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PidFopdtBatchRolloutScore Benchmark Report

【免费下载链接】mat-chem-sim-pred面向工业领域，聚焦计算仿真、预测两大核心场景，构建面向流程工业"机理+数据"双轮驱动的领域计算层，推动AI for Science在材料化学领域的深度应用。项目地址: https://gitcode.com/cann/mat-chem-sim-pred

This document records the measured CPU/NPU behavior ofPidFopdtBatchRolloutScore.

Environment

• NPU host:node202
• Device:Ascend910B3, device id0
• CANN:/usr/local/Ascend/ascend-toolkit/latest
• CPU baseline: benchmark program multi-thread mode, 64 threads
• Build:-DCMAKE_BUILD_TYPE=Release -DSOC_VERSION=Ascend910B3 -DRUN_MODE=npu

Correctness

NPU output isbit-identicalto the CPU reference. The candidate-axis SIMD lane width does not change the numerics (each tile is independent), so widening it leavesmax_abs_errandbest_idx_diff_countexactly as the original 256-wide kernel.

Representative verified cases (B=128, S=1024, tile=C):

Thebest_idx_diff_count=1at large C is a genuine argmin tie present in the original 256-wide kernel as well; it is not introduced by the optimization.

Measured Result

node202 / Ascend910B3, B=128, sim_steps=1024, candidate_tile=C, kernel time is the median of repeated runs. NPU kernel ms is stable; the CPU-64 baseline fluctuates on the shared node, so the speedup is given as the observed range.

These are the shipped numbers after both optimizations below (wider lane + fused inner loop).

Optimization 1 - lane-width (kLane 256 -> 768)

The rollout inner loop is a serial time recurrence (y[k+1]depends ony[k]), so the per-timestep chain of vector ops cannot be pipelined across steps. With a narrow SIMD lane each vector instruction processes few candidates (256 floats = 4 compute cycles) yet still pays a fixed ~10-20 cycle issue/latency, so the loop islatency-bound, not throughput-bound. Widening the candidate-axis lane amortises that fixed latency over more candidates per instruction (fewer instructions for the same work), turning the kernel throughput-bound and filling the vector unit.

kLane=768is the largest lane that keeps the full 8 state vectors + 17-block scratch + the 32-slot delay ring (delay spec0..31) + I/O queues within the 192 KB UB budget.

Optimization 2 - inner-loop instruction reduction

The rollout inner loop issued ~37 vector ops per timestep. Two structural changes cut that to ~32 without changing the result:

• the response errore[k+1] = target - y[k+1]is reused as the next step's error, dropping the redundant top-of-looptarget - yrecompute (saves 2 ops/step);
• the pure metric accumulators that do not feed back into the dynamics (IAE,ISE,control_energy) use the fused multiply-accumulateAxpyinstead of a separate multiply + add (saves 3 ops/step).

The integral and the full state recurrence keep their explicit ops, so the simulated trajectory is unchanged; on this hardwareAxpymatches the separate multiply + add bit-for-bit, so the whole result stays bit-identical to the original 256-wide kernel.

Combined before/after

NPU kernel ms, same inputs, bit-identical output across all stages:

Interpretation

After both optimizations the operator iscompetitive on a single card: NPU kernel time is roughly 4-7x the 64-thread CPU baseline at the candidate counts that dominate the tuning sweep, with bit-identical results. This is the current performance baseline for the FOPDT rollout operator.

The speedup comes from a layout change (wider candidate-axis SIMD) plus a lower instruction count per timestep, not from any accuracy trade-off: the time-stepping recurrence and the score definition are unchanged.

Remaining headroom (not applied)

• The settling-time test is still ~6 ops/step; a branch-free cheaper reduction could trim a little more.
• kLane=1024reaches 22.95 ms (C=4096) / 91.31 ms (C=16384) but requires shrinking the delay ring (spec0..31->0..19) to fit UB; usable only when the max delay is <= 19.
• Cross-batch flattening (fill the lane from the next loop's candidates when one loop has fewer candidates than the lane) for the small-C regime; needs per-element plant params.
• Multi-card data parallelism scales absolute time linearly (hardware, not a single-card algorithmic speedup).

【免费下载链接】mat-chem-sim-pred面向工业领域，聚焦计算仿真、预测两大核心场景，构建面向流程工业"机理+数据"双轮驱动的领域计算层，推动AI for Science在材料化学领域的深度应用。项目地址: https://gitcode.com/cann/mat-chem-sim-pred