Instruction-Level Performance Analysis and Optimization Strategies for Constrained AI Accelerators: A Case Study of CMP 170HX

Limited GPUs are widely present in commercial product lines, where a significant gap often exists between their advertised computational power and actual available performance, rendering traditional peak FLOPs-based performance evaluation methods ineffective. Building upon early community observations that “disabling FP32 FMA instructions can yield performance gains on the CMP 170HX,” this paper extends the hypothesis of the restriction mechanism from a single instruction to selective performance suppression coupled across three dimensions: instruction types, numerical precision, and execution units. To systematically characterize the computational behavior of such limited GPUs, this paper proposes an instruction-level performance analysis framework. This includes an instruction throughput and SM normalization model, an effective precision execution factor, and an instruction-level energy density metric to quantify the actual execution efficiency under different instruction and precision combinations. Based on this framework, a systematic instruction-level experimental analysis is conducted on the CMP 170HX (a restricted accelerator based on the GA100 architecture), with comparisons made against unrestricted GPUs of the same generation. Furthermore, this paper summarizes and proposes four categories of operator-level performance circumvention strategies: instruction splitting, intrinsic instruction substitution, algebraic transformation, and execution unit migration, aiming to bypass restricted instruction paths within the legal programming model. Guided by these strategies, a custom operator extension based on PyTorch is implemented and validated across three typical AI inference tasks—Transformer language model inference, Stable Diffusion image generation, and Diffusion Transformer (DiT, e.g., Z-Image-Turbo). Experimental results demonstrate that on the CMP 170HX, the proposed methods can achieve up to approximately 2× end-to-end inference performance improvement without significantly compromising numerical accuracy. This study provides a reproducible instruction-level analysis framework for performance evaluation and engineering utilization of restricted GPUs, offering practical references for the reuse and energy efficiency optimization of low-cost computational resources.