Mixed-Precision Matmul: Throughput Gains with Accuracy Controls: Reports and Metrics

Reports and metrics

Reports and Metrics for Mixed-Precision Matmul: Throughput Gains with Accuracy Controls is anchored on Tokens or inferences per second at fixed quality target and overflow or underflow incidence per training or inference step.. Convert measurements into mechanism-backed decisions with clear owner accountability.

A useful report explains why movement happened, not only that movement happened.

Evidence matrix

EVIDENCE MATRIX - Mixed-Precision Matmul: Throughput Gains with Accuracy Controls

+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| Evidence                    | Tells you                      | Does not prove                 | Next action               |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+
| occupancy + timeline traces | where utilization is lost      | precise root cause             | map to memory and schedule|
| cache/SRAM/bandwidth stats  | data movement pressure         | model-level quality impact     | correlate with quality run|
| counter + profile alignment | bottleneck class confidence    | rollout safety                 | run full regression matrix|
| thermal/power telemetry     | sustained operating envelope   | correctness closure            | pair with verification    |
| before/after scenario pack  | mitigation movement            | long-tail stability            | execute guardrail replay  |
+-----------------------------+--------------------------------+--------------------------------+---------------------------+

• Track Tokens or inferences per second at fixed quality target and overflow or underflow incidence per training or inference step. on representative production workloads.

• Include build/runtime metadata in every report header.

• Correlate throughput, latency, and quality before rollout decisions.

• Call out contradictory evidence explicitly.

AI accelerator deep dive

Sparse and mixed-precision wins require stable compiler lowering and runtime support coverage.

Concept diagram

SPARSE TENSOR EXECUTION

model graph -> compiler lower -> sparse or dense kernel path -> runtime scheduling -> SLA outcome

Metric graph

SPARSE REALITY CHECK

nominal sparsity      ████████████
real speedup          ██████
fallback overhead     █████

Metrics and artifacts to collect

• tensor-core occupancy

• fallback kernel rate

• sparse metadata overhead

• quality guardrail drift

Mini case study

Structured sparsity improved one layer family while unsupported operators forced dense fallbacks elsewhere.

Debug branches

• Track dense fallback counters

• Audit sparse-format conversions

• Check precision policy with quality gates

Senior review question

Ask: which first-principles bottleneck class explains the symptom, and what artifact proves it reproducibly?

Key takeaways

• Tie every accelerator claim to a reproducible workload slice and one primary metric trend.

• Prefer bounded fixes with clear owner and rollback boundary over broad tuning bundles.

Common pitfalls

• Optimizing synthetic kernels without production-shape validation.

• Reading average latency while ignoring p95 and p99 behavior.

• Declaring sparse or precision wins without fallback and quality evidence.

Report interpretation

Mixed precision increases matrix throughput by using lower-precision inputs such as FP16, BF16, or INT8 while accumulating in wider formats to reduce numerical error. Hardware tensor cores expose fast paths for these formats, but stability still depends on scaling strategy, calibration, and reduction order. For training, techniques like dynamic loss scaling and selective higher-precision layers preserve convergence while capturing most performance gains. For inference, quantization-aware calibration and outlier handling determine whether speedups hold at production accuracy thresholds. A useful explanation always ties observed symptom to a repeatable path where useful work was blocked, delayed, or diluted by overhead.

Use Tokens or inferences per second at fixed quality target and overflow or underflow incidence per training or inference step. as an alarm, then anchor action using hard evidence such as Precision policy sheet mapping operators to compute and accumulation formats with quality guardrails..

Sparse and mixed-precision gains hold only when software paths preserve hardware-friendly execution. Senior reviews expect a chain of proof: workload intent -> mapping -> hardware behavior -> product impact.

For Mixed-Precision Matmul: Throughput Gains with Accuracy Controls, reports should explain why Tokens or inferences per second at fixed quality target and overflow or underflow incidence per training or inference step. moved and which path consumed budget first.