L1 Cache & Texture Path: Interview Drills

Interview drills

Interview Drills for L1 Cache & Texture Path centers on L1 hit rate, texture cache efficiency, and cache-thrashing incidents. The objective is to connect profiler evidence to root-cause mechanism and release-safe action.

PROMPT
You see L1 hit rate, texture cache efficiency, and cache-thrashing incidents on L1 Cache & Texture Path. Walk through root cause and release decision.

STRONG ANSWER
1. Names failing workload/scene and first broken metric.
2. Explains L1 and texture caches reduce VRAM traffic for spatially/temporally local accesses; working-set and access stride drive hit quality.
3. Requests cache hit/miss profile, access stride study, and texture-path latency report.
4. Proposes bounded fix + owner + validation matrix.

WEAK ANSWER
Suggests generic tuning without SIMT, warp, cache, or interconnect evidence.

Whiteboard diagram

L1/texture position in hierarchy

GPU MEMORY HIERARCHY — L1 Cache & Texture Path

                [ Registers ]
              latency:   1-2 cycles
                     |
                [ Shared/L1 ]
              latency:  20-40 cycles
                     |
                    [ L2 ]
              latency: 150-250 cycles
                     |
             [ HBM/GDDR VRAM ]
              latency: 300ns+ effective

Optimization lens: show texture path as a locality optimizer before VRAM

Debug tree to narrate

ROOT-CAUSE TREE — L1 Cache & Texture Path

L1 hit rate, texture cache efficiency, and cache-thrashing incidents regressed
        |
  reproducible on replay?
      /              \
    no                yes
    |                  |
env/test noise    counter triage
                   |
             compute-bound or memory-bound?
                /                  \
             compute            memory/interconnect
             issue stalls       cache/NoC/DRAM stalls

Stop at first failing mechanism, then patch.

GPU deep dive

Bandwidth wins come from coalescing and locality discipline, not peak-memory specs alone.

Concept diagram

MEMORY HIERARCHY

register -> shared/L1 -> L2/LLC -> HBM/GDDR
access pattern quality decides latency

Metric graph

BANDWIDTH UTILIZATION

requested BW  ███████████
effective BW  ████████
wasted BW     ███

Reports and artifacts

• L1/L2 hit-rate report

• HBM efficiency counters

• coalescing transaction log

• shared-memory bank audit

Mini case study

Stencil kernel sat at 43% of peak HBM due to uncoalesced loads; layout rewrite recovered 1.6x effective bandwidth.

Debug branches

• Check transactions per request at warp granularity

• Classify cache-thrash versus true DRAM saturation

• Audit shared-memory bank conflicts before algorithm rewrites

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.

Interview answer expansion

A strong interview answer for L1 Cache & Texture Path starts with the workload and metric, then states the mechanism in plain language: L1 and texture caches reduce VRAM traffic for spatially/temporally local accesses; working-set and access stride drive hit quality.

Then it gives a measurement plan. Good answers name lane masks, issue slots, cache/transaction counters, memory-controller state, NoC congestion, thermal/DVFS telemetry, or stage queues depending on the topic.

Finally, it proposes one bounded fix and explains regression risk. GPU interviews reward tradeoff ownership: what improves, what may regress, and how you would know before tapeout or release.