国产超节点密集落地，十万卡集群跑通全栈验证：当算力竞争从"单卡跑分"切换到"系统交付"，国产替代进入真正的深水区

from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Tuple
import math

@dataclass
class AcceleratorCard:
    """单张AI加速卡规格"""
    card_id: str
    vendor: str                          # e.g., "Huawei", "Cambricon", "Moore Threads"
    model_name: str                      # e.g., "Ascend 910C", "MLU590"
    fp8_tflops: float                    # FP8峰值算力（TFLOPS）
    hbm_capacity_gb: float               # 单卡HBM容量（GB）
    hbm_bandwidth_tb_s: float            # 单卡HBM带宽（TB/s）
    tdp_watt: float                      # 单卡TDP功耗（W）
    process_node_nm: int                 # 制程节点（nm）
    domestic_supply_chain: bool          # 是否全国产供应链

@dataclass
class InterconnectSpec:
    """互联规格"""
    topology: str                        # "NVLink", "HCCS", "OEX", "PCIe5"
    link_bandwidth_gb_s: float           # 单向链路带宽（GB/s）
    max_links_per_card: int              # 每卡最大链路数
    die_to_die_latency_us: float         # 芯粒间延迟（μs）
    cross_node_latency_us: float         # 跨节点延迟（μs）

@dataclass
class SuperNodeConfig:
    """超节点配置"""
    node_id: str
    cards_per_node: int                  # 单节点卡数（如64、384、1024）
    card: AcceleratorCard
    interconnect: InterconnectSpec
    total_fp8_eflops: float = 0.0        # 节点总算力（EFLOPS）
    total_hbm_tb: float = 0.0            # 节点总HBM容量（TB）
    bisection_bandwidth_pb_s: float = 0.0 # 对分带宽（PB/s）

    def compute_metrics(self):
        self.total_fp8_eflops = (
            self.cards_per_node * self.card.fp8_tflops * 1e-6
        )
        self.total_hbm_tb = (
            self.cards_per_node * self.card.hbm_capacity_gb / 1024
        )
        per_card_bw = (
            self.interconnect.link_bandwidth_gb_s
            * self.interconnect.max_links_per_card
        )
        self.bisection_bandwidth_pb_s = (
            self.cards_per_node * per_card_bw / 2 / 1e6
        )

class ClusterTopologyAnalyzer:
    """集群拓扑与通信瓶颈分析器"""

    # MoE模型跨卡通信量估算系数（All-to-All阶段）
    MOE_COMM_OVERHEAD_FACTOR = {
        "DeepSeek_V3": 2.8,     # 专家并行通信放大系数
        "Mixtral_8x22B": 2.1,
        "Qwen2_MoE": 1.9,
        "dense_transformer": 1.0  # 基线：无MoE额外开销
    }

    # 有效算力利用率基准线
    MFU_BASELINE = {
        "training_70B+": 0.45,    # 70B以上模型训练MFU基线
        "training_10B_70B": 0.55,
        "inference_long_ctx": 0.35,
        "inference_short_ctx": 0.60
    }

    def estimate_comm_bottleneck(
        self,
        cluster: SuperNodeConfig,
        model_name: str,
        num_experts: int,
        active_experts: int,
        seq_length: int,
        batch_size: int
    ) -> Dict[str, float]:
        """估算通信瓶颈对有效算力的侵蚀"""
        comm_factor = self.MOE_COMM_OVERHEAD_FACTOR.get(
            model_name, 1.5
        )

        # 每卡需承载的通信量（GB）
        per_card_comm_gb = (
            comm_factor
            * num_experts
            * active_experts
            * seq_length
            * batch_size
            * 2  # FP16 = 2 bytes
            / cluster.cards_per_node
            / 1e9
        )

        # 通信耗时（秒）
        per_card_bw_gb_s = (
            cluster.interconnect.link_bandwidth_gb_s
            * cluster.interconnect.max_links_per_card
        )
        comm_time_s = per_card_comm_gb / per_card_bw_gb_s

        # 计算耗时估算（假设FP8利用率80%）
        flops_per_token = 2 * num_experts * 1e9  # 简化
        compute_time_s = (
            flops_per_token * seq_length * batch_size
            / (cluster.card.fp8_tflops * 0.8 * 1e12)
        )

        # 通信占比
        comm_ratio = comm_time_s / (comm_time_s + compute_time_s)

        # 有效MFU折损
        base_mfu = self.MFU_BASELINE.get("training_70B+", 0.45)
        effective_mfu = base_mfu * (1 - comm_ratio * 0.7)

        return {
            "per_card_comm_gb": round(per_card_comm_gb, 2),
            "comm_time_s": round(comm_time_s, 4),
            "compute_time_s": round(compute_time_s, 4),
            "comm_ratio_pct": round(comm_ratio * 100, 1),
            "estimated_effective_mfu": round(effective_mfu, 3),
            "verdict": (
                        "通信瓶颈严重，需升级互联架构"
                        if comm_ratio > 0.4
                        else "31275.t.kuaisou.com"
                        if comm_ratio > 0.2
                        else "通信非主要瓶颈"
                    )
        }

    def score_topology(self, config: SuperNodeConfig) -> Dict[str, any]:
        """对超节点拓扑进行综合评分"""
        config.compute_metrics()
        scores = {}

        # 算力密度评分（EFLOPS/节点）
        scores["compute_density"] = min(
            config.total_fp8_eflops / 2.0, 1.0
        )  # 2 EFLOPS满分

        # 内存容量评分
        scores["memory_capacity"] = min(
            config.total_hbm_tb / 0.5, 1.0
        )  # 512TB满分

        # 互联带宽评分
        scores["bisection_bw"] = min(
            config.bisection_bandwidth_pb_s / 0.5, 1.0
        )  # 0.5 PB/s满分

        # 国产化加分
        domestic_bonus = 0.15 if config.card.domestic_supply_chain else 0.0

        total = (
            scores["compute_density"] * 0.3
            + scores["memory_capacity"] * 0.25
            + scores["bisection_bw"] * 0.3
            + domestic_bonus
        )

        return {
            "node_id": config.node_id,
            "dimension_scores": scores,
            "domestic_bonus": domestic_bonus,
            "total_score":31276.t.kuaisou.com
            "grade": (
                "S" if total >= 0.85
                else "A" if total >= 0.70
                else "B" if total >= 0.55
                else "C"
            )
        }

from dataclasses import dataclass
from enum import Enum
from typing import Dict, List, Optional, Tuple

class PackagingTier(Enum):
    """封装技术层级"""
    TRADITIONAL = "传统封装（DIP/SOP/QFN）"
    MID_ADVANCED = "中端先进封装（Flip-Chip/FOWLP）"
    HIGH_ADVANCED = "高端先进封装（2.5D CoWoS/EMIB）"
    NEXT_GEN = "下一代封装（3D SoIC/混合键合）"

@dataclass
class PackagingCapability:
    """封装产能与能力"""
    facility: str
    tier: PackagingTier
    monthly_capacity_k_wafers: float    # 月产能（千片等效12寸晶圆）
    min_bump_pitch_um: float            # 最小凸块间距（μm）
    max_reticle_area_mm2: float         # 最大光罩面积（mm²）
    hbm_stack_layers: int               # 支持的HBM堆叠层数
    yield_rate_pct: float               # 良率（%）
    domestic_equipment_ratio: float     # 国产设备占比
    cost_per_unit_usd: float            # 单颗封装成本（美元）

@dataclass
class ChipletDesign:
    """芯粒设计方案"""
    design_id: str
    compute_die_nm: int                 # 计算芯粒制程（nm）
    io_die_nm: 31274.t.kuaisou.com      # I/O芯粒制程（nm）
    hbm_die_count: int                  # HBM芯粒数量
    total_transistors_billion: float    # 总晶体管数（十亿）
    target_tflops_fp8: float            # 目标FP8算力（TFLOPS）
    packaging_tier_required: PackagingTier

class AdvancedPackagingBottleneckAuditor:
    """先进封装瓶颈审计器"""

    # 2026年全球先进封装有效产能分布（估算）
    GLOBAL_CAPACITY_SHARE = {
        "TSMC_CoWoS": 0.55,
        "ASE": 0.12,
        "Samsung_I-Cube": 0.08,
        "Intel_EMIB": 0.05,
        "China_Mainland_Total": 0.11,   # 中国大陆合计
        "Others": 0.09
    }

    # 不同封装层级对AI芯片性能的解锁系数
    PERFORMANCE_UNLOCK_FACTOR = {
        PackagingTier.TRADITIONAL: 0.3,     # 传统封装仅解锁30%理论性能
        PackagingTier.MID_ADVANCED: 0.6,
        PackagingTier.HIGH_ADVANCED: 0.85,
        PackagingTier.NEXT_GEN: 0.95
    }

    # 关键材料/设备国产化率（2026年估算）
    DOMESTIC_READINESS = {
        "ABF载板": 0.15,
        "硅中介层": 0.25,
        "底部填充胶": 0.40,
        "光刻胶（EUV）": 0.05,
        "键合设备": 0.20,
        "检测设备": 0.35,
        "电镀设备": 0.55,
    }

    def audit_supply_chain(
        self, design: ChipletDesign
    ) -> Dict[str, any]:
        """审计芯粒设计的封装供应链可行性"""
        risks = []
        blockers = []

        # 检查封装层级需求 vs 国产产能
        required_tier = design.packaging_tier_required
        china_share = self.GLOBAL_CAPACITY_SHARE["China_Mainland_Total"]

        if required_tier in (
            PackagingTier.HIGH_ADVANCED,
            PackagingTier.NEXT_GEN
        ):
            if china_share < 0.15:
                risks.append(
                    f"高端封装国产产能仅占全球{china_share*100:.0f}%，"
                    f"存在严重产能瓶颈"
                )
            if china_share < 0.05:
                blockers.append(
                    "高端封装产能不足，需依赖海外代工，"
                    "面临出口管制风险"
                )

        # 检查计算芯粒制程可行性
        if design.compute_die_nm <= 7:
            blockers.append(
                f"{design.compute_die_nm}nm制程受EUV设备管制，"
                f"需通过先进封装+成熟制程组合方案绕道"
            )
        elif design.compute_die_nm <= 14:
            risks.append(
                "14nm制程DUV多重曝光方案可行但良率爬坡周期长"
            )

        # 材料/设备短板扫描
        for material, domestic_rate in self.DOMESTIC_READINESS.items():
            if domestic_rate < 0.10:
                blockers.append(
                    f"{material}国产化率仅{domestic_rate*100:.0f}%，"
                    f"构成供应链断点"
                )
            elif domestic_rate < 0.30:
                risks.append(
                    f"{material}国产化率{domestic_rate*100:.0f}%，"
                    f"存在供应集中风险"
                )

        # 性能解锁评估
        unlock = self.PERFORMANCE_UNLOCK_FACTOR.get(required_tier, 0.5)
        effective_tflops = design.target_tflops_fp8 * unlock

        return {
            "design_id": design.design_id,
            "required_packaging": required_tier.value,
            "performance_unlock_factor": unlock,
            "effective_fp8_tflops": round(effective_tflops, 1),
            "supply_chain_risks": risks,
            "hard_blockers": 31274.t.kuaisou.com
            "feasibility": (
                "可行但需进口封装" if blockers and not any(
                    "产能不足" in b for b in blockers
                )
                else "需国产替代方案" if blockers
                else "供应链基本自主可控"
            )
        }

    def compare_packaging_routes(
        self,
        monolithic_nm: int,
        chiplet_compute_nm: int,
        chiplet_io_nm: int,
        target_perf_ratio: float = 1.0
    ) -> Dict[str, any]:
        """对比单片方案 vs 芯粒方案的可行性"""
        # 单片方案：受限于最先进制程可得性
        monolithic_feasible = monolithic_nm >=

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需要我继续往下写吗？比如"软件栈与生态：从芯片到模型的一体化交付"、"商业化落地：Token成本与稳定运行的真正战场"等章节。