GPU Design · All levels
Vertex, Tessellation & Geometry Stages: Inputs & Outputs
Inputs & Outputs for Vertex, Tessellation & Geometry Stages.
Inputs and outputs contract
Inputs & Outputs for Vertex, Tessellation & Geometry Stages centers on primitive amplification ratio, stage occupancy, and setup throughput. The objective is to connect profiler evidence to root-cause mechanism and release-safe action.
Use this as the handoff contract across architecture, kernel, compiler, and silicon teams. Ambiguity here creates expensive late-stage rework.
INPUTS
- workload definition and expected KPI target
- kernel launch geometry, compiler flags, and toolchain versions
- hardware assumptions: SM count, memory type, clocks, thermal envelope
- acceptance criteria for throughput, latency tail, and stability
OUTPUTS
- counter and timeline report with reproducible tags
- bottleneck classification (compute, memory, fabric, thermal)
- owner-signed mitigation proposal
- benchmark rerun summary and release recommendationHierarchy map
GPU MEMORY HIERARCHY — Vertex, Tessellation & Geometry Stages
[ Registers ]
latency: 1-2 cycles
|
[ Shared/L1 ]
latency: 20-40 cycles
|
[ L2 ]
latency: 150-250 cycles
|
[ HBM/GDDR VRAM ]
latency: 300ns+ effective
Optimization lens: capacity vs latencyScheduler map
WARP SCHEDULER VIEW — Vertex, Tessellation & Geometry Stages
cycle -> 0 1 2 3 4
eligible [W1,W2,W5] [W2] [W2,W7] [W7] [W3,W7]
issued W1 W2 W7 W7 W3
stall reason - dep wait - mem wait -
Scheduler objective: keep issue slots non-empty.
Focus: eligible warp qualityGPU deep dive
Frame-time stability depends on balancing fixed-function stages with programmable shader pressure.
Concept diagram
GRAPHICS PIPELINE
vertex -> tessellation -> raster -> fragment -> ROP/blendMetric graph
FRAME-TIME PRESSURE
fragment shading load ████████
raster backpressure █████
ROP/blend stalls ████Reports and artifacts
stage occupancy timeline
early-Z efficiency report
ROP queue depth
overdraw heatmap
Mini case study
Async compute overlapped with heavy fragment scenes and triggered ROP queue buildup, causing p99 frame spikes.
Debug branches
Correlate frame spikes with stage-level queues
Validate early-Z effectiveness under real content
Isolate graphics-compute arbitration conflicts
Senior review question
Ask: which metric and benchmark pairing proves this topic is truly closed in production context?
Key takeaways
Always pair micro-kernel metrics with end-to-end workload impact.
Lock toolchain, driver, and launch metadata before comparing performance results.
Common pitfalls
Optimizing occupancy without checking memory-system saturation.
Comparing profiler captures from different driver or compiler builds.
Declaring wins without reproducible accuracy and performance gates.
Handoff explanation
Inputs are not only API parameters or RTL configuration bits. For GPU design, inputs include workload distribution, launch geometry, shader/compiler form, memory layout, clocks, thermal state, SKU target, and runtime policy. Missing any of these makes the same counter mean different things.
Outputs must be decision-ready: primitive amplification ratio, stage occupancy, and setup throughput, the artifact set (graphics stage timeline, primitive count waterfall, and bottleneck attribution), a bottleneck class, owner, expected effect, and regression scope. A handoff that says only "performance improved" is not enough for architecture or silicon signoff.
The safest handoff format is a before/after packet: workload, revisions, counters, traces, root-cause hypothesis, chosen change, rejected alternatives, and rollback criteria.