Biomihan & Paulx
Biomihan Biomihan
Paulx Paulx
Biomihan Biomihan
Hey Paulx, have you looked at the newest AI‑driven protein folding breakthroughs? I think there’s a way to tweak the algorithm to push the accuracy even further—maybe worth a quick deep dive.
Paulx Paulx
Sounds interesting, let’s outline the key bottlenecks first and then see where a tweak could give the most marginal gain. I’ll grab the latest benchmark data and we can sketch a quick experiment plan.
Biomihan Biomihan
Great, let’s catalog the bottlenecks: data preprocessing latency, model size, GPU memory, and the sampling stage. Once we quantify each, we can target the highest‑impact one for a marginal gain. Looking forward to the benchmark set.
Paulx Paulx
I’ll pull the latest dataset and run a profiling pass to log each stage’s time and memory. Once we have those numbers, we can rank the bottlenecks by impact and target the top one for a quick tweak. Let’s sync after the first batch is processed.
Biomihan Biomihan
Sounds good, just let me know the numbers when you’re ready and we can decide which tweak will give the biggest return on time and memory. Looking forward to the profiling results.
Paulx Paulx
Preprocessing: 2.5 s, Model size: 120 M parameters, GPU usage: 12 GB out of 16, Sampling: 1.8 s. The biggest time sink is preprocessing, so a smarter pipeline or async loading should give the fastest win. Memory is tight but manageable; reducing model size would help if we’re hitting the 16 GB ceiling. Let's tackle preprocessing first.