The Warp Occupancy Experiment: Deploying and Benchmarking a Single-Config Triton Kernel on Blackwell GPUs

In the high-stakes world of production GPU serving, a single percentage point of throughput improvement can translate into thousands of dollars in hardware savings or dramatically better user experience. This is the context for message [msg 13559], a deceptively simple status update from an AI assistant that belies the extraordinary depth of reasoning, hardware intuition, and experimental discipline behind it. The message reports the deployment and initial benchmarking of a Triton autotune configuration change for the DeepSeek-V4 Flash decode kernel running on NVIDIA Blackwell (sm120) GPUs — a change that replaces a multi-config autotuning set with a single, forced num_warps=8 configuration.

On its surface, the message is a straightforward operational update: the assistant deployed a code change, waited for the service to become healthy, and ran a throughput benchmark across concurrency levels 48, 64, 80, 96, 8, and 1. But to understand why this message was written — and why it matters — we must unpack the intricate chain of reasoning, the conflicting evidence from parallel subagents, the architectural constraints of the Blackwell SM, and the delicate dance between Triton's autotuner and CUDA graph capture that made a single-config approach not just a performance experiment but a correctness necessity.

The Motivation: Why Change the Autotune Configuration?

The message is the culmination of a carefully planned A/B experiment, designated "V1" in the assistant's internal tracking. The problem it addresses is rooted in the GPU occupancy characteristics of the DeepSeek-V4 Flash sparse decode kernel — a custom Triton kernel implementing Multi-head Latent Attention (MLA) with split-K parallelism on sm120 architecture.

The existing autotune configuration, as shown in the source file at line 448, offered three candidates:

triton.Config({"BLOCK_T": 16}, num_warps=4, num_stages=2),
triton.Config({"BLOCK_T": 32}, num_warps=4, num_stages=2),
triton.Config({"BLOCK_T": 32}, num_warps=8, num_stages=2),

However, through subagent-led code analysis in earlier messages ([msg 13554] and [msg 13555]), the assistant discovered a critical fact: the two BLOCK_T=32 configurations were silently dead code. Triton's autotuner prunes configurations whose shared memory (SMEM) footprint exceeds the per-SM limit — and on sm120, the BLOCK_T=32 tiles required over 99KB of shared memory, exceeding the available budget. This meant the autotuner could only ever select the single surviving configuration: BLOCK_T=16, num_warps=4, num_stages=2. The result was that the kernel was running with only 4 warps per SM, when the hardware could support up to 8 warps per SM with the same BLOCK_T=16 tile size.

The hypothesis was straightforward: by adding a BLOCK_T=16, num_warps=8 configuration, the kernel could double its warp occupancy per SM from 4 to 8 warps. This additional occupancy would help hide the latency of scattered fp8 KV-gather operations — a known bottleneck in the sparse attention kernel where the GPU must gather key-value data from scattered memory locations based on the sparse index. More warps means more instruction-level parallelism and better latency hiding, particularly at high concurrency where the GPU is saturated with requests.

The Reasoning: Resolving Conflicting Evidence and Making Pragmatic Choices

What makes this message particularly rich is the reasoning process visible in the assistant's thinking, which reveals a careful navigation of conflicting evidence from two independent subagents. The first subagent, analyzing a Kineto trace from a live run, reported 80KB of shared memory usage and 255 registers — consistent with block_h=32 (where block_h is the number of attention heads processed per block). The second subagent, analyzing the Triton cache JSON, reported 60KB of shared memory and block_h=16. These two findings were irreconcilable: they implied different kernel configurations and, critically, different head-count sharding assumptions.

The assistant's reasoning in [msg 13555] shows a methodical attempt to resolve this discrepancy. The model has 64 attention heads, and with tensor parallelism across 4 GPUs (TP4), one would expect 16 heads per GPU — making block_h=16 the natural value. But the assistant's earlier experiment setting _MMA_BLOCK_H=16 produced a consistent 3-9% throughput regression, which should not have happened if the kernel was already using block_h=16. This contradiction forced the assistant to consider that perhaps H (the number of heads passed to the kernel) was actually 64, not 16 — meaning the kernel operated on the full head count before any TP sharding.

Rather than getting stuck in this ambiguity, the assistant made a pragmatic decision: the change was safe regardless of which value was correct. Whether block_h=16 or block_h=32, the BLOCK_T=16 tile with 8 warps would fit within the SMEM budget (either 60KB or 80KB, both well within the sm120 limit). The grid dimensions would differ based on block_h, affecting n_hg (number of head groups) and nsplit (the split-K factor), but the occupancy improvement from 4 to 8 warps per SM would apply in either case. This is a textbook example of making decisions under uncertainty: identify the invariant that holds across all possible resolutions of the ambiguity, and act on that invariant.

The Decision to Force a Single Config

A further layer of reasoning concerned the experimental design itself. The assistant considered two approaches:

# Single config => no autotune benchmarking (capture-safe).

This decision reflects a deep understanding of the interaction between Triton's runtime and SGLang's graph capture mechanism — knowledge that was gathered through a subagent investigation in [msg 13554] specifically tasked with researching "triton autotune + capture + per-config SMEM."

The Benchmark: What the Message Actually Reports

The message itself reports the results of deploying this change. After a 80-second wait for the decode service to restart and become healthy (verified by polling the /v1/chat/completions endpoint until HTTP 200), the assistant ran the benchmark tool across concurrency levels in a deliberate order: high concurrency first (48, 64, 80, 96) to get the target data early, followed by low concurrency (8, 1) to check for regressions.

The results at the four high-concurrency levels are:

| Concurrency | Aggregate Throughput | Per-Request Throughput | P50 Latency | Wall Time | |-------------|---------------------|----------------------|-------------|-----------| | C=48 | 669.3 tok/s | 681.9 tok/s | 17.48s | 73.4s | | C=64 | 786.3 tok/s | 807.9 tok/s | 19.67s | 83.3s | | C=80 | 804.5 tok/s | 842.5 tok/s | 23.88s | 101.8s | | C=96 | 785.1 tok/s | 810.1 tok/s | 30.24s | 125.2s |

These numbers show the system scaling reasonably well up to C=80, where aggregate throughput peaks at ~804 tok/s, then slightly declining at C=96 to ~785 tok/s — suggesting the GPU is approaching saturation. The per-request throughput (which accounts for the number of concurrent requests) shows a similar pattern, peaking at C=80 with 842.5 tok/s per request.

The message cuts off at "C..." suggesting the output was truncated — the low-concurrency results (C=8 and C=1) were presumably reported in a subsequent message or continuation. This truncation is itself noteworthy: it reflects the real-time, streaming nature of the assistant's output, where long benchmark results may be split across messages.

What the Message Does Not Show: The Missing Baseline

A critical observation is that this message reports only the new configuration's performance. It does not include a direct baseline comparison to the previous num_warps=4 configuration. The assistant's reasoning mentions that the baseline was established earlier (the "BLOCK_H=16 experiment" referenced in [msg 13555] showed 3-9% worse throughput, and the production config was running at num_warps=4), but the reader of this message alone cannot determine whether the change was beneficial.

This is not a flaw in the message — it is a reflection of the iterative experimental methodology. The assistant is running the V1 experiment to gather data, and the comparison will come when the results are analyzed against the previously collected baseline. The message is a checkpoint, not a conclusion.

The Broader Context: A System Under Continuous Optimization

To fully appreciate this message, one must understand where it fits in the larger narrative of segment 72. The assistant has just emerged from a grueling debugging session that root-caused a high-concurrency tool-call corruption bug to a multi-stream-overlap race condition in CUDA graph capture (see chunk 1 of segment 72). That investigation involved deploying canary instrumentation, running A/B tests across fp8 vs bf16 and eager vs captured modes, and ultimately disabling SGLANG_OPT_USE_MULTI_STREAM_OVERLAP to eliminate the corruption entirely.

With that crisis resolved, the user's priority shifted to improving decode throughput scaling — specifically, getting the system to scale efficiently from C60 to C90. The assistant had created a comprehensive project plan based on live bottleneck analysis showing that decode was latency/occupancy-bound, with a step time formula of 18ms + 1.05ms/req. The num_warps=8 experiment was one of several levers being evaluated, alongside Two-Batch Overlap (which was definitively ruled out as infeasible) and re-enabling the overlap scheduler (which showed modest gains but triggered a structural desync hazard).

The message thus represents a single data point in a systematic optimization campaign — one that balances experimental rigor, production safety, and the pragmatic need to make progress despite unresolved ambiguities.

Input Knowledge Required

To fully understand this message, a reader needs familiarity with several domains:

  1. GPU Architecture: Understanding of streaming multiprocessors (SMs), warps, occupancy, shared memory (SMEM), and how these constrain kernel configuration. The concept that more warps per SM can improve latency hiding by providing more independent instruction streams is central to the hypothesis being tested.
  2. Triton Compiler Internals: Knowledge of how Triton's @triton.autotune decorator works — that it benchmarks each configuration at warmup time, prunes out-of-resource configs silently, and skips benchmarking entirely when only one config is provided. The distinction between the autotune key (which determines when re-benchmarking occurs) and the configs themselves is also relevant.
  3. CUDA Graph Capture: Understanding that CUDA graphs capture a sequence of GPU operations for replay, and that operations involving CPU synchronization (like Triton's autotuning benchmarks) cannot occur during capture. This is why a single config is "capture-safe."
  4. DeepSeek-V4 Architecture: Knowledge of Multi-head Latent Attention (MLA), sparse attention with top-k indexing, and the split-K parallelism pattern used in the decode kernel. The concept of block_h (number of heads per block), n_hg (number of head groups), and how these affect grid dimensions is specific to this kernel.
  5. SGLang Serving Framework: Familiarity with the service architecture (prefill-decode disaggregation), the CUDA graph capture mechanism, and the benchmarking tools used to measure throughput.

Output Knowledge Created

This message produces several valuable outputs:

  1. Benchmark data: Throughput measurements at C=48, 64, 80, and 96 for the new configuration, providing the raw material for comparison against the baseline.
  2. Operational confirmation: Evidence that the service restarted successfully, became healthy, and handled the benchmark workload without errors (all runs show errs=0).
  3. Experimental methodology: The ordering of benchmark cases (high concurrency first) and the polling strategy for service readiness are documented as reproducible procedures.
  4. A resolved ambiguity (partially): While the message doesn't directly resolve the block_h discrepancy, the assistant's reasoning shows a clear path forward: the SMEM footprint of the running kernel will be read from the Triton cache JSON in a subsequent verification step, which will definitively determine whether block_h=16 or block_h=32.

Assumptions and Potential Pitfalls

The assistant makes several assumptions that deserve scrutiny:

  1. The BLOCK_T=32 configs were truly OOR-pruned: This finding came from subagent analysis and was not directly verified on the live system before making the change. If the subagent's analysis was incorrect — for example, if a different SMEM budget was available due to compiler flags or architecture variants — the original configs might have been viable, and removing them could have discarded a potentially better configuration.
  2. The single-config approach is optimal for all batch sizes: By forcing num_warps=8 universally, the assistant sacrifices the ability to choose num_warps=4 for low-concurrency scenarios where fewer warps might be sufficient and lower register pressure could improve performance. The benchmark at C=8 and C=1 (which appear to be cut off from this message) will be critical for detecting such regressions.
  3. The benchmark results are representative: The benchmark uses a fixed max_tokens=256 and a specific request pattern. Real production traffic may have different characteristics — varying sequence lengths, tool-calling patterns, or inter-arrival times — that could change the performance profile.
  4. The service is stable with the new config: The benchmark ran successfully, but longer-running stress tests would be needed to confirm that the new kernel configuration doesn't introduce numerical issues, memory leaks, or other stability problems.

Conclusion

Message [msg 13559] is far more than a simple "deployed and benchmarked" status update. It is the visible tip of a deep iceberg of reasoning — one that involves resolving conflicting evidence from parallel subagents, understanding the intricate interaction between Triton's autotuner and CUDA graph capture, making pragmatic decisions under uncertainty, and executing a carefully designed experiment to improve GPU occupancy.

The message captures a moment of transition: from the hypothesis-forming and research phase into the measurement and analysis phase. The assistant has committed to a specific experimental design (single config, num_warps=8), deployed it to a production-like environment, and collected the first batch of data. Whether the change proves beneficial or detrimental, the methodology — rigorous, evidence-driven, and safety-conscious — is a model for how to approach performance optimization in complex GPU serving systems.

The results themselves — peak throughput of ~804 tok/s at C=80, with graceful scaling up to that point — provide the raw material for the next step in the optimization journey. Whether this particular lever (warp occupancy) moves the needle will be determined when these numbers are compared against the baseline. But regardless of the outcome, the process has already produced valuable knowledge: about the kernel's SMEM footprint, about Triton's autotuning behavior under graph capture, and about the art of making decisions when the data is incomplete.