Pilot Processing Network: Optimized Fusion

• Pilot Processing Network is an architecture that fuses compute-intensive operator chains by harnessing Distributed Shared Memory, enhancing on-chip utilization and throughput.
• It uses quantitative dataflow analysis and multi-level memory optimization to balance data movement across registers, SMEM, DSM, and global memory.
• By abstracting DSM collectives within a fusion search engine, the approach achieves significant speedups and reduced memory traffic compared to conventional methods.

A Pilot Processing Network denotes a class of architectures and compiler frameworks that systematically expand and optimize the fusion of compute-intensive operator chains in deep learning—specifically under constraints imposed by modern hardware communication and memory hierarchies. In the context of state-of-the-art accelerator design, such as on NVIDIA H100 GPUs, pilot processing networks leverage inter-core connections (notably, Distributed Shared Memory, DSM) to eliminate traditional bottlenecks in intermediate data movement, thus maximizing on-chip compute utilization and throughput for memory-bound workloads (Huang et al., 15 Dec 2025).

1. Motivation and Historical Context

Deep neural network workloads, particularly those composed of chains of dense operators (e.g., General Matrix Multiplications in Transformer Feed-Forward Networks), are increasingly limited by global memory bandwidth rather than raw compute throughput. Traditional kernel fusion techniques in existing frameworks—cuBLAS, CUTLASS, Chimera, BOLT, Welder, MCFuser—are restricted by the per-Streaming Multiprocessor (SMEM) capacity (227 KB on NVIDIA H100). When the working set of fused intermediates exceeds this scratchpad, fusion stalls, and the intermediate results spill to global memory, resulting in large “write-then-read” penalties and nullifying the benefits of fusion (Huang et al., 15 Dec 2025).

The emergence of Distributed Shared Memory (DSM), an L1.5 cache-level interconnect between SMEMs in a cluster of SMs, provides a high-bandwidth, low-latency on-chip pool that is orders of magnitude larger and faster than any single SMEM. However, until recently, no compiler or kernel fusion technique had been devised to effectively exploit DSM as a first-class target for pilot processing.

2. DSM-Based Communication Abstraction

FlashFuser introduces a DSM-based communication abstraction that exposes three collective primitives, which directly leverage inter-SMEM links within SM clusters:

• dsm_all_exchange(op, data): For a cluster partitioned across the K dimension, it performs an intra-cluster all-reduce (if op=Add) or elementwise multiply (if op=Mul), so each block obtains the fully accumulated C-tile.
• dsm_shuffle(data): Enables exchange among blocks in a Shuffle-Group, ensuring each receives the row or column slice of C necessary for subsequent operations.
• dsm_reduce_scatter(data): Each block holds a partial sum, then intra-cluster reduce-scatter followed by inter-cluster atomic reduction produce the final output tile.

By formalizing these patterns, the abstraction enables fusion strategies to transcend single SMEM limitations, distributing intermediate tensors across an effectively pooled DSM resource (Huang et al., 15 Dec 2025).

3. Dataflow Analysis and Multi-Level Memory Optimization

Efficient pilot processing mandates quantitative modeling of data movement across the memory hierarchy (registers, SMEM, DSM, L2/global). FlashFuser's dataflow analyzer takes as input:

• The fused operator chain DAG with loop dimensions $\mathcal X=\{M,N,K,L\}$
• Loop schedule permutation (with spatial/temporal markings)
• Tile sizes at cluster/block/thread granularity
• Resource mapping

For each configuration, it computes per-tile footprints and global traffic for I/O tensors, greedily allocates intermediate tensor storage from innermost to outermost memory, and, if overflow occurs, spills to DSM or L2. The total data movement $D_V$ at each memory level ℓ determines the levelwise cost $C_\ell(t)=V_\ell(t)/B_\ell$. The optimal tile sizes and schedule are then selected to minimize the maximum $C_\ell(t)$ subject to per-level capacity constraints and divisibility requirements (Huang et al., 15 Dec 2025).

4. Fusion Search Engine Architecture

The search space for pilot processing network plans is combinatorially large (e.g., $2.75\times10^{13}$ candidates for GPT-6.7B). The FlashFuser search engine executes:

• Enumeration & Pruning: Enumerates all valid loop schedules, cluster configurations, and tile sizes, applying pruning rules based on tile divisibility, cluster size limit, activation correctness, resource availability, and fusion dependency constraints.
• Cost Model Evaluation: Invokes the dataflow analyzer to assess the cost model for each plan, maintaining a Top-K leaderboard of lowest-cost candidates.
• Final Profiling: Compiles and profiles Top-K kernels offline, selecting the single best plan for deployment.

Empirically, maintaining a Top-K of 11 candidates yields nearly optimal plan selection with 12–68× reduction in search runtime versus brute-force enumeration for large computational graphs (Huang et al., 15 Dec 2025).

5. Performance Impact and Empirical Evaluation

Systematic exploitation of the pilot processing network approach yields pronounced gains across deep learning workloads:

• Memory-Access Reduction: 58% less global memory traffic than non-fused baselines.
• Kernel Latency: 3.3× speedup over highly-tuned libraries (cuBLAS/CUTLASS) and 4.1× over prior fusion compilers (Chimera, BOLT); convolution chains report up to 6.4× vs. Chimera.
• End-to-End Latency: Integration into LLM inference (SG-Lang) delivers a 1.24× overall latency improvement.
• Bandwidth Stability: DSM primitives (all_exchange, shuffle, reduce_scatter) maintain stable and high utilization across cluster sizes.

These results demonstrate that abstracting DSM-based collectives, modeling hierarchical data flows, and scaling the search space for pilot processing transforms previously SMEM-bounded workloads—such as FFNs with large intermediates—into high-throughput compute-bound pipelines (Huang et al., 15 Dec 2025).

6. Architectural and Generalization Implications

By elevating DSM and other inter-core interconnects to software-visible, first-class resources, pilot processing networks fundamentally enlarge the design scope for kernel fusion and data reuse in memory-bound operator chains. Key architectural best practices for extending this paradigm include:

• Extending abstractions for other emerging on-chip interconnect and scratchpad resources.
• Integrating analytical cost models and capacity constraints into accelerator-aware software compilers.
• Generalizing collective primitives (all-exchange, shuffle, reduce-scatter) for broader classes of operator fusion, beyond GEMM chains.
• Prioritizing pilot processing in hardware-software codesign to better match the growing compute-to-bandwidth gap.

Such systems enable operators previously infeasible to fuse (due to intermediate size) to form scalable, efficient on-chip workloads, directly impacting the attainable performance for both training and inference in large-scale neural models (Huang et al., 15 Dec 2025).

7. Comparison with Related Fusion Strategies

Unlike classical tile-based fusion in prior frameworks, which are bounded by a single SMEM's scratchpad size, pilot processing networks (as exemplified by FlashFuser) pool resources at the cluster level, integrating the hardware DSM and orchestrating communication via formal collective patterns. This allows the fusion of arbitrarily large intermediates without spilling to off-chip memory, breaking the traditional limits that have forced sub-optimal memory traffic patterns in large-scale neural workloads (Huang et al., 15 Dec 2025).

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References:

• "FlashFuser: Expanding the Scale of Kernel Fusion for Compute-Intensive Operators via Inter-Core Connection" (Huang et al., 15 Dec 2025)