曙光8000(登峰)：全国产十万卡AI超集群的工程密码——从万卡到十万卡，超智融合架构如何打破算力天花板

"""
曙光8000(登峰) 超智融合调度系统核心逻辑
"""

from enum import Enum
from dataclasses import dataclass
from typing import List, Dict, Optional
import heapq
import time


class PrecisionMode(Enum):
    """计算精度模式"""
    FP64 = "fp64"       # 高精度科学计算
    FP32 = "fp32"       # 混合精度
    FP16 = "fp16"       # AI训练
    INT8 = "int8"       # AI推理
    MIXED = "mixed"     # 混合精度自适应


class TaskType(Enum):
    """任务类型"""
    SCIENTIFIC = "scientific"   # 科学计算
    TRAINING = "training"       # 大模型训练
    INFERENCE = "inference"     # AI推理
    SIMULATION = "simulation"   # 工业仿真


@dataclass
class ComputeTask:
    """计算任务描述"""
    task_id: str
    task_type: TaskType
    precision: PrecisionMode
    flops_required: float       # 所需算力 (PFLOPS)
    memory_required: float      # 所需内存 (TB)
    duration_estimate: float    # 预估时长 (秒)
    priority: int               # 优先级 (0-100, 越高越优先)
    submitted_at: float = 0.0
    deadline: Optional[float] = None


class SuperFusionScheduler:
    """
    超智融合调度器
    
    核心能力：在同一套集群上，同时调度FP64科学计算和INT8 AI推理，
    实现"全精度原生一体化"调度。
    """
    
    def __init__(self, total_flops: float, total_memory: float):
        self.total_flops = total_flops      # 总算力 (PFLOPS)
        self.total_memory = total_memory     # 总内存 (TB)
        
        # 当前资源使用
        self.used_flops = 0.0
        self.used_memory = 0.0
        
        # 任务队列（按优先级排序）
        self.task_queue: List[tuple] = []
        
        # 运行中的任务
        self.running_tasks: Dict[str, ComputeTask] = {}
        
        # 调度统计
        self.stats = {
            "scientific_tasks": 0,
            "training_tasks": 0,
            "inference_tasks": 0,
            "simulation_tasks": 0,
            "total_utilization": 0.0,
            "samples": 0,
        }
    
    def submit_task(self, task: ComputeTask):
        """提交任务到调度队列"""
        task.submitted_at = time.time()
        
        # 根据优先级和任务类型计算调度权重
        # 科学计算任务在夜间获得更高权重（利用闲置算力）
        priority_weight = self._compute_priority_weight(task)
        
        heapq.heappush(self.task_queue, (-priority_weight, task))
        self.stats[f"{task.task_type.value}_tasks"] += 1
    
    def _compute_priority_weight(self, task: ComputeTask) -> float:
        """计算调度优先级权重"""
        base_weight = task.priority
        
        # 科学计算任务在非高峰时段获得加成
        if task.task_type == TaskType.SCIENTIFIC:
            current_hour = time.localtime().tm_hour
            if current_hour < 8 or current_hour > 22:
                base_weight *= 1.5  # 夜间加成
        
        # 推理任务获得实时性加成
        if task.task_type == TaskType.INFERENCE:
            base_weight *= 1.3
        
        # 临近deadline的任务获得紧急加成
        if task.deadline:
            remaining = task.deadline - time.time()
            if remaining < 3600:  # 1小时内
                base_weight *= 2.0
        
        return base_weight
    
    def schedule(self) -> List[ComputeTask]:
        """
        执行一轮调度
        
        核心策略：将集群资源动态分配给不同类型的任务，
        确保高优先级任务优先，同时不浪费闲置资源。
        """
        scheduled = []
        available_flops = self.total_flops - self.used_flops
        available_memory = self.total_memory - self.used_memory
        
        temp_queue = list(self.task_queue)
        self.task_queue = []
        
        while temp_queue and available_flops > 0:
            _, task = heapq.heappop(temp_queue)
            
            if task.flops_required <= available_flops and \
               task.memory_required <= available_memory:
                # 分配资源
                self.used_flops += task.flops_required
                self.used_memory += task.memory_required
                self.running_tasks[task.task_id] = task
                scheduled.append(task)
                
                available_flops -= task.flops_required
                available_memory -= task.memory_required
            else:
                # 资源不足，放回队列
                heapq.heappush(self.task_queue, (-self._compute_priority_weight(task), task))
        
        # 更新利用率统计
        self.stats["total_utilization"] += (self.used_flops / self.total_flops)
        self.stats["samples"] += 1
        
        return scheduled
    
    def complete_task(self, task_id: str):
        """完成任务，释放资源"""
        if task_id in self.running_tasks:
            task = self.running_tasks.pop(task_id)
            self.used_flops -= task.flops_required
            self.used_memory -= task.memory_required
    
    def get_utilization(self) -> float:
        """获取当前集群利用率"""
        if self.stats["samples"] == 0:
            return 0.0
        return self.stats["total_utilization"] / self.stats["samples"]
    
    def get_stats(self) -> dict:
        """获取调度统计"""
        return {
            "total_utilization": f"{self.get_utilization()*100:.1f}%",
            "task_distribution": {
                "scientific": self.stats["scientific_tasks"],
                "training": self.stats["training_tasks"],
                "inference": self.stats["inference_tasks"],
                "simulation": self.stats["simulation_tasks"],
            },
            "running_tasks": len(self.running_tasks),
            "queued_tasks": len(self.task_queue),
        }


def simulate_super_fusion():
    """模拟超智融合调度"""
    # 曙光8000(登峰) 10万卡集群算力配置
    # 每张海光AI加速卡 FP16算力约 200 TFLOPS
    # 10万卡 FP16总算力 = 20 EFLOPS = 20000 PFLOPS
    # 全精度覆盖 FP64/FP32/FP16/INT8
    scheduler = SuperFusionScheduler(
        total_flops=20000,   # 20 EFLOPS (以FP16计)
        total_memory=50000,  # 50 PB 总内存
    )
    
    # 模拟混合负载
    import random
    tasks = []
    
    # 科学计算任务（蛋白质折叠）
    for i in range(5):
        tasks.append(ComputeTask(
            task_id=f"sci_{i}",
            task_type=TaskType.SCIENTIFIC,
            precision=PrecisionMode.FP64,
            flops_required=random.uniform(500, 2000),
            memory_required=random.uniform(100, 500),
            duration_estimate=7200,
            priority=80,
        ))
    
    # 大模型训练任务
    for i in range(3):
        tasks.append(ComputeTask(
            task_id=f"train_{i}",
            task_type=TaskType.TRAINING,
            precision=PrecisionMode.FP16,
            flops_required=random.uniform(2000, 5000),
            memory_required=random.uniform(500, 2000),
            duration_estimate=86400,
            priority=90,
        ))
    
    # AI推理任务（实时）
    for i in range(20):
        tasks.append(ComputeTask(
            task_id=f"inf_{i}",
            task_type=TaskType.INFERENCE,
            precision=PrecisionMode.INT8,
            flops_required=random.uniform(50, 200),
            memory_required=random.uniform(10, 50),
            duration_estimate=300,
            priority=60,
        ))
    
    # 工业仿真任务
    for i in range(3):
        tasks.append(ComputeTask(
            task_id=f"sim_{i}",
            task_type=TaskType.SIMULATION,
            precision=PrecisionMode.FP32,
            flops_required=random.uniform(300, 1000),
            memory_required=random.uniform(50, 200),
            duration_estimate=3600,
            priority=70,
        ))
    
    # 提交所有任务
    for task in tasks:
        scheduler.submit_task(task)
    
    # 模拟多轮调度
    print("=" * 70)
    print("曙光8000(登峰) 超智融合调度模拟")
    print("=" * 70)
    print(f"集群总算力: {scheduler.total_flops:,} PFLOPS (FP16)")
    print(f"集群总内存: {scheduler.total_memory:,} TB")
    print(f"提交任务数: {len(tasks)}")
    
    for round_num in range(5):
        print(f"\n--- 调度轮次 {round_num + 1} ---")
        scheduled = scheduler.schedule()
        
        print(f"本轮调度 {len(scheduled)} 个任务:")
        for task in scheduled:
            print(f"  [{task.task_type.value:12s}] {task.task_id} "
                  f"算力={task.flops_required:.0f} PFLOPS "
                  f"精度={task.precision.value}")
        
        # 模拟完成任务
        for task in scheduled[:len(scheduled)//2]:
            scheduler.complete_task(task.task_id)
    
    print(f"\n最终统计:")
    stats = scheduler.get_stats()
    for k, v in stats.items():
        print(f"  {k}: {v}")


if __name__ == "__main__":
    simulate_super_fusion()

// scaleFabric 高速网络模拟
package main

import (
    "fmt"
    "math"
    "sync"
)

// TopologyType defines the network topology
type TopologyType int

const (
    FatTree TopologyType = iota
    Dragonfly
    Torus3D
)

func (t TopologyType) String() string {
    return []string{"FatTree", "Dragonfly", "3D-Torus"}[t]
}

// NetworkConfig represents the scaleFabric network
type NetworkConfig struct {
    Topology        TopologyType
    NumNodes        int
    LinkBandwidth   float64 // Gbps per link
    SwitchRadix     int     // switch port count
    RouterRadix     int     // router port count
}

// NetworkSimulator simulates network performance
type NetworkSimulator struct {
    config    NetworkConfig
    hopCounts []int
    mu        sync.Mutex
}

func NewNetworkSimulator(config NetworkConfig) *NetworkSimulator {
    return &NetworkSimulator{
        config:    config,
        hopCounts: make([]int, 0),
    }
}

// SimulateAllReduce simulates an all-reduce operation across the cluster
func (ns *NetworkSimulator) SimulateAllReduce(messageSizeMB float64) map[string]float64 {
    ns.mu.Lock()
    defer ns.mu.Unlock()
    
    switch ns.config.Topology {
    case FatTree:
        return ns.simulateFatTree(messageSizeMB)
    case Dragonfly:
        return ns.simulateDragonfly(messageSizeMB)
    case Torus3D:
        return ns.simulateTorus3D(messageSizeMB)
    default:
        return nil
    }
}

func (ns *NetworkSimulator) simulateFatTree(msgMB float64) map[string]float64 {
    // FatTree topology: k-ary, (k/2)^2 pods, each with k servers
    // k = switch radix
    k := ns.config.SwitchRadix
    numServers := k * k * k / 4  // total servers in k-ary FatTree
    
    // Hop count: any-to-any in FatTree is at most 2*log_k(numServers) hops
    maxHops := int(2 * math.Log2(float64(numServers)) / math.Log2(float64(k)))
    
    // Bandwidth per flow: shared across multiple paths
    // In FatTree with k switches, there are k/2 parallel paths between any two pods
    parallelPaths := k / 2
    effectiveBW := ns.config.LinkBandwidth * float64(parallelPaths) / float64(numServers)
    
    // All-reduce time: 2 * (data_size / bandwidth) * log2(numServers)
    // Ring all-reduce: 2 * (N-1) * data_size / bandwidth / N
    dataBits := msgMB * 8 * 1024 * 1024 * 8  // MB to bits
    ringTime := 2.0 * dataBits * float64(numServers-1) / 
                (ns.config.LinkBandwidth * 1e9 * float64(numServers))
    
    // Tree all-reduce (used in scaleFabric)
    // Using hierarchical aggregation tree
    treeTime := 2.0 * dataBits * math.Log2(float64(numServers)) / 
                (ns.config.LinkBandwidth * 1e9 * float64(parallelPaths))
    
    return map[string]float64{
        "topology":           float64(FatTree),
        "num_servers":        float64(numServers),
        "max_hops":           float64(maxHops),
        "parallel_paths":     float64(parallelPaths),
        "ring_allreduce_ms":  ringTime * 1000,
        "tree_allreduce_ms":  treeTime * 1000,
        "effective_bw_gbps":  effectiveBW,
    }
}

func (ns *NetworkSimulator) simulateDragonfly(msgMB float64) map[string]float64 {
    // Dragonfly: groups connected in all-to-all, groups connected via optical
    groups := 64
    nodesPerGroup := ns.config.NumNodes / groups
    hopsWithinGroup := 1
    hopsBetweenGroups := 3  // node → group router → optical → group router → node
    
    // scaleFabric uses a hybrid Dragonfly+ topology
    dataBits := msgMB * 8 * 1024 * 1024 * 8
    opticalBW := ns.config.LinkBandwidth * 100  // optical interconnects: 100x
    
    // Hierarchical all-reduce: intra-group + inter-group
    intraGroupTime := dataBits * float64(nodesPerGroup) / 
                      (ns.config.LinkBandwidth * 1e9 * float64(nodesPerGroup/2))
    interGroupTime := dataBits * float64(groups) / 
                      (opticalBW * 1e9 * float64(groups/4))
    totalTime := intraGroupTime + interGroupTime
    
    return map[string]float64{
        "topology":           float64(Dragonfly),
        "groups":             float64(groups),
        "nodes_per_group":    float64(nodesPerGroup),
        "intra_group_hops":   float64(hopsWithinGroup),
        "inter_group_hops":   float64(hopsBetweenGroups),
        "total_allreduce_ms": totalTime * 1000,
        "optical_bw_gbps":    opticalBW,
    }
}

func (ns *NetworkSimulator) simulateTorus3D(msgMB float64) map[string]float64 {
    // 3D Torus: nodes arranged in a 3D grid
    // For 100K nodes: 46x46x46 (approx)
    dim := int(math.Cbrt(float64(ns.config.NumNodes)))
    dataBits := msgMB * 8 * 1024 * 1024 * 8
    
    // In 3D Torus, all-reduce is done in 3 phases (X, Y, Z dimensions)
    phaseTime := dataBits * float64(dim-1) / (ns.config.LinkBandwidth * 1e9 * float64(dim))
    totalTime := 3 * phaseTime
    
    return map[string]float64{
        "topology":           float64(Torus3D),
        "dimension":          float64(dim),
        "total_nodes":        float64(dim * dim * dim),
        "phase_time_ms":      phaseTime * 1000,
        "total_allreduce_ms": totalTime * 1000,
    }
}

func main() {
    fmt.Println("=" * 70)
    fmt.Println("scaleFabric 十万卡高速网络性能对比")
    fmt.Println("=" * 70)
    
    configs := []NetworkConfig{
        {FatTree, 100000, 400, 64, 64},     // 400Gbps per link, 64-port switches
        {Dragonfly, 100000, 400, 64, 128},  // Dragonfly with optical backbone
        {Torus3D, 100000, 400, 64, 64},     // 3D Torus with 400Gbps links
    }
    
    for _, cfg := range configs {
        sim := NewNetworkSimulator(cfg)
        result := sim.SimulateAllReduce(100) // 100MB message
        
        fmt.Printf("\n📡 Topology: %s\n", cfg.Topology)
        fmt.Printf("   10万卡 all-reduce (100MB) 时间:\n")
        
        // scaleFabric uses Dragonfly+ hybrid with optical
        if cfg.Topology == Dragonfly {
            fmt.Printf("   总时间: %.2f ms\n", result["total_allreduce_ms"])
            fmt.Printf("   光互联带宽: %.0f Gbps\n", result["optical_bw_gbps"])
            fmt.Printf("   分组数: %.0f, 每组节点数: %.0f\n", 
                result["groups"], result["nodes_per_group"])
        } else if cfg.Topology == FatTree {
            fmt.Printf("   Ring AllReduce: %.2f ms\n", result["ring_allreduce_ms"])
            fmt.Printf("   Tree AllReduce: %.2f ms\n", result["tree_allreduce_ms"])
            fmt.Printf("   并行路径数: %.0f\n", result["parallel_paths"])
        } else {
            fmt.Printf("   每阶段: %.2f ms\n", result["phase_time_ms"])
            fmt.Printf("   总时间: %.2f ms\n", result["total_allreduce_ms"])
        }
    }
    
    fmt.Println("\n" + "=" * 70)
    fmt.Println("scaleFabric 核心技术指标")
    fmt.Println("=" * 70)
    fmt.Println("""
    ┌─────────────────────────────────────────────────────┐
    │ scaleFabric 类IB原生RDMA                            │
    ├─────────────────────────────────────────────────────┤
    │ 单链路带宽: 400Gbps → 800Gbps (升级中)              │
    │ 光互联骨干: 100× 带宽聚合                           │
    │ 端到端延迟: < 2μs (一跳)                            │
    │ 并行路径: 多路径负载均衡                            │
    │ 拓扑: Dragonfly+ 混合架构                          │
    │ 国产率: 100% (交换芯片+光模块+光纤)                │
    │ 十万卡互联: 无阻塞全互联                           │
    └─────────────────────────────────────────────────────┘
    """)
    
    // Linear scalability analysis
    fmt.Println("线性加速比分析:")
    for scale := 1000; scale <= 100000; scale *= 10 {
        efficiency := 0.98 - 0.08*math.Log10(float64(scale)/1000)
        if efficiency < 0.7 {
            efficiency = 0.7
        }
        fmt.Printf("  %d卡: 加速比=%.1f, 效率=%.1f%%\n", 
            scale, float64(scale)*efficiency, efficiency*100)
    }
}

"""
ParaStor 分布式存储系统性能分析
"""

import math
from dataclasses import dataclass
from typing import Dict, List


@dataclass
class StorageNode:
    """存储节点配置"""
    node_id: int
    nvme_capacity_tb: float     # NVMe SSD容量
    nvme_bandwidth_gbps: float  # NVMe带宽
    network_bandwidth_gbps: float  # 网络带宽
    is_metadata: bool = False   # 是否元数据节点


class ParaStorSimulator:
    """
    ParaStor 分布式存储性能模拟
    
    核心能力：支持大模型训练中的海量数据读写，
    千万级IOPS，PB级吞吐。
    """
    
    def __init__(self, num_nodes: int, replica: int = 3):
        self.nodes = [
            StorageNode(
                node_id=i,
                nvme_capacity_tb=30.72,      # 30.72TB NVMe SSD
                nvme_bandwidth_gbps=28.0,     # PCIe 5.0 x4
                network_bandwidth_gbps=200.0, # 200Gbps RoCE
            )
            for i in range(num_nodes)
        ]
        self.replica = replica
        self.total_capacity = sum(n.nvme_capacity_tb for n in self.nodes)
        self.effective_capacity = self.total_capacity / replica
    
    def simulate_checkpoint(self, model_size_tb: float) -> Dict:
        """
        模拟大模型训练中的checkpoint写入
        
        万亿参数大模型 checkpoint 约 2-10TB
        """
        # 分布式写入：数据分片到所有节点
        shard_size_tb = model_size_tb / len(self.nodes) * self.replica
        
        # 每个节点的写入时间
        # NVMe带宽 + 网络带宽，取较小值
        per_node_bw = min(
            self.nodes[0].nvme_bandwidth_gbps,
            self.nodes[0].network_bandwidth_gbps
        )
        
        # 并行写入所有节点
        shard_size_gb = shard_size_tb * 1024
        write_time_s = shard_size_gb * 8 / per_node_bw  # GB→Gb / Gbps = seconds
        
        # 元数据操作开销
        metadata_overhead = 0.5  # 500ms
        
        return {
            "model_size_tb": model_size_tb,
            "num_nodes": len(self.nodes),
            "shard_size_tb": round(shard_size_tb, 2),
            "per_node_bw_gbps": per_node_bw,
            "write_time_s": round(write_time_s + metadata_overhead, 2),
            "aggregate_bandwidth_tbps": round(
                per_node_bw * len(self.nodes) / 1000, 2
            ),
            "total_capacity_pb": round(self.total_capacity / 1024, 2),
            "effective_capacity_pb": round(self.effective_capacity / 1024, 2),
        }
    
    def simulate_data_loading(self, dataset_size_tb: float) -> Dict:
        """
        模拟训练数据加载
        
        LLM训练需要持续从存储读取数据，
        对IOPS和吞吐都有极高要求。
        """
        # 数据分布在所有节点上
        shard_size_tb = dataset_size_tb / len(self.nodes)
        
        # 训练数据加载：需要高IOPS
        # 典型场景：每秒读取数千个样本
        sample_size_mb = 8  # 每个训练样本约8MB
        samples_per_second = 5000  # 每秒5000个样本
        
        total_samples = dataset_size_tb * 1024 * 1024 / sample_size_mb
        read_bw_gbps = sample_size_mb * 8 * samples_per_second / 1000
        read_bw_per_node = read_bw_gbps / len(self.nodes)
        
        return {
            "dataset_size_tb": dataset_size_tb,
            "total_samples": int(total_samples),
            "samples_per_second": samples_per_second,
            "required_read_bw_gbps": round(read_bw_gbps, 2),
            "per_node_read_bw_gbps": round(read_bw_per_node, 2),
            "io_sustainable": read_bw_per_node < self.nodes[0].nvme_bandwidth_gbps,
        }


def para_stor_benchmark():
    """ParaStor 性能基准测试"""
    print("=" * 70)
    print("ParaStor 分布式存储 IO500 双冠性能分析")
    print("=" * 70)
    
    # 配置：1000个存储节点，3副本
    # 对应曙光8000(登峰)的存储配置
    configs = [
        (100, 3),   # 小规模
        (500, 3),   # 中等规模
        (1000, 3),  # 大规模 (IO500 10节点测试)
        (5000, 3),  # 超大规模 (IO500 全节点测试)
    ]
    
    print(f"\n{'存储规模':<12} {'总容量(PB)':<14} {'有效容量(PB)':<14} {'聚合带宽(Tbps)':<14}")
    print("-" * 60)
    
    for num_nodes, replica in configs:
        store = ParaStorSimulator(num_nodes, replica)
        print(f"{num_nodes}节点×30.72TB  {store.total_capacity/1024:<10.1f}  "
              f"{store.effective_capacity/1024:<10.1f}  "
              f"{min(28,200)*num_nodes/1000:<10.2f}")
    
    # 模拟大模型检查点
    print(f"\n--- 大模型Checkpoint写入模拟 ---")
    store = ParaStorSimulator(1000, 3)
    
    for model_size in [2, 5, 10]:
        result = store.simulate_checkpoint(model_size)
        print(f"  {model_size}TB万亿参数模型: "
              f"写入时间={result['write_time_s']}s, "
              f"聚合带宽={result['aggregate_bandwidth_tbps']}Tbps")
    
    # 模拟训练数据加载
    print(f"\n--- 训练数据加载模拟 ---")
    for dataset_size in [100, 500, 1000]:
        result = store.simulate_data_loading(dataset_size)
        status = "✅ 可持续" if result["io_sustainable"] else "❌ 带宽不足"
        print(f"  {dataset_size}TB数据集: "
              f"需要带宽={result['required_read_bw_gbps']:.1f}Gbps, "
              f"{status}")


if __name__ == "__main__":
    para_stor_benchmark()

// 曙光8000 应用生态分析
package main

import "fmt"

type ApplicationDomain struct {
    Name        string
    Apps        int
    Milestones  []string
}

func main() {
    domains := []ApplicationDomain{
        {
            "大模型训练", 45,
            []string{"万亿参数MoE训练", "千亿参数RLHF", "128K上下文微调"},
        },
        {
            "科学计算(AI4S)", 60,
            []string{"蛋白质折叠模拟", "万亿原子水分子动力学", "百万亿网格湍流模拟"},
        },
        {
            "机器人", 35,
            []string{"具身智能训练", "运动控制仿真", "多模态感知融合"},
        },
        {
            "创新药", 30,
            []string{"分子动力学模拟", "药物筛选", "蛋白质结构预测"},
        },
        {
            "新材料", 25,
            []string{"晶体结构预测", "材料性质计算", "催化剂设计"},
        },
        {
            "量子计算", 20,
            []string{"量子电路模拟", "量子纠错", "变分量子算法"},
        },
        {
            "天文气象", 25,
            []string{"气候模拟", "台风路径预测", "星系演化模拟"},
        },
        {
            "自动驾驶", 30,
            []string{"感知模型训练", "决策规划仿真", "场景重建"},
        },
        {
            "工业仿真", 30,
            []string{"CFD仿真", "结构力学", "电磁场模拟"},
        },
    }
    
    totalApps := 0
    fmt.Println("=" * 72)
    fmt.Println("曙光8000(登峰) 超智融合应用生态")
    fmt.Println("=" * 72)
    
    for _, d := range domains {
        totalApps += d.Apps
        fmt.Printf("\n📊 %s (%d项)\n", d.Name, d.Apps)
        for _, m := range d.Milestones {
            fmt.Printf("  • %s\n", m)
        }
    }
    
    fmt.Println("\n" + "=" * 72)
    fmt.Printf("总计: %d 项超智融合应用优化\n", totalApps)
    fmt.Printf("万卡规模扩展: 70+ 项\n")
    fmt.Printf("领域覆盖: 20+ 个\n")
    fmt.Println("=" * 72)
}