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- §6 Supporting Agentic Workloads: how the fused GQA design extends to agentic fan-out/fan-in; three-layer split (framework/runtime/kernel); Stationary-KV vs Distributed-Q execution policies. - §7 Hardware Performance-Spec Search for GQA: WIP stub (sweep intent over GEMM TFLOPS, MATH-engine ALUs, CUBE↔CUBE and SIP↔SIP BW). - Renumber Discussion/Conclusion/Future-Work to 08/09/10; update main.tex input order and toc.md. - Add Agentic_Runtime_Architecture.md design note; rebuild main.pdf. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
55 lines
3.0 KiB
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55 lines
3.0 KiB
TeX
\section{Discussion: which hardware changes are meaningful}
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\label{sec:discussion}
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Read together, the three studies point to a consistent ranking of where
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hardware investment pays off for attention-centric decoding.
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\paragraph{The composite command is foundational, but indirectly.} Its
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direct effect---driving compute-rich GEMMs to
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\textasciitilde\SI{78}{\percent} of peak---matters most for the
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compute-bound parts of a model (the large feed-forward and projection
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matrices). For attention itself, its more important effect is
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\emph{diagnostic}: by making issue and compute nearly free, it removes the
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overhead that would otherwise mask the true bottleneck, and the fused GQA
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results then show unambiguously that the kernel is data-movement bound.
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Without a cheap, self-routing issue mechanism we would not be able to tell
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whether attention is slow because of compute or because of data movement;
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with it, the answer is clear.
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\paragraph{The communication path is where attention latency actually
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lives.} Every all-reduce and fused-GQA measurement says the same thing:
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the limiting resource is moving and reducing data, not multiplying it. That
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makes the PE\_IPCQ design and the choices around it the highest-value
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hardware levers for this workload:
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\begin{itemize}
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\item \textbf{On-device collectives with a compute/communication
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virtual-channel split.} Performing the reduction on the device, with
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\texttt{vc\_comm} separated from \texttt{vc\_compute} so the reduction
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does not stall the compute DMA, is what lets attention overlap KV
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movement with score computation at all.
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\item \textbf{Fast on-PE staging memory.} Keeping the IPCQ buffer in TCM
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rather than HBM or SRAM is worth \SI{14}{}--\SI{38}{\percent} of
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collective latency at large payloads---a pure placement decision with a
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first-order effect.
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\item \textbf{Wrap-around (torus) inter-device links.} A torus fabric
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buys \SI{20}{}--\SI{25}{\percent} over a mesh or ring at scale by
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shortening the worst-case reduction path.
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\end{itemize}
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\paragraph{Raw MAC throughput is not the constraint for attention.} The
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GQA panels leave the GEMM and vector-math engines two to three orders of
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magnitude below the DMA engine in busy time. Adding MAC area would not move
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decode latency; the workload cannot use it. This is the single most
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actionable finding for an attention-dominated roadmap. The implication is
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that future hardware investment should prioritize communication and
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memory-system efficiency over additional compute throughput for
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attention-dominated inference workloads.
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\paragraph{Caveats.} These conclusions are achievable-kernel results from a
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deterministic model, not E2E measurements; absolute numbers carry the
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model's idealizations (\S\ref{sec:latency}), though the relative rankings
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that drive the recommendations are robust to them. The headline GQA panels
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are also at modest scale (up to four users, single SIP), and one designed
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refinement---the two-composite \textsf{softmax\_merge} decode---is not yet
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in the measured path. Larger-scale and multi-SIP headline runs are 2H work.
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