since the HBM is quite temperature-sensitive. I chose dual 360 rads—two 180mm fans each—to keep things cool. I can maintain the CPU below 60°C even when running at full power. My software is configured to use only 64 cores at once, which maximizes performance. Clock speeds will swing between 3.2GHz and 3.5GHz based on workload demands, and power consumption will be quite inconsistent, fluctuating from 600 to 1100 watts during the session.
Great update, it looks like the math library issue was resolved by moving to the latest OneAPI version. Performance improved noticeably on the 60GB test, now matching the speed of the fastest dual socket EPYC system. It makes sense given the recent release of Sapphire Rapids in 2023. I'm interested in testing AMD's math library next, though I'm a bit concerned about HBM support and AMD's software development pace compared to Intel.
I ended up purchasing the DDR5 RAM and it actually shows lower latency compared to HBM memory (~105ns vs ~120ns). ^^ Keep in mind the non-square matrix since NUMA nodes 8-15 are the HBM stacks and don’t have CPU connections to them. Also, my previous assumption was incorrect—the OneAPI update wasn’t responsible; the real reason for the performance boost was a different default damping factor in the solver.