The problem
What happens when some blocks finish early and the remaining work can’t be redistributed cheaply?
What current research shows
GPU execution assigns work in blocks and warps, while kernel resource limits and dependencies constrain what can run concurrently. When work sizes or paths differ, active resources can become unevenly occupied.
Where the evidence stops
Load imbalance can arise from the algorithm, data distribution, resource limits, or dependencies. A scheduler can’t recover work that the program doesn’t expose.
What Valen Systems is testing
Measure block completion spread, SM occupancy over time, tail duration, and useful work under balanced and intentionally skewed workloads.