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Not sure if GPU performance engineering is worth the effort? Here’s what it looks like when it works: • 300x faster telecom simulation • 13x speed-up for a video codec • 38% higher throughput on Apple Silicon • Real-time speech synthesis on mobile, no cloud Four projects. Four domains. One outcome, on one video. More on our GPU work: https://lnkd.in/ghQ7qc3m Case-studies: - CloudRF: https://lnkd.in/gFf5k4iA - V-Nova (VC-6): https://lnkd.in/guWwtWB4 - V-Nova (LCEVC): https://lnkd.in/gAhPweJp - Kazakh TTS: https://lnkd.in/gUBVevVY

Some performance problems aren’t about tuning. They’re about rethinking what you’re even trying to compute. In the CloudRF project, we didn’t just “optimize” a simulation. We introduced a new algorithmic approach, one that scaled properly on GPU hardware. The result? 300x speed-up. And a shift from overnight batch runs to quasi real-time interaction. Here’s what that kind of work actually looks like, on the video. More on our GPU work: https://lnkd.in/ghQ7qc3m Case-study: https://lnkd.in/gFf5k4iA

Most GPU workloads on Apple Silicon are still CPU-bound. Which means you’re not getting what you paid for. We worked with V-Nova to fix that, without rewriting their entire decoder. Just the pixel processing path. Rewritten in Metal. Tuned for Apple’s GPU stack. Result: 38% higher throughput. 25% lower CPU usage. Same algorithm. Same hardware. Just better engineering. Here’s how we did it, in the video below. More on our GPU work: https://lnkd.in/ghQ7qc3m Case study: https://lnkd.in/gAhPweJp

You can write GPU code that runs everywhere. But will it run well everywhere? That’s the difference between functional portability and performance portability. And if you’re targeting Apple silicon with OpenCL, you already know which one you’re not getting. We’ve solved this before, without rewriting everything. Here’s how we helped V-Nova get 3× faster on M1, without forking their codebase, on the video. More on our GPU work: https://lnkd.in/ghQ7qc3m Case-study: https://lnkd.in/guWwtWB4

Some performance problems aren’t about tuning. They’re about rethinking what you’re even trying to compute. In the CloudRF project, we didn’t just “optimize” a simulation. We introduced a new algorithmic approach, one that scaled properly on GPU hardware. The result? 300x speed-up. And a shift from overnight batch runs to quasi real-time interaction. Here’s what that kind of work actually looks like, on the video. More on our GPU work: https://lnkd.in/ghQ7qc3m Case-study: https://lnkd.in/gFf5k4iA

GPU optimisation isn’t hard because it’s obscure; it’s hard because it’s honest. CPUs protect you. GPUs expose everything. If you don’t understand the hardware, you’ll get burned. Here’s my take on why GPU optimisation feels brutal and why it’s worth mastering. #GPU #PerformanceEngineering #TechLeadership #ProfilingTools