// RTX Spark统一内存带宽模型
package main

import (
	"fmt"
	"math"
)

type MemoryArchitecture struct {
	Name            string
	TotalMemoryGB   float64
	CPUGPUBandwidth float64 // GB/s
	PCIBandwidth    float64 // GB/s (PCIe 5.0 x16)
	ZeroCopy        bool
}

func main() {
	archs := []MemoryArchitecture{
		{"RTX Spark (NVLink-C2C)", 128, 1200, 1200, true},
		{"RTX 5090 + DDR5", 32 + 64, 1200, 64, false},
		{"Apple M3 Ultra", 192, 800, 800, true},
		{"传统PC (PCIe 5.0)", 32 + 128, 1200, 64, false},
	}

	fmt.Println("=== 统一内存架构带宽对比 ===")
	fmt.Printf("%-25s | %-12s | %-18s | %-12s | %-10s\n",
		"架构", "总内存(GB)", "GPU-CPU互联(GB/s)", "PCIe带宽", "零拷贝")
	fmt.Println("------------------------------------------------------------")

	for _, a := range archs {
		var zeroCopyStr string
		if a.ZeroCopy {
			zeroCopyStr = "✅ 是"
		} else {
			zeroCopyStr = "❌ 否"
		}
		fmt.Printf("%-25s | %-12.0f | %-18.0f | %-12.0f | %-10s\n",
			a.Name, a.TotalMemoryGB, a.CPUGPUBandwidth, a.PCIBandwidth, zeroCopyStr)
	}

	// 模拟大模型推理时的数据传输开销
	fmt.Println("\n=== 大模型推理（120B参数，FP16）数据传输开销模拟 ===")
	modelSizeGB := 120 * 2.0 / 1.0 // 120B参数 × 2 bytes (FP16) = 240GB → 需要量化

	// 实际可加载模型大小（考虑KV Cache等）
	usableModels := []struct {
		Name    string
		ParamsB float64
		Quant   string
		SizeGB  float64
	}{
		{"Llama 3.1 70B", 70, "FP16", 140},
		{"Llama 3.1 70B", 70, "INT4", 35},
		{"Qwen 2.5 72B", 72, "INT4", 36},
		{"DeepSeek V4 Flash", 37, "FP16", 74},
		{"DeepSeek V4 Flash", 37, "INT4", 18.5},
	}

	for _, m := range usableModels {
		canLoad := m.SizeGB <= 128
		status := "❌ 超出内存"
		if canLoad {
			status = "✅ 可加载"
		}
		fmt.Printf("  %-25s | %3.0fB %-6s | %6.1fGB | %s\n",
			m.Name, m.ParamsB, m.Quant, m.SizeGB, status)
	}

	// 数据搬运时间对比
	fmt.Println("\n=== 加载70B模型(140GB FP16)到GPU的时间对比 ===")
	modelLoadGB := 140.0
	pcieTime := modelLoadGB / 64.0 // PCIe 5.0 x16: 64 GB/s
	nvlinkTime := modelLoadGB / 1200.0 // NVLink-C2C: 1200 GB/s

	fmt.Printf("  PCIe 5.0 x16: %.2f 秒（需分页加载）\n", pcieTime)
	fmt.Printf("  NVLink-C2C:   %.4f 秒（零拷贝，按需访问）\n", nvlinkTime)
	fmt.Printf("  加速比: %.0fx\n", pcieTime/nvlinkTime)

	// 能效比
	fmt.Println("\n=== 能效比分析 ===")
	archEfficiency := []struct {
		Name       string
		PerfTOPS   float64
		PowerW     float64
		TOPSPerWatt float64
	}{
		{"RTX Spark (N1X)", 1000, 140, 1000 / 140},
		{"RTX 5090", 1800, 575, 1800 / 575},
		{"Apple M3 Ultra", 800, 120, 800 / 120},
		{"Intel i9-15900K + RTX 5090", 1800, 253 + 575, 1800 / 828},
	}

	fmt.Printf("%-35s | %-10s | %-8s | %-12s\n", "方案", "AI算力(TOPS)", "功耗(W)", "每瓦TOPS")
	fmt.Println("------------------------------------------------")
	for _, a := range archEfficiency {
		fmt.Printf("%-35s | %-10.0f | %-8.0f | %-12.2f\n",
			a.Name, a.PerfTOPS, a.PowerW, a.TOPSPerWatt)
	}
}

"""
RTX Spark本地推理性能模拟器
模拟不同模型在RTX Spark上的推理性能
"""
import numpy as np
from dataclasses import dataclass
from typing import List, Tuple

@dataclass
class ModelConfig:
    name: str
    params_b: float  # 参数量（十亿）
    precision: str
    memory_gb: float  # 模型占用内存
    context_len: int  # 支持的上下文长度

@dataclass
class HardwareConfig:
    name: str
    memory_gb: float  # 可用内存
    bandwidth_gbs: float  # 内存带宽 GB/s
    compute_tops: float  # AI算力 TOPS
    tdp_w: float  # 功耗

@dataclass
class InferenceResult:
    model: str
    hardware: str
    can_load: bool
    tokens_per_sec: float
    context_support: bool
    memory_util: float

def simulate_inference(
    model: ModelConfig,
    hw: HardwareConfig,
    batch_size: int = 1,
    kv_cache_overhead: float = 0.3  # KV Cache额外开销
) -> InferenceResult:
    """模拟模型在硬件上的推理性能"""
    
    # 检查是否能加载
    total_memory_needed = model.memory_gb * (1 + kv_cache_overhead)
    can_load = total_memory_needed <= hw.memory_gb
    
    if not can_load:
        return InferenceResult(model.name, hw.name, False, 0, False, 0)
    
    # 检查上下文支持
    context_support = model.context_len >= 1000000  # 100万token
    
    # 估算推理速度
    # 主流LLM推理速度 ≈ 算力利用率 / (模型参数 × 每token计算量)
    # 假设 FP4 精度下，每参数每token约 2 FLOPs
    flops_per_token = model.params_b * 1e9 * 2  # FP4
    compute_utilization = 0.3  # 30% 利用率（实际推理）
    effective_tops = hw.compute_tops * compute_utilization * 1e12  # TOPS → FLOPs/s
    
    # 内存带宽限制
    # 每个token需要读取一次模型参数
    bandwidth_per_token = model.memory_gb * 1e9  # bytes
    bandwidth_limit = hw.bandwidth_gbs * 1e9 / bandwidth_per_token  # tokens/s
    
    # 计算限制
    compute_limit = effective_tops / flops_per_token  # tokens/s
    
    # 实际速度受限于二者中较慢者
    tokens_per_sec = min(bandwidth_limit, compute_limit)
    
    memory_util = (total_memory_needed / hw.memory_gb) * 100
    
    return InferenceResult(model.name, hw.name, can_load, tokens_per_sec, context_support, memory_util)

# 定义模型
models = [
    ModelConfig("Llama 3.1 70B (FP16)", 70, "FP16", 140, 128000),
    ModelConfig("Llama 3.1 70B (INT4)", 70, "INT4", 35, 128000),
    ModelConfig("Qwen 2.5 72B (INT4)", 72, "INT4", 36, 131072),
    ModelConfig("DeepSeek V4 Flash (FP16)", 37, "FP16", 74, 1000000),
    ModelConfig("DeepSeek V4 Flash (INT4)", 37, "INT4", 18.5, 1000000),
    ModelConfig("Gemma 3 27B (INT4)", 27, "INT4", 13.5, 256000),
    ModelConfig("Stable Diffusion 3", 8, "FP16", 16, 0),
    ModelConfig("120B MoE (INT4)", 120, "INT4", 60, 1000000),
]

# 定义硬件
hardware = [
    HardwareConfig("RTX Spark (N1X)", 128, 536, 1000, 140),
    HardwareConfig("RTX 5090 Desktop", 32, 1200, 1800, 575),
    HardwareConfig("Apple M3 Ultra", 192, 800, 800, 120),
]

print("=" * 120)
print(f"{'模型':30s} {'硬件':20s} {'可加载':8s} {'Token/s':10s} {'100万ctx':10s} {'内存利用':10s}")
print("=" * 120)

results = []
for model in models:
    for hw in hardware:
        result = simulate_inference(model, hw)
        results.append(result)
        if result.can_load:
            print(f"{model.name:30s} {hw.name:20s} {'✅':8s} {result.tokens_per_sec:>8.1f} {'✅' if result.context_support else '❌':10s} {result.memory_util:>6.1f}%")
        else:
            print(f"{model.name:30s} {hw.name:20s} {'❌':8s} {'N/A':>8s} {'N/A':10s} {'N/A':>6s}")

# 汇总RTX Spark可运行模型
print("\n\n=== RTX Spark 可本地运行模型汇总 ===")
spark_results = [r for r in results if r.hardware == "RTX Spark (N1X)" and r.can_load]
print(f"{'模型':30s} {'Token/s':12s} {'内存利用率':12s}")
print("-" * 54)
for r in sorted(spark_results, key=lambda x: x.tokens_per_sec, reverse=True):
    print(f"{r.model:30s} {r.tokens_per_sec:>8.1f} tok/s {r.memory_util:>6.1f}%")

"""
RTX Spark 3D渲染性能对比
"""
import numpy as np

class RenderBenchmark:
    def __init__(self):
        self.scenarios = [
            {
                "name": "曼哈顿3D城市场景",
                "scene_size_gb": 90,
                "polygons_billions": 20,
                "texture_resolution": "8K"
            },
            {
                "name": "12K 4:2:2 视频编辑",
                "resolution": "12288×6480",
                "color_depth": "10-bit",
                "codec": "HEVC/H.265"
            },
            {
                "name": "Blender 3D渲染",
                "samples": 4096,
                "denoiser": "NVIDIA OptiX AI"
            }
        ]
    
    def memory_analysis(self):
        """内存使用分析"""
        print("=== 3D场景内存占用分析 ===")
        print(f"场景文件大小: 90GB")
        print(f"传统16GB笔记本: ❌ 无法加载（内存溢出）")
        print(f"传统32GB工作站: ⚠️ 需要SSD虚拟内存，频繁交换")
        print(f"传统64GB台式机: ✅ 可加载但显存不足（GPU仅24GB VRAM）")
        print(f"RTX Spark 128GB: ✅ 统一内存，零拷贝，GPU直接访问全部128GB")
        print(f"   - 场景加载: 90GB")
        print(f"   - 纹理缓存: 15GB")
        print(f"   - 渲染缓冲区: 10GB")
        print(f"   - 剩余可用: 13GB")
    
    def video_editing_analysis(self):
        """视频编辑能力"""
        print("\n=== 12K视频编辑能力 ===")
        print("格式: 12K (12288×6480) 4:2:2 10-bit HEVC")
        print("传统方案: 需要专业级桌面工作站 + 专用视频卡")
        print("RTX Spark: 第五代NVENC，硬件解码，轻薄本即可流畅编辑")
        
        # 计算像素吞吐量
        pixels_4k = 3840 * 2160
        pixels_8k = 7680 * 4320
        pixels_12k = 12288 * 6480
        
        print(f"\n像素数对比:")
        print(f"  4K: {pixels_4k/1e6:.1f}M pixels")
        print(f"  8K: {pixels_8k/1e6:.1f}M pixels")
        print(f"  12K: {pixels_12k/1e6:.1f}M pixels")
        print(f"  12K是4K的 {pixels_12k/pixels_4k:.1f} 倍像素量")

bench = RenderBenchmark()
bench.memory_analysis()
bench.video_editing_analysis()

package main

import (
	"crypto/aes"
	"crypto/cipher"
	"crypto/rand"
	"encoding/json"
	"fmt"
	"io"
	"os"
	"strings"
	"sync"
	"time"
)

// 权限级别
type PermissionLevel int

const (
	PermissionDeny  PermissionLevel = 0
	PermissionLocal PermissionLevel = 1
	PermissionCloud PermissionLevel = 2
)

// 安全策略
type SecurityPolicy struct {
	FileRead     PermissionLevel `json:"file_read"`
	FileWrite    PermissionLevel `json:"file_write"`
	NetworkAccess PermissionLevel `json:"network_access"`
	CameraAccess PermissionLevel `json:"camera_access"`
	Microphone   PermissionLevel `json:"microphone"`
	AppExecution PermissionLevel `json:"app_execution"`
}

// 隐私分流决策
type PrivacyRouter struct {
	mu          sync.RWMutex
	policy      SecurityPolicy
	sensitiveKB map[string]bool // 敏感关键词库
	encryptKey  []byte
}

func NewPrivacyRouter(policy SecurityPolicy) *PrivacyRouter {
	key := make([]byte, 32)
	rand.Read(key)
	
	return &PrivacyRouter{
		policy:      policy,
		sensitiveKB: buildSensitiveKB(),
		encryptKey:  key,
	}
}

func buildSensitiveKB() map[string]bool {
	return map[string]bool{
		"password": true, "ssn": true, "credit_card": true,
		"medical": true, "salary": true, "address": true,
		"phone": true, "bank_account": true, "passport": true,
		"private_key": true, "token": true, "api_key": true,
	}
}

// 判断请求是否包含敏感信息
func (pr *PrivacyRouter) containsSensitiveData(query string) bool {
	pr.mu.RLock()
	defer pr.mu.RUnlock()
	
	lowerQuery := strings.ToLower(query)
	for keyword := range pr.sensitiveKB {
		if strings.Contains(lowerQuery, keyword) {
			return true
		}
	}
	return false
}

// 脱敏处理
func (pr *PrivacyRouter) sanitize(query string) string {
	// 替换敏感模式
	replacements := []struct {
		pattern string
		replacement string
	}{
		{`\b\d{3}-\d{2}-\d{4}\b`, "[SSN_REDACTED]"},
		{`\b\d{16}\b`, "[CC_REDACTED]"},
		{`\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b`, "[EMAIL_REDACTED]"},
		{`\b\d{5,}\b`, "[NUM_REDACTED]"},
	}
	
	sanitized := query
	for _, r := range replacements {
		// 简单实现，实际使用正则
		if strings.Contains(sanitized, r.pattern) {
			sanitized = strings.ReplaceAll(sanitized, r.pattern, r.replacement)
		}
	}
	return sanitized
}

// 推理请求
type InferenceRequest struct {
	Query         string `json:"query"`
	RequireCloud  bool   `json:"require_cloud"`
	PrivacyLevel  string `json:"privacy_level"`
}

// 推理结果
type InferenceResult struct {
	Response     string `json:"response"`
	Source       string `json:"source"` // "local" or "cloud"
	LatencyMs    int64  `json:"latency_ms"`
	PrivacySafe  bool   `json:"privacy_safe"`
}

// 本地模型推理器（模拟）
type LocalInferencer struct {
	modelName string
	paramsB   float64
}

func NewLocalInferencer() *LocalInferencer {
	return &LocalInferencer{
		modelName: "Qwen-35B-INT4",
		paramsB:   35,
	}
}

func (li *LocalInferencer) Infer(query string) InferenceResult {
	start := time.Now()
	time.Sleep(50 * time.Millisecond) // 模拟推理延迟
	return InferenceResult{
		Response:    fmt.Sprintf("[本地推理] %s => 基于本地模型%s的响应", query, li.modelName),
		Source:      "local",
		LatencyMs:   time.Since(start).Milliseconds(),
		PrivacySafe: true,
	}
}

// 智能体运行时
type AgentRuntime struct {
	router     *PrivacyRouter
	localModel *LocalInferencer
	cloudAPI   string
}

func NewAgentRuntime(policy SecurityPolicy) *AgentRuntime {
	return &AgentRuntime{
		router:     NewPrivacyRouter(policy),
		localModel: NewLocalInferencer(),
		cloudAPI:   "https://api.openai.com/v1/chat/completions",
	}
}

// 执行推理请求
func (ar *AgentRuntime) Execute(req InferenceRequest) InferenceResult {
	// 1. 检查请求是否包含敏感信息
	hasSensitive := ar.router.containsSensitiveData(req.Query)
	
	// 2. 决策路由
	useLocal := false
	reason := ""
	
	switch {
	case req.PrivacyLevel == "max":
		useLocal = true
		reason = "用户指定最高隐私级别，强制本地推理"
	case hasSensitive:
		useLocal = true
		reason = "检测到敏感信息，重定向到本地模型"
	case ar.router.policy.NetworkAccess == PermissionDeny:
		useLocal = true
		reason = "网络访问被策略禁止，使用本地模型"
	default:
		// 需要云端且不敏感时，可走云端
		if req.RequireCloud {
			useLocal = false
			reason = "请求需要云端能力，发送云端"
		} else {
			// 默认走本地（隐私优先）
			useLocal = true
			reason = "默认本地推理（隐私优先策略）"
		}
	}
	
	fmt.Printf("[OpenShell] 路由决策: %s\n", reason)
	
	if useLocal {
		return ar.localModel.Infer(req.Query)
	}
	
	// 云端推理（脱敏后发送）
	sanitizedQuery := ar.router.sanitize(req.Query)
	start := time.Now()
	time.Sleep(200 * time.Millisecond) // 模拟云端API延迟
	return InferenceResult{
		Response:    fmt.Sprintf("[云端推理] %s => 云端模型响应", sanitizedQuery),
		Source:      "cloud",
		LatencyMs:   time.Since(start).Milliseconds(),
		PrivacySafe: !hasSensitive,
	}
}

// 加密存储（TEE模拟）
func (ar *AgentRuntime) EncryptAndStore(data []byte) error {
	block, err := aes.NewCipher(ar.router.encryptKey)
	if err != nil {
		return err
	}
	
	aesGCM, err := cipher.NewGCM(block)
	if err != nil {
		return err
	}
	
	nonce := make([]byte, aesGCM.NonceSize())
	if _, err := io.ReadFull(rand.Reader, nonce); err != nil {
		return err
	}
	
	ciphertext := aesGCM.Seal(nonce, nonce, data, nil)
	return os.WriteFile("agent_memory.enc", ciphertext, 0600)
}

func main() {
	// 配置安全策略
	policy := SecurityPolicy{
		FileRead:      PermissionLocal,
		FileWrite:     PermissionDeny,
		NetworkAccess: PermissionLocal,
		CameraAccess:  PermissionLocal,
		Microphone:     PermissionLocal,
		AppExecution:  PermissionLocal,
	}
	
	runtime := NewAgentRuntime(policy)
	
	fmt.Println("=== RTX Spark OpenShell 智能体运行时 ===")
	fmt.Printf("安全策略: %+v\n", policy)
	fmt.Println()
	
	// 测试用例
	testQueries := []InferenceRequest{
		{Query: "帮我总结今天的日程安排", PrivacyLevel: "normal"},
		{Query: "我的社保号码是123-45-6789，请帮我注册", PrivacyLevel: "normal"},
		{Query: "分析这份财报数据，生成投资建议", PrivacyLevel: "max"},
		{Query: "写一个Python脚本处理CSV文件", PrivacyLevel: "normal", RequireCloud: true},
	}
	
	for i, req := range testQueries {
		fmt.Printf("\n--- 请求 %d ---\n", i+1)
		result := runtime.Execute(req)
		respJSON, _ := json.MarshalIndent(result, "", "  ")
		fmt.Printf("结果: %s\n", string(respJSON))
	}
	
	// 加密存储演示
	memory := []byte("用户偏好: 喜欢简洁的代码风格，偏好Python和Go")
	runtime.EncryptAndStore(memory)
	fmt.Println("\n[安全存储] 用户记忆已加密保存到 agent_memory.enc")
}