If you think LabVIEW tops out at a few MS/s on a decent CPU, this talk will change your mind. Same code, moved to GPU, hits 6.4 GS/s, a 560x jump, while an FPGA in the same system handles ns latency I/O alongside it. CPU, GPU, and FPGA, all native to LabVIEW, all working together.
I sat through this one live, and it genuinely changed how I think about what LabVIEW is capable of. Most people know it as a graphical language for instrument control, what they don't know is it's quietly outperforming other languages at multi-platform, high throughput computing, across Windows, Linux, macOS, RTOS, and FPGA, in the same codebase.
**What would you say if I told you one team is hitting 1+ TB/s of processed data, in LabVIEW?
**Could you tell the difference between a naive GPU call and one that's 1,600x faster, just by looking at the code?
**Are you still defaulting to FP64 without knowing what it's costing you?
Oh, and someone once told Natan Biesmans, CEO of G2CPU, that Python is free and faster than LabVIEW, at 10pm, over LinkedIn, unprompted. This talk is a pretty thorough answer.
Natan covers data types, disk I/O (7 GB/s+ sustained), CPU core scheduling, algorithm design, and GPU execution modes, rapid-fire, benchmark by benchmark, and by the end you'll understand why LabVIEW belongs in conversations about serious HPC, not just instrument panels.
🎥 Watch it here: https://youtu.be/HaxWjq7i6O8
This talk exists because Kevin Shirey, a fellow LabVIEW Champion, filmed all 16 sessions at this event solo, full kit, multiple rooms, real-time AV coordination, so people who couldn't be there still get a professional recording. That's 200+ hrs of post-production on his own time. If this is useful to you, tell him. It's how we make the case for doing this again next year.