Data-dependent process

CTOs face a core trade-off: how tightly data access patterns align with GPU compute. Every branch, every memory fetch, and every warp stall ripples into cost and value. Optimizing data-dependent process is no longer an engineering detail—it’s a business outcome.

Why it matters

• Cost: Inefficient memory access wastes GPU cycles, inflating cloud bills.
• Performance: Divergent data paths increase tail latency and reduce throughput.
• Value: Each unoptimized step compounds at scale, delaying model convergence and ROI.

Optimization focus

CTOs should push teams to measure GPU utilization at the kernel level. Look at:

• Warp divergence (branching penalties).
• Memory coalescing efficiency.
• SM occupancy vs. actual achieved occupancy.
• GPU dollar cost per training step.

Optimization isn’t academic—aligning data-dependent process means better cloud economics and faster time-to-value.