Sugon 8000 Summit: The Engineering Secrets of China's First 100K-Card All-Domestic AI Supercluster — How Super-Fusion Architecture Breaks the Compute Ceiling

"""
Sugon 8000 (Summit) Super-Fusion Scheduler Core Logic
"""

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


class PrecisionMode(Enum):
    FP64 = "fp64"
    FP32 = "fp32"
    FP16 = "fp16"
    INT8 = "int8"
    MIXED = "mixed"


class TaskType(Enum):
    SCIENTIFIC = "scientific"
    TRAINING = "training"
    INFERENCE = "inference"
    SIMULATION = "simulation"


@dataclass
class ComputeTask:
    task_id: str
    task_type: TaskType
    precision: PrecisionMode
    flops_required: float
    memory_required: float
    duration_estimate: float
    priority: int
    submitted_at: float = 0.0
    deadline: Optional[float] = None


class SuperFusionScheduler:
    """
    Super-Fusion scheduler that dynamically allocates cluster resources
    across FP64 scientific computing and INT8 AI inference simultaneously.
    """
    
    def __init__(self, total_flops: float, total_memory: float):
        self.total_flops = total_flops
        self.total_memory = total_memory
        self.used_flops = 0.0
        self.used_memory = 0.0
        self.task_queue: List[tuple] = []
        self.running_tasks: Dict[str, ComputeTask] = {}
        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):
        task.submitted_at = time.time()
        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:
        base_weight = task.priority
        # Scientific tasks get bonus during off-peak hours (night)
        if task.task_type == TaskType.SCIENTIFIC:
            current_hour = time.localtime().tm_hour
            if current_hour < 8 or current_hour > 22:
                base_weight *= 1.5
        # Inference tasks get real-time bonus
        if task.task_type == TaskType.INFERENCE:
            base_weight *= 1.3
        # Deadline urgency
        if task.deadline:
            remaining = task.deadline - time.time()
            if remaining < 3600:
                base_weight *= 2.0
        return base_weight
    
    def schedule(self) -> List[ComputeTask]:
        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:
                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:
                heapq.heappush(self.task_queue, 
                    (-self._compute_priority_weight(task), task))
        
        self.stats["total_utilization"] += (self.used_flops / self.total_flops)
        self.stats["samples"] += 1
        return scheduled
    
    def complete_task(self, task_id: str):
        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:
        if self.stats["samples"] == 0:
            return 0.0
        return self.stats["total_utilization"] / self.stats["samples"]


def simulate_super_fusion():
    """Simulate the super-fusion scheduler"""
    scheduler = SuperFusionScheduler(
        total_flops=20000,    # 20 EFLOPS (FP16)
        total_memory=50000,   # 50 PB total memory
    )
    
    tasks = []
    # Scientific computing tasks (protein folding)
    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))
    
    # LLM training tasks
    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 inference tasks (real-time)
    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))
    
    # Industrial simulation
    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))
    
    for task in tasks:
        scheduler.submit_task(task)
    
    print("=" * 70)
    print("Sugon 8000 Super-Fusion Scheduler Simulation")
    print("=" * 70)
    print(f"Total compute: {scheduler.total_flops:,} PFLOPS (FP16)")
    print(f"Total memory:  {scheduler.total_memory:,} TB")
    print(f"Tasks submitted: {len(tasks)}")
    
    for round_num in range(5):
        print(f"\n--- Schedule Round {round_num + 1} ---")
        scheduled = scheduler.schedule()
        print(f"Scheduled {len(scheduled)} tasks:")
        for task in scheduled:
            print(f"  [{task.task_type.value:12s}] {task.task_id} "
                  f"FLOPs={task.flops_required:.0f} PFLOPS "
                  f"Precision={task.precision.value}")
        for task in scheduled[:len(scheduled)//2]:
            scheduler.complete_task(task.task_id)
    
    print(f"\nFinal utilization: {scheduler.get_utilization()*100:.1f}%")


if __name__ == "__main__":
    simulate_super_fusion()

// scaleFabric Network Performance Simulation
package main

import (
    "fmt"
    "math"
)

type TopologyType int
const (
    FatTree TopologyType = iota
    Dragonfly
    Torus3D
)

type NetworkConfig struct {
    Topology      TopologyType
    NumNodes      int
    LinkBandwidth float64 // Gbps per link
    SwitchRadix   int
}

func simulateAllReduce(cfg NetworkConfig, msgMB float64) map[string]float64 {
    dataBits := msgMB * 8 * 1024 * 1024 * 8
    
    switch cfg.Topology {
    case Dragonfly:
        groups := 64
        nodesPerGroup := cfg.NumNodes / groups
        opticalBW := cfg.LinkBandwidth * 100
        
        intraGroupTime := dataBits * float64(nodesPerGroup) / 
            (cfg.LinkBandwidth * 1e9 * float64(nodesPerGroup/2))
        interGroupTime := dataBits * float64(groups) / 
            (opticalBW * 1e9 * float64(groups/4))
        totalTime := intraGroupTime + interGroupTime
        
        return map[string]float64{
            "total_allreduce_ms": totalTime * 1000,
            "optical_bw_gbps":   opticalBW,
        }
        
    case FatTree:
        k := cfg.SwitchRadix
        numServers := k * k * k / 4
        parallelPaths := k / 2
        effectiveBW := cfg.LinkBandwidth * float64(parallelPaths) / float64(numServers)
        treeTime := 2.0 * dataBits * math.Log2(float64(numServers)) / 
            (cfg.LinkBandwidth * 1e9 * float64(parallelPaths))
        
        return map[string]float64{
            "tree_allreduce_ms": treeTime * 1000,
            "effective_bw":      effectiveBW,
        }
        
    case Torus3D:
        dim := int(math.Cbrt(float64(cfg.NumNodes)))
        phaseTime := dataBits * float64(dim-1) / 
            (cfg.LinkBandwidth * 1e9 * float64(dim))
        return map[string]float64{
            "total_allreduce_ms": 3 * phaseTime * 1000,
        }
    }
    return nil
}

func main() {
    fmt.Println("=" * 70)
    fmt.Println("scaleFabric 100K-Card Network Performance Comparison")
    fmt.Println("=" * 70)
    
    configs := []NetworkConfig{
        {FatTree, 100000, 400, 64},
        {Dragonfly, 100000, 400, 64},
        {Torus3D, 100000, 400, 64},
    }
    
    for _, cfg := range configs {
        names := map[TopologyType]string{
            FatTree: "FatTree", Dragonfly: "Dragonfly+", Torus3D: "3D-Torus",
        }
        result := simulateAllReduce(cfg, 100)
        
        fmt.Printf("\n📡 Topology: %s\n", names[cfg.Topology])
        fmt.Printf("   100K-card AllReduce (100MB): %.2f ms\n", 
            result["total_allreduce_ms"])
        if cfg.Topology == Dragonfly {
            fmt.Printf("   Optical interconnect: %.0f Gbps\n", result["optical_bw_gbps"])
        }
    }
    
    fmt.Println("\nscaleFabric Key Specs:")
    fmt.Println("""
    ┌─────────────────────────────────────────────────────┐
    │ scaleFabric IB-Native RDMA                          │
    ├─────────────────────────────────────────────────────┤
    │ Per-link bandwidth: 400-800Gbps                     │
    │ Optical backbone: 100× bandwidth aggregation        │
    │ End-to-end latency: < 2μs (single hop)             │
    │ Topology: Dragonfly+ hybrid                         │
    │ Domestic rate: 100% (switches + optics + cables)   │
    │ 100K-card: non-blocking full interconnect          │
    └─────────────────────────────────────────────────────┘
    """)
}