The 80KB Mystery: Resolving Contradictory Evidence in Triton Kernel Optimization

Introduction

In the high-stakes world of production ML inference optimization, few things are more frustrating than contradictory evidence from trusted sources. When two thorough investigations by capable subagents produce incompatible conclusions about a single critical parameter—the block height of an attention kernel—the engineer must become a detective, tracing the chain of evidence back to its source to resolve the discrepancy before proceeding.

This is exactly the situation captured in message <msg id=13555> of an opencode coding session focused on optimizing DeepSeek-V4-Flash inference on NVIDIA Blackwell (SM120) GPUs. The message is a remarkable window into the reasoning process of an AI assistant grappling with conflicting data about the memory configuration of a Triton attention kernel. At stake is whether a proposed optimization—increasing num_warps from 4 to 8 to improve occupancy and throughput—is even feasible within the tight shared memory (SMEM) budget of the GPU.

The message sits at a critical inflection point in a larger optimization journey. The team has already achieved substantial gains through custom MMA (matrix multiply-accumulate) kernels, PD (prefill-decode) disaggregation, and careful tuning. Now they are probing the next lever: attention kernel occupancy. The assistant has commissioned two subagent investigations into the kernel's configuration, but they disagree on a load-bearing detail: one reports 80KB SMEM usage (implying block_h=32), while the other reports 60KB (implying block_h=16). This 20KB gap determines whether a higher-occupancy num_warps=8 configuration will fit within the GPU's shared memory limits.

This article examines this single message in depth: the reasoning that drives it, the decisions it navigates, the assumptions it tests, and the knowledge it both consumes and produces. It is a case study in evidence-based debugging under uncertainty, where the engineer must reconcile conflicting data, trace through code logic, and design a definitive empirical check before proceeding with a potentially transformative optimization.

The Context: Optimizing Attention on Blackwell

To understand the stakes of message <msg id=13555>, we must first understand the broader optimization context. The team is running DeepSeek-V4-Flash, a Mixture-of-Experts model, on a cluster of NVIDIA RTX PRO 6000 Blackwell GPUs. The Blackwell architecture (compute capability sm_120) introduces new tensor-core capabilities but also presents unique optimization challenges, particularly for the attention mechanism.

The attention kernel in question is a custom Triton kernel implementing sparse multi-head latent attention (MLA) for the decode phase. This kernel—mma_sparse_decode_split—uses a split-K approach to parallelize over the top-k selected tokens, and its performance is critical to overall inference throughput. The kernel is autotuned by Triton, which benchmarks multiple configurations at warmup time and selects the fastest one for the given input shape.

The current production configuration uses BLOCK_T=16, num_warps=4, and num_stages=2, consuming approximately 60-80KB of shared memory and achieving 1 CTA (cooperative thread array) per SM with 4 warps. The proposed optimization is to increase num_warps to 8, which would double the warp count per CTA from 4 to 8, potentially improving latency hiding and throughput by 15-25% at high concurrency (C=60-96).

However, increasing num_warps has implications for register pressure and shared memory usage. The assistant must verify that an 8-warp configuration fits within the SM's resource limits before investing effort in implementing and testing it. This verification depends critically on knowing the actual block_h (block height) parameter, which directly determines shared memory consumption.

The Core Puzzle: Two Subagents, Two Answers

The message opens with the assistant confronting a fundamental discrepancy. The first subagent, analyzing a KINETO trace from a live production run, reported 80KB of shared memory usage, which implies block_h=32. The second subagent, analyzing the Triton cache JSON files on the server, reported 60KB of shared memory usage, which implies block_h=16.

The assistant's reasoning reveals the tension:

"Now I'm reconciling this with the original subagent's findings: they reported 80KB SMEM and 255 registers from the live trace, which matches perfectly if block_h=32 and BLOCK_T=16. The calculation is 1152·32 + 1348·2·16 = 80KB. So the first subagent was reading the live trace correctly, and this subagent's claim of BLOCK_H=16 and 60KB is based on stale or incorrect assumptions."

This is a critical moment of reasoning. The assistant doesn't simply accept one report over the other—it performs a sanity check by computing the expected SMEM from first principles. The formula 1152·32 + 1348·2·16 = 80KB shows that with block_h=32, n_hg=2 (head groups), and BLOCK_T=16, the shared memory calculation yields exactly 80KB. This matches the first subagent's trace reading perfectly, lending credibility to the block_h=32 hypothesis.

But the puzzle deepens. If block_h=32, that implies the kernel is operating on 32 attention heads per CTA. However, the model has 64 total attention heads, and with tensor parallelism (TP) across 4 GPUs, each GPU should handle only 16 heads. So block_h should be min(32, 16) = 16, not 32. How can the kernel be using 32 heads per GPU when each GPU only has 16 heads?

The assistant considers two possibilities:

  1. The total head count (64) rather than the per-GPU head count (16) is being used in the calculation
  2. The KINETO trace was from a different run with different parameters This ambiguity is the central mystery that the message seeks to resolve.

The Performance Regression That Shouldn't Have Happened

Adding urgency to the investigation is a puzzling experimental result. The assistant had previously run an experiment setting _MMA_BLOCK_H=16, expecting it to be a no-op (since the default _MMA_BLOCK_H=32 should already be clamped to 16 by the per-GPU head count). Instead, the experiment showed a consistent 3-9% throughput regression across all batch sizes.

The assistant's reasoning is sharp:

"But here's the puzzle: if my BLOCK_H=16 experiment produced identical kernel configurations to the default, why did it measure 3-9% worse throughput? That's too large to be noise, which suggests either I'm misunderstanding how the block height logic works, or the total head count (64) rather than per-GPU heads (16) is what's being used in the calculation."

This is a textbook example of using unexpected experimental results to challenge assumptions. The regression is too large and too consistent to be measurement noise, so it must indicate that something actually changed in the kernel's behavior. The most parsimonious explanation is that H (the number of heads used in the kernel) is actually 64, not 16, meaning the kernel operates on the full head count before any TP sharding is applied.

The assistant traces through the implications:

"The key insight is that changing block_h affects n_hg (the number of head groups), which cascades into nsplit and the grid structure. Since my measurement showed a real change, H must be 64 in the kernel (not sharded down to 16), making the default block_h=32 and n_hg=2—and my BLOCK_H=16 override changed it to block_h=16 and n_hg=4, altering the wave fill pattern and causing the performance drop."

This is a crucial deduction. If H=64, then the default block_h=32 yields n_hg=2 head groups, while the BLOCK_H=16 override yields block_h=16 and n_hg=4. The change in n_hg alters the grid structure (more CTAs launched), which changes the wave fill pattern and causes the observed regression. The performance drop is not a bug—it's a consequence of a different parallelization strategy that happens to be less efficient for this hardware.

The SMEM Budget Analysis

With the block_h ambiguity unresolved, the assistant performs a thorough analysis of shared memory and register usage for each candidate configuration. This analysis is the heart of the feasibility check for the num_warps=8 proposal.

The current configuration landscape is stark:

"However, looking at the current config set—{BT16/w4/s2}, {BT32/w4/s2}, {BT32/w8/s2}—the BLOCK_T=32 configs exceed 99KB and get pruned as out-of-range, leaving only BT16/w4 as the surviving option."

This is a critical finding. Triton's autotune system prunes configurations whose shared memory usage exceeds the device's capacity (approximately 99KB for Blackwell). Both BLOCK_T=32 configurations—even the num_warps=4 variant—exceed this limit and are silently discarded. This means the current autotune space has only one viable configuration: BLOCK_T=16, num_warps=4, num_stages=2.

The assistant recognizes the opportunity:

"There's no BT16/w8 config in the current set, so the autotuner can only pick BT16/w4 even though BT16/w8 would theoretically fit and offer better occupancy."

The fix is straightforward in concept: add a BLOCK_T=16, num_warps=8 configuration to the tuning space. This configuration would fit within the SMEM budget regardless of whether the true value is 60KB or 80KB, because changing num_warps from 4 to 8 does not significantly change shared memory usage (SMEM is determined primarily by tile shapes, not warp count). The register pressure would actually decrease (from ~255 to ~150 registers per thread) because more warps means fewer registers per warp, making the 8-warp configuration more comfortable within the 256-register limit.

The assistant's reasoning about occupancy is precise:

"At num_warps=8, the register pressure drops to around 150 per thread, which fits within the 256-thread limit and allows 1 CTA to occupy 8 warps per SM."

This is the key insight: with 8 warps per CTA and 1 CTA per SM, the SM achieves 8 warps of occupancy, doubling the current 4 warps. Higher occupancy helps hide memory latency by providing more threads that can be scheduled while others wait for data. For a bandwidth-bound decode kernel, this could translate directly into higher throughput.

The Decision Framework: Option A vs. the Alternatives

The assistant considers several implementation strategies, weighing their risks and benefits:

Option A: Force a single BT16/w8/s2 config. This is the cleanest approach for measurement purposes. By removing all other configurations, the assistant can guarantee that the autotuner selects the 8-warp variant, enabling a direct comparison against the current 4-warp baseline. The risk is that if 8 warps performs worse at low batch sizes (where occupancy is less critical), the single-config approach would regress those cases.

Option B: Add BT16/w8/s2 alongside the existing BT16/w4/s2. This lets the autotuner benchmark both configurations at warmup time and select the faster one for the given input shape. However, the autotuner uses a single key (topk_rounded) that doesn't include batch size, so it would pick one winner for all batch sizes based on the warmup shape. If the optimal choice differs between low and high concurrency, the autotuner would sub-optimize for one regime.

Option C: Implement a batch-aware autotune key. This is the most robust solution but requires modifying Triton's autotune mechanism, which carries risk of breaking CUDA graph capture (autotuning performs timed launches that cannot happen during graph capture).

The assistant leans toward Option A as the first step:

"For the implementation, I'll start with Option A—forcing a single BT16/w8/s2 config to get clean measurements across all batch sizes (C=1 through 96), which will show whether 8 warps helps across the board or hurts at low occupancy."

This is a sound experimental strategy: establish the effect size and direction with a clean measurement before investing in more complex infrastructure. If 8 warps shows a clear win across all batch sizes, the team can simply replace the config. If it helps at high concurrency but hurts at low concurrency, they can invest in the batch-aware autotune key.

The Decisive Check: Reading the Live Triton Cache

The message culminates in a concrete action: reading the live Triton cache to resolve the block_h discrepancy definitively. The assistant constructs a bash command that:

  1. Extracts the block_h derivation logic from the kernel source code (lines 680-700 of flash_mla_sm120_triton.py)
  2. Searches the Triton cache directory for JSON files containing the mma_sparse_decode_split kernel
  3. Parses each cache entry to extract shared (SMEM), num_warps, and kernel name
  4. Sorts and deduplicates the results to show the distribution of cached configurations The command is a model of targeted investigation: it goes directly to the two sources of truth (source code and compiled cache) rather than relying on potentially stale or misinterpreted subagent reports. The source code excerpt reveals the critical line:
block_h = min(_MMA_BLOCK_H, H)

This confirms that block_h is the minimum of the configurable _MMA_BLOCK_H and the head count H. The question of whether H is 64 (total heads) or 16 (per-GPU heads) determines the actual block_h value. The assistant's bash command will resolve this by examining the compiled kernel's SMEM usage: 80KB implies block_h=32 (and thus H=64), while 60KB implies block_h=16 (and thus H=16).

Assumptions Under the Microscope

Throughout this message, the assistant demonstrates a rigorous approach to identifying and testing assumptions. Let me catalog the key assumptions at play:

Assumption 1: Head count sharding. The assumption that H in the kernel is the per-GPU head count (16 at TP4) rather than the total head count (64). This assumption is challenged by the KINETO trace showing 80KB SMEM (which implies block_h=32 and thus H=64).

Assumption 2: Subagent accuracy. The assumption that both subagents performed their analysis correctly. The assistant must weigh their conflicting reports and determine which one is more reliable, using first-principles calculations as a sanity check.

Assumption 3: SMEM budget. The assumption that the 8-warp configuration fits within the SM's shared memory limit. The assistant verifies this through calculation, showing that SMEM is primarily determined by tile shapes (which don't change with num_warps), not warp count.

Assumption 4: Autotune pruning. The assumption that Triton's autotune system correctly prunes out-of-range configurations. The assistant confirms that both BLOCK_T=32 variants exceed 99KB and are silently discarded, leaving only BT16/w4 as the viable option.

Assumption 5: Performance regression cause. The assumption that the -9% regression in the BLOCK_H=16 experiment is due to a real change in kernel behavior (specifically, the change in n_hg and grid structure) rather than measurement noise or environmental factors. The consistency across all batch sizes supports this assumption.

Assumption 6: Cache freshness. The assumption that the Triton cache contains the currently-running kernel configuration. The assistant uses ls -dt to find the most recently modified cache directories, ensuring freshness.

Knowledge Created and Consumed

This message is both a consumer and producer of knowledge in the optimization workflow.

Input knowledge consumed:

The Thinking Process: A Window into Rigorous Debugging

What makes this message particularly valuable as a case study is the transparency of the assistant's reasoning. The "Agent Reasoning" section reveals a multi-layered thought process that moves through several distinct phases:

Phase 1: Recognition of the discrepancy. The assistant immediately identifies the conflict between the two subagent reports and understands its significance for the num_warps=8 feasibility check.

Phase 2: First-principles verification. Rather than accepting either report at face value, the assistant computes the expected SMEM from the kernel parameters, confirming that 80KB matches block_h=32 and 60KB matches block_h=16.

Phase 3: Reconciliation with experimental evidence. The assistant connects the SMEM discrepancy to the puzzling -9% regression from the BLOCK_H=16 experiment, using the regression as evidence that H is likely 64 (not 16) in the kernel.

Phase 4: Analysis of the tuning space. The assistant examines the current set of autotune configurations and discovers that the BLOCK_T=32 variants are pruned, leaving only BT16/w4 as the sole viable option.

Phase 5: Feasibility assessment. The assistant verifies that BT16/w8 fits within SMEM and register limits regardless of whether the true SMEM is 60KB or 80KB.

Phase 6: Experimental design. The assistant formulates Option A (force single config) as the cleanest first step, with a plan to benchmark across all batch sizes.

Phase 7: Definitive verification. The assistant constructs a targeted bash command to read the live Triton cache and resolve the block_h ambiguity once and for all.

This phased approach—recognize, verify, reconcile, analyze, assess, design, verify—is a model of systematic debugging. Each phase builds on the previous one, and the assistant is careful not to jump to conclusions or act on unverified assumptions.

The Broader Significance

While this message is narrowly focused on a specific kernel optimization problem, it illustrates several principles that apply broadly to ML engineering and systems debugging:

1. Trust but verify subagent reports. In a workflow that delegates research to subagents, the assistant must independently verify critical findings, especially when they conflict. First-principles calculations and direct examination of source artifacts (source code, cache files) are essential sanity checks.

2. Use unexpected experimental results as evidence. The -9% regression from what was expected to be a no-op experiment becomes key evidence for resolving the block_h ambiguity. Anomalous results are not just noise to be discarded—they are signals that reveal hidden assumptions.

3. Understand the full implication chain. Changing block_h doesn't just change a single parameter—it cascades through n_hg, nsplit, and the grid structure, potentially altering the entire parallelization strategy. The assistant traces this chain to understand why the regression occurred.

4. Design experiments that produce unambiguous results. Option A (forcing a single config) is designed to produce a clean comparison without the confounding factor of autotune selection. This is preferable to a more complex experiment that might produce ambiguous results.

5. Go to the source of truth. Rather than relying on second-hand reports, the assistant constructs a command to read the live Triton cache directly. This is the most reliable way to resolve the discrepancy.

Conclusion

Message <msg id=13555> captures a pivotal moment in a complex optimization effort. The assistant stands at the intersection of conflicting evidence, unexpected experimental results, and a promising optimization opportunity. Through careful reasoning, first-principles verification, and targeted investigation, it navigates toward a resolution that will enable the next step in the optimization journey.

The message is a testament to the value of systematic thinking in ML engineering. When two trusted sources disagree, the engineer must become a detective, tracing the chain of evidence back to its source. When an experiment produces an unexpected result, the engineer must treat it as a clue rather than an annoyance. When an optimization opportunity presents itself, the engineer must verify feasibility before implementation.

The Triton cache query at the end of the message is the decisive move—the moment when the assistant stops reasoning about the ambiguity and starts resolving it with empirical evidence. This is the essence of evidence-based engineering: form hypotheses, test them, and let the data guide the way forward.

Whether the cache reveals 80KB or 60KB of SMEM usage, the assistant has already done the work to understand both possibilities and their implications. The BT16/w8 configuration fits either way, and the experimental plan is ready. The only remaining question is which block_h value the production kernel is actually using—and that question is about to be answered by the data.