Unverified50% confidenceTradeoffExact time
Under the GPU's SIMT execution model, control flow divergence (warp divergence) can cause a sharp drop in performance
1
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50%
Confidence
Long-term
Relevance
9/10/2026
First Seen
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Unverified在GPU的SIMT执行模型下，Karatsuba算法的递归分解会引发线程分歧，有效计算效率可能降至峰值的1/4甚至更低77% similarUnverifiedGPU的SIMD架构在推理阶段面临结构性错配，推理时batch size较小导致算力利用率不足，内存带宽成为核心瓶颈73% similarUnverified在GPU的SIMT执行模型下，同一warp中的32个线程需在同一时钟周期执行相同指令，Karatsuba的条件分支会引发线程分歧导致有效计算效率降至峰值的1/4甚至更低70% similarUnverifiedNon-deterministic GPU operations are designed to prioritize performance over determinism; forcing a fixed execution order introduces numerous synchronization barriers that significantly reduce parallel efficiency.68% similarVerifiedIn GPU parallel reduction, because floating-point operations do not satisfy the associative law, differences in the execution order of different warps cause deviations in numerical results, which is the root cause of non-determinism.68% similar
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