chore(security): enhance environment configuration, CI workflows, and wallet daemon with security improvements
- Restructure .env.example with security-focused documentation, service-specific environment file references, and AWS Secrets Manager integration - Update CLI tests workflow to single Python 3.13 version, add pytest-mock dependency, and consolidate test execution with coverage - Add comprehensive security validation to package publishing workflow with manual approval gates, secret scanning, and release
This commit is contained in:
oib
354
gpu_acceleration/legacy/fastapi_cuda_zk_api.py
Normal file
354
gpu_acceleration/legacy/fastapi_cuda_zk_api.py
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@@ -0,0 +1,354 @@
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#!/usr/bin/env python3
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"""
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FastAPI Integration for Production CUDA ZK Accelerator
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Provides REST API endpoints for GPU-accelerated ZK circuit operations
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"""
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from fastapi import FastAPI, HTTPException, BackgroundTasks
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel, Field
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from typing import Dict, List, Optional, Any
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import asyncio
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import logging
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import time
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import os
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import sys
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# Add GPU acceleration path
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sys.path.append('/home/oib/windsurf/aitbc/gpu_acceleration')
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try:
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from production_cuda_zk_api import ProductionCUDAZKAPI, ZKOperationRequest, ZKOperationResult
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CUDA_AVAILABLE = True
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except ImportError as e:
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CUDA_AVAILABLE = False
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print(f"⚠️ CUDA API import failed: {e}")
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# Configure logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger("CUDA_ZK_FASTAPI")
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# Initialize FastAPI app
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app = FastAPI(
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title="AITBC CUDA ZK Acceleration API",
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description="Production-ready GPU acceleration for zero-knowledge circuit operations",
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version="1.0.0",
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docs_url="/docs",
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redoc_url="/redoc"
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)
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# Add CORS middleware
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Initialize CUDA API
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cuda_api = ProductionCUDAZKAPI()
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# Pydantic models for API
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class FieldAdditionRequest(BaseModel):
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num_elements: int = Field(..., ge=1, le=10000000, description="Number of field elements")
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modulus: Optional[List[int]] = Field(default=[0xFFFFFFFFFFFFFFFF] * 4, description="Field modulus")
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optimization_level: str = Field(default="high", pattern="^(low|medium|high)$")
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use_gpu: bool = Field(default=True, description="Use GPU acceleration")
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class ConstraintVerificationRequest(BaseModel):
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num_constraints: int = Field(..., ge=1, le=10000000, description="Number of constraints")
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constraints: Optional[List[Dict[str, Any]]] = Field(default=None, description="Constraint data")
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optimization_level: str = Field(default="high", pattern="^(low|medium|high)$")
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use_gpu: bool = Field(default=True, description="Use GPU acceleration")
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class WitnessGenerationRequest(BaseModel):
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num_inputs: int = Field(..., ge=1, le=1000000, description="Number of inputs")
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witness_size: int = Field(..., ge=1, le=10000000, description="Witness size")
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optimization_level: str = Field(default="high", pattern="^(low|medium|high)$")
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use_gpu: bool = Field(default=True, description="Use GPU acceleration")
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class BenchmarkRequest(BaseModel):
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max_elements: int = Field(default=1000000, ge=1000, le=10000000, description="Maximum elements to benchmark")
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class APIResponse(BaseModel):
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success: bool
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message: str
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data: Optional[Dict[str, Any]] = None
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execution_time: Optional[float] = None
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gpu_used: Optional[bool] = None
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speedup: Optional[float] = None
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# Health check endpoint
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@app.get("/health", response_model=Dict[str, Any])
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async def health_check():
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"""Health check endpoint"""
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try:
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stats = cuda_api.get_performance_statistics()
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return {
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"status": "healthy",
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"timestamp": time.time(),
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"cuda_available": stats["cuda_available"],
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"cuda_initialized": stats["cuda_initialized"],
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"gpu_device": stats["gpu_device"]
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}
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except Exception as e:
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logger.error(f"Health check failed: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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# Performance statistics endpoint
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@app.get("/stats", response_model=Dict[str, Any])
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async def get_performance_stats():
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"""Get comprehensive performance statistics"""
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try:
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return cuda_api.get_performance_statistics()
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except Exception as e:
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logger.error(f"Failed to get stats: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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# Field addition endpoint
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@app.post("/field-addition", response_model=APIResponse)
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async def field_addition(request: FieldAdditionRequest):
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"""Perform GPU-accelerated field addition"""
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start_time = time.time()
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try:
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zk_request = ZKOperationRequest(
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operation_type="field_addition",
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circuit_data={
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"num_elements": request.num_elements,
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"modulus": request.modulus
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},
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optimization_level=request.optimization_level,
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use_gpu=request.use_gpu
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)
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result = await cuda_api.process_zk_operation(zk_request)
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return APIResponse(
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success=result.success,
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message="Field addition completed successfully" if result.success else "Field addition failed",
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data=result.result_data,
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execution_time=result.execution_time,
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gpu_used=result.gpu_used,
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speedup=result.speedup
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)
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except Exception as e:
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logger.error(f"Field addition failed: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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# Constraint verification endpoint
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@app.post("/constraint-verification", response_model=APIResponse)
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async def constraint_verification(request: ConstraintVerificationRequest):
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"""Perform GPU-accelerated constraint verification"""
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start_time = time.time()
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try:
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zk_request = ZKOperationRequest(
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operation_type="constraint_verification",
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circuit_data={"num_constraints": request.num_constraints},
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constraints=request.constraints,
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optimization_level=request.optimization_level,
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use_gpu=request.use_gpu
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)
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result = await cuda_api.process_zk_operation(zk_request)
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return APIResponse(
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success=result.success,
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message="Constraint verification completed successfully" if result.success else "Constraint verification failed",
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data=result.result_data,
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execution_time=result.execution_time,
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gpu_used=result.gpu_used,
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speedup=result.speedup
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)
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except Exception as e:
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logger.error(f"Constraint verification failed: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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# Witness generation endpoint
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@app.post("/witness-generation", response_model=APIResponse)
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async def witness_generation(request: WitnessGenerationRequest):
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"""Perform GPU-accelerated witness generation"""
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start_time = time.time()
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try:
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zk_request = ZKOperationRequest(
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operation_type="witness_generation",
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circuit_data={"num_inputs": request.num_inputs},
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witness_data={"num_inputs": request.num_inputs, "witness_size": request.witness_size},
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optimization_level=request.optimization_level,
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use_gpu=request.use_gpu
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)
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result = await cuda_api.process_zk_operation(zk_request)
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return APIResponse(
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success=result.success,
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message="Witness generation completed successfully" if result.success else "Witness generation failed",
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data=result.result_data,
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execution_time=result.execution_time,
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gpu_used=result.gpu_used,
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speedup=result.speedup
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)
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except Exception as e:
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logger.error(f"Witness generation failed: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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# Comprehensive benchmark endpoint
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@app.post("/benchmark", response_model=Dict[str, Any])
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async def comprehensive_benchmark(request: BenchmarkRequest, background_tasks: BackgroundTasks):
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"""Run comprehensive performance benchmark"""
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try:
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logger.info(f"Starting comprehensive benchmark up to {request.max_elements:,} elements")
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# Run benchmark asynchronously
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results = await cuda_api.benchmark_comprehensive_performance(request.max_elements)
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return {
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"success": True,
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"message": "Comprehensive benchmark completed",
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"data": results,
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"timestamp": time.time()
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}
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except Exception as e:
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logger.error(f"Benchmark failed: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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# Quick benchmark endpoint
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@app.get("/quick-benchmark", response_model=Dict[str, Any])
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async def quick_benchmark():
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"""Run quick performance benchmark"""
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try:
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logger.info("Running quick benchmark")
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# Test field addition with 100K elements
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field_request = ZKOperationRequest(
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operation_type="field_addition",
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circuit_data={"num_elements": 100000},
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use_gpu=True
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)
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field_result = await cuda_api.process_zk_operation(field_request)
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# Test constraint verification with 50K constraints
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constraint_request = ZKOperationRequest(
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operation_type="constraint_verification",
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circuit_data={"num_constraints": 50000},
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use_gpu=True
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)
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constraint_result = await cuda_api.process_zk_operation(constraint_request)
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return {
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"success": True,
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"message": "Quick benchmark completed",
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"data": {
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"field_addition": {
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"success": field_result.success,
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"execution_time": field_result.execution_time,
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"gpu_used": field_result.gpu_used,
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"speedup": field_result.speedup,
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"throughput": field_result.throughput
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},
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"constraint_verification": {
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"success": constraint_result.success,
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"execution_time": constraint_result.execution_time,
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"gpu_used": constraint_result.gpu_used,
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"speedup": constraint_result.speedup,
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"throughput": constraint_result.throughput
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}
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},
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"timestamp": time.time()
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}
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except Exception as e:
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logger.error(f"Quick benchmark failed: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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# GPU information endpoint
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@app.get("/gpu-info", response_model=Dict[str, Any])
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async def get_gpu_info():
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"""Get GPU information and capabilities"""
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try:
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stats = cuda_api.get_performance_statistics()
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return {
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"cuda_available": stats["cuda_available"],
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"cuda_initialized": stats["cuda_initialized"],
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"gpu_device": stats["gpu_device"],
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"total_operations": stats["total_operations"],
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"gpu_operations": stats["gpu_operations"],
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"cpu_operations": stats["cpu_operations"],
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"gpu_usage_rate": stats.get("gpu_usage_rate", 0),
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"average_speedup": stats.get("average_speedup", 0),
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"average_execution_time": stats.get("average_execution_time", 0)
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}
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except Exception as e:
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logger.error(f"Failed to get GPU info: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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# Reset statistics endpoint
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@app.post("/reset-stats", response_model=Dict[str, str])
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async def reset_statistics():
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"""Reset performance statistics"""
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try:
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# Reset the statistics in the CUDA API
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cuda_api.operation_stats = {
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"total_operations": 0,
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"gpu_operations": 0,
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"cpu_operations": 0,
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"total_time": 0.0,
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"average_speedup": 0.0
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}
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return {"success": True, "message": "Statistics reset successfully"}
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except Exception as e:
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logger.error(f"Failed to reset stats: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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# Root endpoint
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@app.get("/", response_model=Dict[str, Any])
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async def root():
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"""Root endpoint with API information"""
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return {
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"name": "AITBC CUDA ZK Acceleration API",
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"version": "1.0.0",
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"description": "Production-ready GPU acceleration for zero-knowledge circuit operations",
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"endpoints": {
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||||
"health": "/health",
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"stats": "/stats",
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"gpu_info": "/gpu-info",
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"field_addition": "/field-addition",
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"constraint_verification": "/constraint-verification",
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"witness_generation": "/witness-generation",
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"quick_benchmark": "/quick-benchmark",
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||||
"comprehensive_benchmark": "/benchmark",
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"docs": "/docs",
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"redoc": "/redoc"
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||||
},
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"cuda_available": CUDA_AVAILABLE,
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"timestamp": time.time()
|
||||
}
|
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|
||||
if __name__ == "__main__":
|
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import uvicorn
|
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|
||||
print("🚀 Starting AITBC CUDA ZK Acceleration API Server")
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||||
print("=" * 50)
|
||||
print(f" CUDA Available: {CUDA_AVAILABLE}")
|
||||
print(f" API Documentation: http://localhost:8001/docs")
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||||
print(f" ReDoc Documentation: http://localhost:8001/redoc")
|
||||
print("=" * 50)
|
||||
|
||||
uvicorn.run(
|
||||
"fastapi_cuda_zk_api:app",
|
||||
host="0.0.0.0",
|
||||
port=8001,
|
||||
reload=True,
|
||||
log_level="info"
|
||||
)
|
||||
453
gpu_acceleration/legacy/high_performance_cuda_accelerator.py
Normal file
453
gpu_acceleration/legacy/high_performance_cuda_accelerator.py
Normal file
@@ -0,0 +1,453 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
High-Performance CUDA ZK Accelerator with Optimized Kernels
|
||||
Implements optimized CUDA kernels with memory coalescing, vectorization, and shared memory
|
||||
"""
|
||||
|
||||
import ctypes
|
||||
import numpy as np
|
||||
from typing import List, Tuple, Optional
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
|
||||
# Optimized field element structure for flat array access
|
||||
class OptimizedFieldElement(ctypes.Structure):
|
||||
_fields_ = [("limbs", ctypes.c_uint64 * 4)]
|
||||
|
||||
class HighPerformanceCUDAZKAccelerator:
|
||||
"""High-performance Python interface for optimized CUDA ZK operations"""
|
||||
|
||||
def __init__(self, lib_path: str = None):
|
||||
"""
|
||||
Initialize high-performance CUDA accelerator
|
||||
|
||||
Args:
|
||||
lib_path: Path to compiled optimized CUDA library (.so file)
|
||||
"""
|
||||
self.lib_path = lib_path or self._find_optimized_cuda_lib()
|
||||
self.lib = None
|
||||
self.initialized = False
|
||||
|
||||
try:
|
||||
self.lib = ctypes.CDLL(self.lib_path)
|
||||
self._setup_function_signatures()
|
||||
self.initialized = True
|
||||
print(f"✅ High-Performance CUDA ZK Accelerator initialized: {self.lib_path}")
|
||||
except Exception as e:
|
||||
print(f"❌ Failed to initialize CUDA accelerator: {e}")
|
||||
self.initialized = False
|
||||
|
||||
def _find_optimized_cuda_lib(self) -> str:
|
||||
"""Find the compiled optimized CUDA library"""
|
||||
possible_paths = [
|
||||
"./liboptimized_field_operations.so",
|
||||
"./optimized_field_operations.so",
|
||||
"../liboptimized_field_operations.so",
|
||||
"../../liboptimized_field_operations.so",
|
||||
"/usr/local/lib/liboptimized_field_operations.so"
|
||||
]
|
||||
|
||||
for path in possible_paths:
|
||||
if os.path.exists(path):
|
||||
return path
|
||||
|
||||
raise FileNotFoundError("Optimized CUDA library not found. Please compile optimized_field_operations.cu first.")
|
||||
|
||||
def _setup_function_signatures(self):
|
||||
"""Setup function signatures for optimized CUDA library functions"""
|
||||
if not self.lib:
|
||||
return
|
||||
|
||||
# Initialize optimized CUDA device
|
||||
self.lib.init_optimized_cuda_device.argtypes = []
|
||||
self.lib.init_optimized_cuda_device.restype = ctypes.c_int
|
||||
|
||||
# Optimized field addition with flat arrays
|
||||
self.lib.gpu_optimized_field_addition.argtypes = [
|
||||
np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"),
|
||||
np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"),
|
||||
np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"),
|
||||
np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"),
|
||||
ctypes.c_int
|
||||
]
|
||||
self.lib.gpu_optimized_field_addition.restype = ctypes.c_int
|
||||
|
||||
# Vectorized field addition
|
||||
self.lib.gpu_vectorized_field_addition.argtypes = [
|
||||
np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"), # field_vector_t
|
||||
np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"),
|
||||
np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"),
|
||||
np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"),
|
||||
ctypes.c_int
|
||||
]
|
||||
self.lib.gpu_vectorized_field_addition.restype = ctypes.c_int
|
||||
|
||||
# Shared memory field addition
|
||||
self.lib.gpu_shared_memory_field_addition.argtypes = [
|
||||
np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"),
|
||||
np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"),
|
||||
np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"),
|
||||
np.ctypeslib.ndpointer(ctypes.c_uint64, flags="C_CONTIGUOUS"),
|
||||
ctypes.c_int
|
||||
]
|
||||
self.lib.gpu_shared_memory_field_addition.restype = ctypes.c_int
|
||||
|
||||
def init_device(self) -> bool:
|
||||
"""Initialize optimized CUDA device and check capabilities"""
|
||||
if not self.initialized:
|
||||
print("❌ CUDA accelerator not initialized")
|
||||
return False
|
||||
|
||||
try:
|
||||
result = self.lib.init_optimized_cuda_device()
|
||||
if result == 0:
|
||||
print("✅ Optimized CUDA device initialized successfully")
|
||||
return True
|
||||
else:
|
||||
print(f"❌ CUDA device initialization failed: {result}")
|
||||
return False
|
||||
except Exception as e:
|
||||
print(f"❌ CUDA device initialization error: {e}")
|
||||
return False
|
||||
|
||||
def benchmark_optimized_kernels(self, max_elements: int = 10000000) -> dict:
|
||||
"""
|
||||
Benchmark all optimized CUDA kernels and compare performance
|
||||
|
||||
Args:
|
||||
max_elements: Maximum number of elements to test
|
||||
|
||||
Returns:
|
||||
Comprehensive performance benchmark results
|
||||
"""
|
||||
if not self.initialized:
|
||||
return {"error": "CUDA accelerator not initialized"}
|
||||
|
||||
print(f"🚀 High-Performance CUDA Kernel Benchmark (up to {max_elements:,} elements)")
|
||||
print("=" * 80)
|
||||
|
||||
# Test different dataset sizes
|
||||
test_sizes = [
|
||||
1000, # 1K elements
|
||||
10000, # 10K elements
|
||||
100000, # 100K elements
|
||||
1000000, # 1M elements
|
||||
5000000, # 5M elements
|
||||
10000000, # 10M elements
|
||||
]
|
||||
|
||||
results = {
|
||||
"test_sizes": [],
|
||||
"optimized_flat": [],
|
||||
"vectorized": [],
|
||||
"shared_memory": [],
|
||||
"cpu_baseline": [],
|
||||
"performance_summary": {}
|
||||
}
|
||||
|
||||
for size in test_sizes:
|
||||
if size > max_elements:
|
||||
break
|
||||
|
||||
print(f"\n📊 Benchmarking {size:,} elements...")
|
||||
|
||||
# Generate test data as flat arrays for optimal memory access
|
||||
a_flat, b_flat = self._generate_flat_test_data(size)
|
||||
|
||||
# bn128 field modulus (simplified)
|
||||
modulus = [0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF, 0xFFFFFFFFFFFFFFFF]
|
||||
|
||||
# Benchmark optimized flat array kernel
|
||||
flat_result = self._benchmark_optimized_flat_kernel(a_flat, b_flat, modulus, size)
|
||||
|
||||
# Benchmark vectorized kernel
|
||||
vec_result = self._benchmark_vectorized_kernel(a_flat, b_flat, modulus, size)
|
||||
|
||||
# Benchmark shared memory kernel
|
||||
shared_result = self._benchmark_shared_memory_kernel(a_flat, b_flat, modulus, size)
|
||||
|
||||
# Benchmark CPU baseline
|
||||
cpu_result = self._benchmark_cpu_baseline(a_flat, b_flat, modulus, size)
|
||||
|
||||
# Store results
|
||||
results["test_sizes"].append(size)
|
||||
results["optimized_flat"].append(flat_result)
|
||||
results["vectorized"].append(vec_result)
|
||||
results["shared_memory"].append(shared_result)
|
||||
results["cpu_baseline"].append(cpu_result)
|
||||
|
||||
# Print comparison
|
||||
print(f" Optimized Flat: {flat_result['time']:.4f}s, {flat_result['throughput']:.0f} elem/s")
|
||||
print(f" Vectorized: {vec_result['time']:.4f}s, {vec_result['throughput']:.0f} elem/s")
|
||||
print(f" Shared Memory: {shared_result['time']:.4f}s, {shared_result['throughput']:.0f} elem/s")
|
||||
print(f" CPU Baseline: {cpu_result['time']:.4f}s, {cpu_result['throughput']:.0f} elem/s")
|
||||
|
||||
# Calculate speedups
|
||||
flat_speedup = cpu_result['time'] / flat_result['time'] if flat_result['time'] > 0 else 0
|
||||
vec_speedup = cpu_result['time'] / vec_result['time'] if vec_result['time'] > 0 else 0
|
||||
shared_speedup = cpu_result['time'] / shared_result['time'] if shared_result['time'] > 0 else 0
|
||||
|
||||
print(f" Speedups - Flat: {flat_speedup:.2f}x, Vec: {vec_speedup:.2f}x, Shared: {shared_speedup:.2f}x")
|
||||
|
||||
# Calculate performance summary
|
||||
results["performance_summary"] = self._calculate_performance_summary(results)
|
||||
|
||||
# Print final summary
|
||||
self._print_performance_summary(results["performance_summary"])
|
||||
|
||||
return results
|
||||
|
||||
def _benchmark_optimized_flat_kernel(self, a_flat: np.ndarray, b_flat: np.ndarray,
|
||||
modulus: List[int], num_elements: int) -> dict:
|
||||
"""Benchmark optimized flat array kernel"""
|
||||
try:
|
||||
result_flat = np.zeros_like(a_flat)
|
||||
modulus_array = np.array(modulus, dtype=np.uint64)
|
||||
|
||||
# Multiple runs for consistency
|
||||
times = []
|
||||
for run in range(3):
|
||||
start_time = time.time()
|
||||
success = self.lib.gpu_optimized_field_addition(
|
||||
a_flat, b_flat, result_flat, modulus_array, num_elements
|
||||
)
|
||||
run_time = time.time() - start_time
|
||||
|
||||
if success == 0: # Success
|
||||
times.append(run_time)
|
||||
|
||||
if not times:
|
||||
return {"time": float('inf'), "throughput": 0, "success": False}
|
||||
|
||||
avg_time = sum(times) / len(times)
|
||||
throughput = num_elements / avg_time if avg_time > 0 else 0
|
||||
|
||||
return {"time": avg_time, "throughput": throughput, "success": True}
|
||||
|
||||
except Exception as e:
|
||||
print(f" ❌ Optimized flat kernel error: {e}")
|
||||
return {"time": float('inf'), "throughput": 0, "success": False}
|
||||
|
||||
def _benchmark_vectorized_kernel(self, a_flat: np.ndarray, b_flat: np.ndarray,
|
||||
modulus: List[int], num_elements: int) -> dict:
|
||||
"""Benchmark vectorized kernel"""
|
||||
try:
|
||||
# Convert flat arrays to vectorized format (uint4)
|
||||
# For simplicity, we'll reuse the flat array kernel as vectorized
|
||||
# In practice, would convert to proper vector format
|
||||
result_flat = np.zeros_like(a_flat)
|
||||
modulus_array = np.array(modulus, dtype=np.uint64)
|
||||
|
||||
times = []
|
||||
for run in range(3):
|
||||
start_time = time.time()
|
||||
success = self.lib.gpu_vectorized_field_addition(
|
||||
a_flat, b_flat, result_flat, modulus_array, num_elements
|
||||
)
|
||||
run_time = time.time() - start_time
|
||||
|
||||
if success == 0:
|
||||
times.append(run_time)
|
||||
|
||||
if not times:
|
||||
return {"time": float('inf'), "throughput": 0, "success": False}
|
||||
|
||||
avg_time = sum(times) / len(times)
|
||||
throughput = num_elements / avg_time if avg_time > 0 else 0
|
||||
|
||||
return {"time": avg_time, "throughput": throughput, "success": True}
|
||||
|
||||
except Exception as e:
|
||||
print(f" ❌ Vectorized kernel error: {e}")
|
||||
return {"time": float('inf'), "throughput": 0, "success": False}
|
||||
|
||||
def _benchmark_shared_memory_kernel(self, a_flat: np.ndarray, b_flat: np.ndarray,
|
||||
modulus: List[int], num_elements: int) -> dict:
|
||||
"""Benchmark shared memory kernel"""
|
||||
try:
|
||||
result_flat = np.zeros_like(a_flat)
|
||||
modulus_array = np.array(modulus, dtype=np.uint64)
|
||||
|
||||
times = []
|
||||
for run in range(3):
|
||||
start_time = time.time()
|
||||
success = self.lib.gpu_shared_memory_field_addition(
|
||||
a_flat, b_flat, result_flat, modulus_array, num_elements
|
||||
)
|
||||
run_time = time.time() - start_time
|
||||
|
||||
if success == 0:
|
||||
times.append(run_time)
|
||||
|
||||
if not times:
|
||||
return {"time": float('inf'), "throughput": 0, "success": False}
|
||||
|
||||
avg_time = sum(times) / len(times)
|
||||
throughput = num_elements / avg_time if avg_time > 0 else 0
|
||||
|
||||
return {"time": avg_time, "throughput": throughput, "success": True}
|
||||
|
||||
except Exception as e:
|
||||
print(f" ❌ Shared memory kernel error: {e}")
|
||||
return {"time": float('inf'), "throughput": 0, "success": False}
|
||||
|
||||
def _benchmark_cpu_baseline(self, a_flat: np.ndarray, b_flat: np.ndarray,
|
||||
modulus: List[int], num_elements: int) -> dict:
|
||||
"""Benchmark CPU baseline for comparison"""
|
||||
try:
|
||||
start_time = time.time()
|
||||
|
||||
# Simple CPU field addition
|
||||
result_flat = np.zeros_like(a_flat)
|
||||
for i in range(num_elements):
|
||||
base_idx = i * 4
|
||||
for j in range(4):
|
||||
result_flat[base_idx + j] = (a_flat[base_idx + j] + b_flat[base_idx + j]) % modulus[j]
|
||||
|
||||
cpu_time = time.time() - start_time
|
||||
throughput = num_elements / cpu_time if cpu_time > 0 else 0
|
||||
|
||||
return {"time": cpu_time, "throughput": throughput, "success": True}
|
||||
|
||||
except Exception as e:
|
||||
print(f" ❌ CPU baseline error: {e}")
|
||||
return {"time": float('inf'), "throughput": 0, "success": False}
|
||||
|
||||
def _generate_flat_test_data(self, num_elements: int) -> Tuple[np.ndarray, np.ndarray]:
|
||||
"""Generate flat array test data for optimal memory access"""
|
||||
# Generate flat arrays (num_elements * 4 limbs)
|
||||
flat_size = num_elements * 4
|
||||
|
||||
# Use numpy for fast generation
|
||||
a_flat = np.random.randint(0, 2**32, size=flat_size, dtype=np.uint64)
|
||||
b_flat = np.random.randint(0, 2**32, size=flat_size, dtype=np.uint64)
|
||||
|
||||
return a_flat, b_flat
|
||||
|
||||
def _calculate_performance_summary(self, results: dict) -> dict:
|
||||
"""Calculate performance summary statistics"""
|
||||
summary = {}
|
||||
|
||||
# Find best performing kernel for each size
|
||||
best_speedups = []
|
||||
best_throughputs = []
|
||||
|
||||
for i, size in enumerate(results["test_sizes"]):
|
||||
cpu_time = results["cpu_baseline"][i]["time"]
|
||||
|
||||
# Calculate speedups
|
||||
flat_speedup = cpu_time / results["optimized_flat"][i]["time"] if results["optimized_flat"][i]["time"] > 0 else 0
|
||||
vec_speedup = cpu_time / results["vectorized"][i]["time"] if results["vectorized"][i]["time"] > 0 else 0
|
||||
shared_speedup = cpu_time / results["shared_memory"][i]["time"] if results["shared_memory"][i]["time"] > 0 else 0
|
||||
|
||||
best_speedup = max(flat_speedup, vec_speedup, shared_speedup)
|
||||
best_speedups.append(best_speedup)
|
||||
|
||||
# Find best throughput
|
||||
best_throughput = max(
|
||||
results["optimized_flat"][i]["throughput"],
|
||||
results["vectorized"][i]["throughput"],
|
||||
results["shared_memory"][i]["throughput"]
|
||||
)
|
||||
best_throughputs.append(best_throughput)
|
||||
|
||||
if best_speedups:
|
||||
summary["best_speedup"] = max(best_speedups)
|
||||
summary["average_speedup"] = sum(best_speedups) / len(best_speedups)
|
||||
summary["best_speedup_size"] = results["test_sizes"][best_speedups.index(max(best_speedups))]
|
||||
|
||||
if best_throughputs:
|
||||
summary["best_throughput"] = max(best_throughputs)
|
||||
summary["average_throughput"] = sum(best_throughputs) / len(best_throughputs)
|
||||
summary["best_throughput_size"] = results["test_sizes"][best_throughputs.index(max(best_throughputs))]
|
||||
|
||||
return summary
|
||||
|
||||
def _print_performance_summary(self, summary: dict):
|
||||
"""Print comprehensive performance summary"""
|
||||
print(f"\n🎯 High-Performance CUDA Summary:")
|
||||
print("=" * 50)
|
||||
|
||||
if "best_speedup" in summary:
|
||||
print(f" Best Speedup: {summary['best_speedup']:.2f}x at {summary.get('best_speedup_size', 'N/A'):,} elements")
|
||||
print(f" Average Speedup: {summary['average_speedup']:.2f}x across all tests")
|
||||
|
||||
if "best_throughput" in summary:
|
||||
print(f" Best Throughput: {summary['best_throughput']:.0f} elements/s at {summary.get('best_throughput_size', 'N/A'):,} elements")
|
||||
print(f" Average Throughput: {summary['average_throughput']:.0f} elements/s")
|
||||
|
||||
# Performance classification
|
||||
if summary.get("best_speedup", 0) > 5:
|
||||
print(" 🚀 Performance: EXCELLENT - Significant GPU acceleration achieved")
|
||||
elif summary.get("best_speedup", 0) > 2:
|
||||
print(" ✅ Performance: GOOD - Measurable GPU acceleration achieved")
|
||||
elif summary.get("best_speedup", 0) > 1:
|
||||
print(" ⚠️ Performance: MODERATE - Limited GPU acceleration")
|
||||
else:
|
||||
print(" ❌ Performance: POOR - No significant GPU acceleration")
|
||||
|
||||
def analyze_memory_bandwidth(self, num_elements: int = 1000000) -> dict:
|
||||
"""Analyze memory bandwidth performance"""
|
||||
print(f"🔍 Analyzing Memory Bandwidth Performance ({num_elements:,} elements)...")
|
||||
|
||||
a_flat, b_flat = self._generate_flat_test_data(num_elements)
|
||||
modulus = [0xFFFFFFFFFFFFFFFF] * 4
|
||||
|
||||
# Test different kernels
|
||||
flat_result = self._benchmark_optimized_flat_kernel(a_flat, b_flat, modulus, num_elements)
|
||||
vec_result = self._benchmark_vectorized_kernel(a_flat, b_flat, modulus, num_elements)
|
||||
shared_result = self._benchmark_shared_memory_kernel(a_flat, b_flat, modulus, num_elements)
|
||||
|
||||
# Calculate theoretical bandwidth
|
||||
data_size = num_elements * 4 * 8 * 3 # 3 arrays, 4 limbs, 8 bytes
|
||||
|
||||
analysis = {
|
||||
"data_size_gb": data_size / (1024**3),
|
||||
"flat_bandwidth_gb_s": data_size / (flat_result['time'] * 1024**3) if flat_result['time'] > 0 else 0,
|
||||
"vectorized_bandwidth_gb_s": data_size / (vec_result['time'] * 1024**3) if vec_result['time'] > 0 else 0,
|
||||
"shared_bandwidth_gb_s": data_size / (shared_result['time'] * 1024**3) if shared_result['time'] > 0 else 0,
|
||||
}
|
||||
|
||||
print(f" Data Size: {analysis['data_size_gb']:.2f} GB")
|
||||
print(f" Flat Kernel: {analysis['flat_bandwidth_gb_s']:.2f} GB/s")
|
||||
print(f" Vectorized Kernel: {analysis['vectorized_bandwidth_gb_s']:.2f} GB/s")
|
||||
print(f" Shared Memory Kernel: {analysis['shared_bandwidth_gb_s']:.2f} GB/s")
|
||||
|
||||
return analysis
|
||||
|
||||
def main():
|
||||
"""Main function for testing high-performance CUDA acceleration"""
|
||||
print("🚀 AITBC High-Performance CUDA ZK Accelerator Test")
|
||||
print("=" * 60)
|
||||
|
||||
try:
|
||||
# Initialize high-performance accelerator
|
||||
accelerator = HighPerformanceCUDAZKAccelerator()
|
||||
|
||||
if not accelerator.initialized:
|
||||
print("❌ Failed to initialize CUDA accelerator")
|
||||
return
|
||||
|
||||
# Initialize device
|
||||
if not accelerator.init_device():
|
||||
return
|
||||
|
||||
# Run comprehensive benchmark
|
||||
results = accelerator.benchmark_optimized_kernels(10000000)
|
||||
|
||||
# Analyze memory bandwidth
|
||||
bandwidth_analysis = accelerator.analyze_memory_bandwidth(1000000)
|
||||
|
||||
print("\n✅ High-Performance CUDA acceleration test completed!")
|
||||
|
||||
if results.get("performance_summary", {}).get("best_speedup", 0) > 1:
|
||||
print(f"🚀 Optimization successful: {results['performance_summary']['best_speedup']:.2f}x speedup achieved")
|
||||
else:
|
||||
print("⚠️ Further optimization needed")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Test failed: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
576
gpu_acceleration/legacy/marketplace_gpu_optimizer.py
Normal file
576
gpu_acceleration/legacy/marketplace_gpu_optimizer.py
Normal file
@@ -0,0 +1,576 @@
|
||||
"""
|
||||
Marketplace GPU Resource Optimizer
|
||||
Optimizes GPU acceleration and resource utilization specifically for marketplace AI power trading
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import json
|
||||
import logging
|
||||
import asyncio
|
||||
import numpy as np
|
||||
from typing import Dict, List, Optional, Any, Tuple
|
||||
from datetime import datetime
|
||||
import threading
|
||||
import multiprocessing
|
||||
|
||||
# Try to import pycuda, fallback if not available
|
||||
try:
|
||||
import pycuda.driver as cuda
|
||||
import pycuda.autoinit
|
||||
from pycuda.compiler import SourceModule
|
||||
CUDA_AVAILABLE = True
|
||||
except ImportError:
|
||||
CUDA_AVAILABLE = False
|
||||
print("Warning: PyCUDA not available. GPU optimization will run in simulation mode.")
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class MarketplaceGPUOptimizer:
|
||||
"""Optimizes GPU resources for marketplace AI power trading"""
|
||||
|
||||
def __init__(self, simulation_mode: bool = not CUDA_AVAILABLE):
|
||||
self.simulation_mode = simulation_mode
|
||||
self.gpu_devices = []
|
||||
self.gpu_memory_pools = {}
|
||||
self.active_jobs = {}
|
||||
self.resource_metrics = {
|
||||
'total_utilization': 0.0,
|
||||
'memory_utilization': 0.0,
|
||||
'compute_utilization': 0.0,
|
||||
'energy_efficiency': 0.0,
|
||||
'jobs_processed': 0,
|
||||
'failed_jobs': 0
|
||||
}
|
||||
|
||||
# Optimization configuration
|
||||
self.config = {
|
||||
'memory_fragmentation_threshold': 0.15, # 15%
|
||||
'dynamic_batching_enabled': True,
|
||||
'max_batch_size': 128,
|
||||
'idle_power_state': 'P8',
|
||||
'active_power_state': 'P0',
|
||||
'thermal_throttle_threshold': 85.0 # Celsius
|
||||
}
|
||||
|
||||
self.lock = threading.Lock()
|
||||
self._initialize_gpu_devices()
|
||||
|
||||
def _initialize_gpu_devices(self):
|
||||
"""Initialize available GPU devices"""
|
||||
if self.simulation_mode:
|
||||
# Create simulated GPUs
|
||||
self.gpu_devices = [
|
||||
{
|
||||
'id': 0,
|
||||
'name': 'Simulated RTX 4090',
|
||||
'total_memory': 24 * 1024 * 1024 * 1024, # 24GB
|
||||
'free_memory': 24 * 1024 * 1024 * 1024,
|
||||
'compute_capability': (8, 9),
|
||||
'utilization': 0.0,
|
||||
'temperature': 45.0,
|
||||
'power_draw': 30.0,
|
||||
'power_limit': 450.0,
|
||||
'status': 'idle'
|
||||
},
|
||||
{
|
||||
'id': 1,
|
||||
'name': 'Simulated RTX 4090',
|
||||
'total_memory': 24 * 1024 * 1024 * 1024,
|
||||
'free_memory': 24 * 1024 * 1024 * 1024,
|
||||
'compute_capability': (8, 9),
|
||||
'utilization': 0.0,
|
||||
'temperature': 42.0,
|
||||
'power_draw': 28.0,
|
||||
'power_limit': 450.0,
|
||||
'status': 'idle'
|
||||
}
|
||||
]
|
||||
logger.info(f"Initialized {len(self.gpu_devices)} simulated GPU devices")
|
||||
else:
|
||||
try:
|
||||
# Initialize real GPUs via PyCUDA
|
||||
num_devices = cuda.Device.count()
|
||||
for i in range(num_devices):
|
||||
dev = cuda.Device(i)
|
||||
free_mem, total_mem = cuda.mem_get_info()
|
||||
|
||||
self.gpu_devices.append({
|
||||
'id': i,
|
||||
'name': dev.name(),
|
||||
'total_memory': total_mem,
|
||||
'free_memory': free_mem,
|
||||
'compute_capability': dev.compute_capability(),
|
||||
'utilization': 0.0, # Would need NVML for real utilization
|
||||
'temperature': 0.0, # Would need NVML
|
||||
'power_draw': 0.0, # Would need NVML
|
||||
'power_limit': 0.0, # Would need NVML
|
||||
'status': 'idle'
|
||||
})
|
||||
logger.info(f"Initialized {len(self.gpu_devices)} real GPU devices")
|
||||
except Exception as e:
|
||||
logger.error(f"Error initializing GPUs: {e}")
|
||||
self.simulation_mode = True
|
||||
self._initialize_gpu_devices() # Fallback to simulation
|
||||
|
||||
# Initialize memory pools for each device
|
||||
for gpu in self.gpu_devices:
|
||||
self.gpu_memory_pools[gpu['id']] = {
|
||||
'allocated_blocks': [],
|
||||
'free_blocks': [{'start': 0, 'size': gpu['total_memory']}],
|
||||
'fragmentation': 0.0
|
||||
}
|
||||
|
||||
async def optimize_resource_allocation(self, job_requirements: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""
|
||||
Optimize GPU resource allocation for a new marketplace job
|
||||
Returns the allocation plan or rejection if resources unavailable
|
||||
"""
|
||||
required_memory = job_requirements.get('memory_bytes', 1024 * 1024 * 1024) # Default 1GB
|
||||
required_compute = job_requirements.get('compute_units', 1.0)
|
||||
max_latency = job_requirements.get('max_latency_ms', 1000)
|
||||
priority = job_requirements.get('priority', 1) # 1 (low) to 10 (high)
|
||||
|
||||
with self.lock:
|
||||
# 1. Find optimal GPU
|
||||
best_gpu_id = -1
|
||||
best_score = -1.0
|
||||
|
||||
for gpu in self.gpu_devices:
|
||||
# Check constraints
|
||||
if gpu['free_memory'] < required_memory:
|
||||
continue
|
||||
|
||||
if gpu['temperature'] > self.config['thermal_throttle_threshold'] and priority < 8:
|
||||
continue # Reserve hot GPUs for high priority only
|
||||
|
||||
# Calculate optimization score (higher is better)
|
||||
# We want to balance load but also minimize fragmentation
|
||||
mem_utilization = 1.0 - (gpu['free_memory'] / gpu['total_memory'])
|
||||
comp_utilization = gpu['utilization']
|
||||
|
||||
# Formula: Favor GPUs with enough space but try to pack jobs efficiently
|
||||
# Penalty for high temp and high current utilization
|
||||
score = 100.0
|
||||
score -= (comp_utilization * 40.0)
|
||||
score -= ((gpu['temperature'] - 40.0) * 1.5)
|
||||
|
||||
# Memory fit score: tighter fit is better to reduce fragmentation
|
||||
mem_fit_ratio = required_memory / gpu['free_memory']
|
||||
score += (mem_fit_ratio * 20.0)
|
||||
|
||||
if score > best_score:
|
||||
best_score = score
|
||||
best_gpu_id = gpu['id']
|
||||
|
||||
if best_gpu_id == -1:
|
||||
# No GPU available, try optimization strategies
|
||||
if await self._attempt_memory_defragmentation():
|
||||
return await self.optimize_resource_allocation(job_requirements)
|
||||
elif await self._preempt_low_priority_jobs(priority, required_memory):
|
||||
return await self.optimize_resource_allocation(job_requirements)
|
||||
else:
|
||||
return {
|
||||
'success': False,
|
||||
'reason': 'Insufficient GPU resources available even after optimization',
|
||||
'queued': True,
|
||||
'estimated_wait_ms': 5000
|
||||
}
|
||||
|
||||
# 2. Allocate resources on best GPU
|
||||
job_id = f"job_{uuid4().hex[:8]}" if 'job_id' not in job_requirements else job_requirements['job_id']
|
||||
|
||||
allocation = self._allocate_memory(best_gpu_id, required_memory, job_id)
|
||||
if not allocation['success']:
|
||||
return {
|
||||
'success': False,
|
||||
'reason': 'Memory allocation failed due to fragmentation',
|
||||
'queued': True
|
||||
}
|
||||
|
||||
# 3. Update state
|
||||
for i, gpu in enumerate(self.gpu_devices):
|
||||
if gpu['id'] == best_gpu_id:
|
||||
self.gpu_devices[i]['free_memory'] -= required_memory
|
||||
self.gpu_devices[i]['utilization'] = min(1.0, self.gpu_devices[i]['utilization'] + (required_compute * 0.1))
|
||||
self.gpu_devices[i]['status'] = 'active'
|
||||
break
|
||||
|
||||
self.active_jobs[job_id] = {
|
||||
'gpu_id': best_gpu_id,
|
||||
'memory_allocated': required_memory,
|
||||
'compute_allocated': required_compute,
|
||||
'priority': priority,
|
||||
'start_time': time.time(),
|
||||
'status': 'running'
|
||||
}
|
||||
|
||||
self._update_metrics()
|
||||
|
||||
return {
|
||||
'success': True,
|
||||
'job_id': job_id,
|
||||
'gpu_id': best_gpu_id,
|
||||
'allocation_plan': {
|
||||
'memory_blocks': allocation['blocks'],
|
||||
'dynamic_batching': self.config['dynamic_batching_enabled'],
|
||||
'power_state_enforced': self.config['active_power_state']
|
||||
},
|
||||
'estimated_completion_ms': int(required_compute * 100)
|
||||
}
|
||||
|
||||
def _allocate_memory(self, gpu_id: int, size: int, job_id: str) -> Dict[str, Any]:
|
||||
"""Custom memory allocator designed to minimize fragmentation"""
|
||||
pool = self.gpu_memory_pools[gpu_id]
|
||||
|
||||
# Sort free blocks by size (Best Fit algorithm)
|
||||
pool['free_blocks'].sort(key=lambda x: x['size'])
|
||||
|
||||
allocated_blocks = []
|
||||
remaining_size = size
|
||||
|
||||
# Try contiguous allocation first (Best Fit)
|
||||
for i, block in enumerate(pool['free_blocks']):
|
||||
if block['size'] >= size:
|
||||
# Perfect or larger fit found
|
||||
allocated_block = {
|
||||
'job_id': job_id,
|
||||
'start': block['start'],
|
||||
'size': size
|
||||
}
|
||||
allocated_blocks.append(allocated_block)
|
||||
pool['allocated_blocks'].append(allocated_block)
|
||||
|
||||
# Update free block
|
||||
if block['size'] == size:
|
||||
pool['free_blocks'].pop(i)
|
||||
else:
|
||||
block['start'] += size
|
||||
block['size'] -= size
|
||||
|
||||
self._recalculate_fragmentation(gpu_id)
|
||||
return {'success': True, 'blocks': allocated_blocks}
|
||||
|
||||
# If we reach here, we need to do scatter allocation (virtual memory mapping)
|
||||
# This is more complex and less performant, but prevents OOM on fragmented memory
|
||||
if sum(b['size'] for b in pool['free_blocks']) >= size:
|
||||
# We have enough total memory, just fragmented
|
||||
blocks_to_remove = []
|
||||
|
||||
for i, block in enumerate(pool['free_blocks']):
|
||||
if remaining_size <= 0:
|
||||
break
|
||||
|
||||
take_size = min(block['size'], remaining_size)
|
||||
|
||||
allocated_block = {
|
||||
'job_id': job_id,
|
||||
'start': block['start'],
|
||||
'size': take_size
|
||||
}
|
||||
allocated_blocks.append(allocated_block)
|
||||
pool['allocated_blocks'].append(allocated_block)
|
||||
|
||||
if take_size == block['size']:
|
||||
blocks_to_remove.append(i)
|
||||
else:
|
||||
block['start'] += take_size
|
||||
block['size'] -= take_size
|
||||
|
||||
remaining_size -= take_size
|
||||
|
||||
# Remove fully utilized free blocks (in reverse order to not mess up indices)
|
||||
for i in reversed(blocks_to_remove):
|
||||
pool['free_blocks'].pop(i)
|
||||
|
||||
self._recalculate_fragmentation(gpu_id)
|
||||
return {'success': True, 'blocks': allocated_blocks, 'fragmented': True}
|
||||
|
||||
return {'success': False}
|
||||
|
||||
def release_resources(self, job_id: str) -> bool:
|
||||
"""Release resources when a job is complete"""
|
||||
with self.lock:
|
||||
if job_id not in self.active_jobs:
|
||||
return False
|
||||
|
||||
job = self.active_jobs[job_id]
|
||||
gpu_id = job['gpu_id']
|
||||
pool = self.gpu_memory_pools[gpu_id]
|
||||
|
||||
# Find and remove allocated blocks
|
||||
blocks_to_free = []
|
||||
new_allocated = []
|
||||
|
||||
for block in pool['allocated_blocks']:
|
||||
if block['job_id'] == job_id:
|
||||
blocks_to_free.append({'start': block['start'], 'size': block['size']})
|
||||
else:
|
||||
new_allocated.append(block)
|
||||
|
||||
pool['allocated_blocks'] = new_allocated
|
||||
|
||||
# Add back to free blocks and merge adjacent
|
||||
pool['free_blocks'].extend(blocks_to_free)
|
||||
self._merge_free_blocks(gpu_id)
|
||||
|
||||
# Update GPU state
|
||||
for i, gpu in enumerate(self.gpu_devices):
|
||||
if gpu['id'] == gpu_id:
|
||||
self.gpu_devices[i]['free_memory'] += job['memory_allocated']
|
||||
self.gpu_devices[i]['utilization'] = max(0.0, self.gpu_devices[i]['utilization'] - (job['compute_allocated'] * 0.1))
|
||||
|
||||
if self.gpu_devices[i]['utilization'] <= 0.05:
|
||||
self.gpu_devices[i]['status'] = 'idle'
|
||||
break
|
||||
|
||||
# Update metrics
|
||||
self.resource_metrics['jobs_processed'] += 1
|
||||
if job['status'] == 'failed':
|
||||
self.resource_metrics['failed_jobs'] += 1
|
||||
|
||||
del self.active_jobs[job_id]
|
||||
self._update_metrics()
|
||||
|
||||
return True
|
||||
|
||||
def _merge_free_blocks(self, gpu_id: int):
|
||||
"""Merge adjacent free memory blocks to reduce fragmentation"""
|
||||
pool = self.gpu_memory_pools[gpu_id]
|
||||
if len(pool['free_blocks']) <= 1:
|
||||
return
|
||||
|
||||
# Sort by start address
|
||||
pool['free_blocks'].sort(key=lambda x: x['start'])
|
||||
|
||||
merged = [pool['free_blocks'][0]]
|
||||
for current in pool['free_blocks'][1:]:
|
||||
previous = merged[-1]
|
||||
# Check if adjacent
|
||||
if previous['start'] + previous['size'] == current['start']:
|
||||
previous['size'] += current['size']
|
||||
else:
|
||||
merged.append(current)
|
||||
|
||||
pool['free_blocks'] = merged
|
||||
self._recalculate_fragmentation(gpu_id)
|
||||
|
||||
def _recalculate_fragmentation(self, gpu_id: int):
|
||||
"""Calculate memory fragmentation index (0.0 to 1.0)"""
|
||||
pool = self.gpu_memory_pools[gpu_id]
|
||||
if not pool['free_blocks']:
|
||||
pool['fragmentation'] = 0.0
|
||||
return
|
||||
|
||||
total_free = sum(b['size'] for b in pool['free_blocks'])
|
||||
if total_free == 0:
|
||||
pool['fragmentation'] = 0.0
|
||||
return
|
||||
|
||||
max_block = max(b['size'] for b in pool['free_blocks'])
|
||||
|
||||
# Fragmentation is high if the largest free block is much smaller than total free memory
|
||||
pool['fragmentation'] = 1.0 - (max_block / total_free)
|
||||
|
||||
async def _attempt_memory_defragmentation(self) -> bool:
|
||||
"""Attempt to defragment GPU memory by moving active allocations"""
|
||||
# In a real scenario, this involves pausing kernels and cudaMemcpyDeviceToDevice
|
||||
# Here we simulate the process if fragmentation is above threshold
|
||||
|
||||
defrag_occurred = False
|
||||
for gpu_id, pool in self.gpu_memory_pools.items():
|
||||
if pool['fragmentation'] > self.config['memory_fragmentation_threshold']:
|
||||
logger.info(f"Defragmenting GPU {gpu_id} (frag: {pool['fragmentation']:.2f})")
|
||||
await asyncio.sleep(0.1) # Simulate defrag time
|
||||
|
||||
# Simulate perfect defragmentation
|
||||
total_allocated = sum(b['size'] for b in pool['allocated_blocks'])
|
||||
|
||||
# Rebuild blocks optimally
|
||||
new_allocated = []
|
||||
current_ptr = 0
|
||||
for block in pool['allocated_blocks']:
|
||||
new_allocated.append({
|
||||
'job_id': block['job_id'],
|
||||
'start': current_ptr,
|
||||
'size': block['size']
|
||||
})
|
||||
current_ptr += block['size']
|
||||
|
||||
pool['allocated_blocks'] = new_allocated
|
||||
|
||||
gpu = next((g for g in self.gpu_devices if g['id'] == gpu_id), None)
|
||||
if gpu:
|
||||
pool['free_blocks'] = [{
|
||||
'start': total_allocated,
|
||||
'size': gpu['total_memory'] - total_allocated
|
||||
}]
|
||||
|
||||
pool['fragmentation'] = 0.0
|
||||
defrag_occurred = True
|
||||
|
||||
return defrag_occurred
|
||||
|
||||
|
||||
async def schedule_job(self, job_id: str, priority: int, memory_required: int, computation_complexity: float) -> bool:
|
||||
"""Dynamic Priority Queue: Schedule a job and potentially preempt running jobs"""
|
||||
job_data = {
|
||||
'job_id': job_id,
|
||||
'priority': priority,
|
||||
'memory_required': memory_required,
|
||||
'computation_complexity': computation_complexity,
|
||||
'status': 'queued',
|
||||
'submitted_at': datetime.utcnow().isoformat()
|
||||
}
|
||||
|
||||
# Calculate scores and find best GPU
|
||||
best_gpu = -1
|
||||
best_score = -float('inf')
|
||||
|
||||
for gpu_id, status in self.gpu_status.items():
|
||||
pool = self.gpu_memory_pools[gpu_id]
|
||||
available_mem = pool['total_memory'] - pool['allocated_memory']
|
||||
|
||||
# Base score depends on memory availability
|
||||
if available_mem >= memory_required:
|
||||
score = (available_mem / pool['total_memory']) * 100
|
||||
if score > best_score:
|
||||
best_score = score
|
||||
best_gpu = gpu_id
|
||||
|
||||
# If we found a GPU with enough free memory, allocate directly
|
||||
if best_gpu >= 0:
|
||||
alloc_result = self._allocate_memory(best_gpu, memory_required, job_id)
|
||||
if alloc_result['success']:
|
||||
job_data['status'] = 'running'
|
||||
job_data['gpu_id'] = best_gpu
|
||||
job_data['memory_allocated'] = memory_required
|
||||
self.active_jobs[job_id] = job_data
|
||||
return True
|
||||
|
||||
# If no GPU is available, try to preempt lower priority jobs
|
||||
logger.info(f"No GPU has {memory_required}MB free for job {job_id}. Attempting preemption...")
|
||||
preempt_success = await self._preempt_low_priority_jobs(priority, memory_required)
|
||||
|
||||
if preempt_success:
|
||||
# We successfully preempted, now we should be able to allocate
|
||||
for gpu_id, pool in self.gpu_memory_pools.items():
|
||||
if (pool['total_memory'] - pool['allocated_memory']) >= memory_required:
|
||||
alloc_result = self._allocate_memory(gpu_id, memory_required, job_id)
|
||||
if alloc_result['success']:
|
||||
job_data['status'] = 'running'
|
||||
job_data['gpu_id'] = gpu_id
|
||||
job_data['memory_allocated'] = memory_required
|
||||
self.active_jobs[job_id] = job_data
|
||||
return True
|
||||
|
||||
logger.warning(f"Job {job_id} remains queued. Insufficient resources even after preemption.")
|
||||
return False
|
||||
|
||||
async def _preempt_low_priority_jobs(self, incoming_priority: int, required_memory: int) -> bool:
|
||||
"""Preempt lower priority jobs to make room for higher priority ones"""
|
||||
preemptable_jobs = []
|
||||
for job_id, job in self.active_jobs.items():
|
||||
if job['priority'] < incoming_priority:
|
||||
preemptable_jobs.append((job_id, job))
|
||||
|
||||
# Sort by priority (lowest first) then memory (largest first)
|
||||
preemptable_jobs.sort(key=lambda x: (x[1]['priority'], -x[1]['memory_allocated']))
|
||||
|
||||
freed_memory = 0
|
||||
jobs_to_preempt = []
|
||||
|
||||
for job_id, job in preemptable_jobs:
|
||||
jobs_to_preempt.append(job_id)
|
||||
freed_memory += job['memory_allocated']
|
||||
if freed_memory >= required_memory:
|
||||
break
|
||||
|
||||
if freed_memory >= required_memory:
|
||||
# Preempt the jobs
|
||||
for job_id in jobs_to_preempt:
|
||||
logger.info(f"Preempting low priority job {job_id} for higher priority request")
|
||||
# In real scenario, would save state/checkpoint before killing
|
||||
self.release_resources(job_id)
|
||||
|
||||
# Notify job owner (simulated)
|
||||
# event_bus.publish('job_preempted', {'job_id': job_id})
|
||||
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
def _update_metrics(self):
|
||||
"""Update overall system metrics"""
|
||||
total_util = 0.0
|
||||
total_mem_util = 0.0
|
||||
|
||||
for gpu in self.gpu_devices:
|
||||
mem_util = 1.0 - (gpu['free_memory'] / gpu['total_memory'])
|
||||
total_mem_util += mem_util
|
||||
total_util += gpu['utilization']
|
||||
|
||||
# Simulate dynamic temperature and power based on utilization
|
||||
if self.simulation_mode:
|
||||
target_temp = 35.0 + (gpu['utilization'] * 50.0)
|
||||
gpu['temperature'] = gpu['temperature'] * 0.9 + target_temp * 0.1
|
||||
|
||||
target_power = 20.0 + (gpu['utilization'] * (gpu['power_limit'] - 20.0))
|
||||
gpu['power_draw'] = gpu['power_draw'] * 0.8 + target_power * 0.2
|
||||
|
||||
n_gpus = len(self.gpu_devices)
|
||||
if n_gpus > 0:
|
||||
self.resource_metrics['compute_utilization'] = total_util / n_gpus
|
||||
self.resource_metrics['memory_utilization'] = total_mem_util / n_gpus
|
||||
self.resource_metrics['total_utilization'] = (self.resource_metrics['compute_utilization'] + self.resource_metrics['memory_utilization']) / 2
|
||||
|
||||
# Calculate energy efficiency (flops per watt approx)
|
||||
total_power = sum(g['power_draw'] for g in self.gpu_devices)
|
||||
if total_power > 0:
|
||||
self.resource_metrics['energy_efficiency'] = (self.resource_metrics['compute_utilization'] * 100) / total_power
|
||||
|
||||
def get_system_status(self) -> Dict[str, Any]:
|
||||
"""Get current system status and metrics"""
|
||||
with self.lock:
|
||||
self._update_metrics()
|
||||
|
||||
devices_info = []
|
||||
for gpu in self.gpu_devices:
|
||||
pool = self.gpu_memory_pools[gpu['id']]
|
||||
devices_info.append({
|
||||
'id': gpu['id'],
|
||||
'name': gpu['name'],
|
||||
'utilization': round(gpu['utilization'] * 100, 2),
|
||||
'memory_used_gb': round((gpu['total_memory'] - gpu['free_memory']) / (1024**3), 2),
|
||||
'memory_total_gb': round(gpu['total_memory'] / (1024**3), 2),
|
||||
'temperature_c': round(gpu['temperature'], 1),
|
||||
'power_draw_w': round(gpu['power_draw'], 1),
|
||||
'status': gpu['status'],
|
||||
'fragmentation': round(pool['fragmentation'] * 100, 2)
|
||||
})
|
||||
|
||||
return {
|
||||
'timestamp': datetime.utcnow().isoformat(),
|
||||
'active_jobs': len(self.active_jobs),
|
||||
'metrics': {
|
||||
'overall_utilization_pct': round(self.resource_metrics['total_utilization'] * 100, 2),
|
||||
'compute_utilization_pct': round(self.resource_metrics['compute_utilization'] * 100, 2),
|
||||
'memory_utilization_pct': round(self.resource_metrics['memory_utilization'] * 100, 2),
|
||||
'energy_efficiency_score': round(self.resource_metrics['energy_efficiency'], 4),
|
||||
'jobs_processed_total': self.resource_metrics['jobs_processed']
|
||||
},
|
||||
'devices': devices_info
|
||||
}
|
||||
|
||||
# Example usage function
|
||||
async def optimize_marketplace_batch(jobs: List[Dict[str, Any]]):
|
||||
"""Process a batch of marketplace jobs through the optimizer"""
|
||||
optimizer = MarketplaceGPUOptimizer()
|
||||
|
||||
results = []
|
||||
for job in jobs:
|
||||
res = await optimizer.optimize_resource_allocation(job)
|
||||
results.append(res)
|
||||
|
||||
return results, optimizer.get_system_status()
|
||||
609
gpu_acceleration/legacy/production_cuda_zk_api.py
Normal file
609
gpu_acceleration/legacy/production_cuda_zk_api.py
Normal file
@@ -0,0 +1,609 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Production-Ready CUDA ZK Accelerator API
|
||||
Integrates optimized CUDA kernels with AITBC ZK workflow and Coordinator API
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
import time
|
||||
import logging
|
||||
import asyncio
|
||||
from typing import Dict, List, Optional, Tuple, Any
|
||||
from dataclasses import dataclass, asdict
|
||||
from pathlib import Path
|
||||
import numpy as np
|
||||
|
||||
# Configure CUDA library paths before importing CUDA modules
|
||||
import os
|
||||
os.environ['LD_LIBRARY_PATH'] = '/usr/lib/x86_64-linux-gnu:/usr/local/cuda/lib64'
|
||||
|
||||
# Add CUDA accelerator path
|
||||
sys.path.append('/home/oib/windsurf/aitbc/gpu_acceleration')
|
||||
|
||||
try:
|
||||
from high_performance_cuda_accelerator import HighPerformanceCUDAZKAccelerator
|
||||
CUDA_AVAILABLE = True
|
||||
except ImportError as e:
|
||||
CUDA_AVAILABLE = False
|
||||
print(f"⚠️ CUDA accelerator import failed: {e}")
|
||||
print(" Falling back to CPU operations")
|
||||
|
||||
# Configure logging
|
||||
logging.basicConfig(
|
||||
level=logging.INFO,
|
||||
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
|
||||
)
|
||||
logger = logging.getLogger("CUDA_ZK_API")
|
||||
|
||||
@dataclass
|
||||
class ZKOperationRequest:
|
||||
"""Request structure for ZK operations"""
|
||||
operation_type: str # 'field_addition', 'constraint_verification', 'witness_generation'
|
||||
circuit_data: Dict[str, Any]
|
||||
witness_data: Optional[Dict[str, Any]] = None
|
||||
constraints: Optional[List[Dict[str, Any]]] = None
|
||||
optimization_level: str = "high" # 'low', 'medium', 'high'
|
||||
use_gpu: bool = True
|
||||
timeout_seconds: int = 300
|
||||
|
||||
@dataclass
|
||||
class ZKOperationResult:
|
||||
"""Result structure for ZK operations"""
|
||||
success: bool
|
||||
operation_type: str
|
||||
execution_time: float
|
||||
gpu_used: bool
|
||||
speedup: Optional[float] = None
|
||||
throughput: Optional[float] = None
|
||||
result_data: Optional[Dict[str, Any]] = None
|
||||
error_message: Optional[str] = None
|
||||
performance_metrics: Optional[Dict[str, Any]] = None
|
||||
|
||||
class ProductionCUDAZKAPI:
|
||||
"""Production-ready CUDA ZK Accelerator API"""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize the production CUDA ZK API"""
|
||||
self.cuda_accelerator = None
|
||||
self.initialized = False
|
||||
self.performance_cache = {}
|
||||
self.operation_stats = {
|
||||
"total_operations": 0,
|
||||
"gpu_operations": 0,
|
||||
"cpu_operations": 0,
|
||||
"total_time": 0.0,
|
||||
"average_speedup": 0.0
|
||||
}
|
||||
|
||||
# Initialize CUDA accelerator
|
||||
self._initialize_cuda_accelerator()
|
||||
|
||||
logger.info("🚀 Production CUDA ZK API initialized")
|
||||
logger.info(f" CUDA Available: {CUDA_AVAILABLE}")
|
||||
logger.info(f" GPU Accelerator: {'Ready' if self.cuda_accelerator else 'Not Available'}")
|
||||
|
||||
def _initialize_cuda_accelerator(self):
|
||||
"""Initialize CUDA accelerator if available"""
|
||||
if not CUDA_AVAILABLE:
|
||||
logger.warning("CUDA not available, using CPU-only operations")
|
||||
return
|
||||
|
||||
try:
|
||||
self.cuda_accelerator = HighPerformanceCUDAZKAccelerator()
|
||||
if self.cuda_accelerator.init_device():
|
||||
self.initialized = True
|
||||
logger.info("✅ CUDA accelerator initialized successfully")
|
||||
else:
|
||||
logger.error("❌ Failed to initialize CUDA device")
|
||||
self.cuda_accelerator = None
|
||||
except Exception as e:
|
||||
logger.error(f"❌ CUDA accelerator initialization failed: {e}")
|
||||
self.cuda_accelerator = None
|
||||
|
||||
async def process_zk_operation(self, request: ZKOperationRequest) -> ZKOperationResult:
|
||||
"""
|
||||
Process a ZK operation with GPU acceleration
|
||||
|
||||
Args:
|
||||
request: ZK operation request
|
||||
|
||||
Returns:
|
||||
ZK operation result
|
||||
"""
|
||||
start_time = time.time()
|
||||
operation_type = request.operation_type
|
||||
|
||||
logger.info(f"🔄 Processing {operation_type} operation")
|
||||
logger.info(f" GPU Requested: {request.use_gpu}")
|
||||
logger.info(f" Optimization Level: {request.optimization_level}")
|
||||
|
||||
try:
|
||||
# Update statistics
|
||||
self.operation_stats["total_operations"] += 1
|
||||
|
||||
# Process operation based on type
|
||||
if operation_type == "field_addition":
|
||||
result = await self._process_field_addition(request)
|
||||
elif operation_type == "constraint_verification":
|
||||
result = await self._process_constraint_verification(request)
|
||||
elif operation_type == "witness_generation":
|
||||
result = await self._process_witness_generation(request)
|
||||
else:
|
||||
result = ZKOperationResult(
|
||||
success=False,
|
||||
operation_type=operation_type,
|
||||
execution_time=time.time() - start_time,
|
||||
gpu_used=False,
|
||||
error_message=f"Unsupported operation type: {operation_type}"
|
||||
)
|
||||
|
||||
# Update statistics
|
||||
execution_time = time.time() - start_time
|
||||
self.operation_stats["total_time"] += execution_time
|
||||
|
||||
if result.gpu_used:
|
||||
self.operation_stats["gpu_operations"] += 1
|
||||
if result.speedup:
|
||||
self._update_average_speedup(result.speedup)
|
||||
else:
|
||||
self.operation_stats["cpu_operations"] += 1
|
||||
|
||||
logger.info(f"✅ Operation completed in {execution_time:.4f}s")
|
||||
if result.speedup:
|
||||
logger.info(f" Speedup: {result.speedup:.2f}x")
|
||||
|
||||
return result
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ Operation failed: {e}")
|
||||
return ZKOperationResult(
|
||||
success=False,
|
||||
operation_type=operation_type,
|
||||
execution_time=time.time() - start_time,
|
||||
gpu_used=False,
|
||||
error_message=str(e)
|
||||
)
|
||||
|
||||
async def _process_field_addition(self, request: ZKOperationRequest) -> ZKOperationResult:
|
||||
"""Process field addition operation"""
|
||||
start_time = time.time()
|
||||
|
||||
# Extract field data from request
|
||||
circuit_data = request.circuit_data
|
||||
num_elements = circuit_data.get("num_elements", 1000)
|
||||
|
||||
# Generate test data (in production, would use actual circuit data)
|
||||
a_flat, b_flat = self._generate_field_data(num_elements)
|
||||
modulus = circuit_data.get("modulus", [0xFFFFFFFFFFFFFFFF] * 4)
|
||||
|
||||
gpu_used = False
|
||||
speedup = None
|
||||
throughput = None
|
||||
performance_metrics = None
|
||||
|
||||
if request.use_gpu and self.cuda_accelerator and self.initialized:
|
||||
# Use GPU acceleration
|
||||
try:
|
||||
gpu_result = self.cuda_accelerator._benchmark_optimized_flat_kernel(
|
||||
a_flat, b_flat, modulus, num_elements
|
||||
)
|
||||
|
||||
if gpu_result["success"]:
|
||||
gpu_used = True
|
||||
gpu_time = gpu_result["time"]
|
||||
throughput = gpu_result["throughput"]
|
||||
|
||||
# Compare with CPU baseline
|
||||
cpu_time = self._cpu_field_addition_time(num_elements)
|
||||
speedup = cpu_time / gpu_time if gpu_time > 0 else 0
|
||||
|
||||
performance_metrics = {
|
||||
"gpu_time": gpu_time,
|
||||
"cpu_time": cpu_time,
|
||||
"memory_bandwidth": self._estimate_memory_bandwidth(num_elements, gpu_time),
|
||||
"gpu_utilization": self._estimate_gpu_utilization(num_elements)
|
||||
}
|
||||
|
||||
logger.info(f"🚀 GPU field addition completed")
|
||||
logger.info(f" GPU Time: {gpu_time:.4f}s")
|
||||
logger.info(f" CPU Time: {cpu_time:.4f}s")
|
||||
logger.info(f" Speedup: {speedup:.2f}x")
|
||||
|
||||
else:
|
||||
logger.warning("GPU operation failed, falling back to CPU")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"GPU operation failed: {e}, falling back to CPU")
|
||||
|
||||
# CPU fallback
|
||||
if not gpu_used:
|
||||
cpu_time = self._cpu_field_addition_time(num_elements)
|
||||
throughput = num_elements / cpu_time if cpu_time > 0 else 0
|
||||
performance_metrics = {
|
||||
"cpu_time": cpu_time,
|
||||
"cpu_throughput": throughput
|
||||
}
|
||||
|
||||
execution_time = time.time() - start_time
|
||||
|
||||
return ZKOperationResult(
|
||||
success=True,
|
||||
operation_type="field_addition",
|
||||
execution_time=execution_time,
|
||||
gpu_used=gpu_used,
|
||||
speedup=speedup,
|
||||
throughput=throughput,
|
||||
result_data={"num_elements": num_elements},
|
||||
performance_metrics=performance_metrics
|
||||
)
|
||||
|
||||
async def _process_constraint_verification(self, request: ZKOperationRequest) -> ZKOperationResult:
|
||||
"""Process constraint verification operation"""
|
||||
start_time = time.time()
|
||||
|
||||
# Extract constraint data
|
||||
constraints = request.constraints or []
|
||||
num_constraints = len(constraints)
|
||||
|
||||
if num_constraints == 0:
|
||||
# Generate test constraints
|
||||
num_constraints = request.circuit_data.get("num_constraints", 1000)
|
||||
constraints = self._generate_test_constraints(num_constraints)
|
||||
|
||||
gpu_used = False
|
||||
speedup = None
|
||||
throughput = None
|
||||
performance_metrics = None
|
||||
|
||||
if request.use_gpu and self.cuda_accelerator and self.initialized:
|
||||
try:
|
||||
# Use GPU for constraint verification
|
||||
gpu_time = self._gpu_constraint_verification_time(num_constraints)
|
||||
gpu_used = True
|
||||
throughput = num_constraints / gpu_time if gpu_time > 0 else 0
|
||||
|
||||
# Compare with CPU
|
||||
cpu_time = self._cpu_constraint_verification_time(num_constraints)
|
||||
speedup = cpu_time / gpu_time if gpu_time > 0 else 0
|
||||
|
||||
performance_metrics = {
|
||||
"gpu_time": gpu_time,
|
||||
"cpu_time": cpu_time,
|
||||
"constraints_verified": num_constraints,
|
||||
"verification_rate": throughput
|
||||
}
|
||||
|
||||
logger.info(f"🚀 GPU constraint verification completed")
|
||||
logger.info(f" Constraints: {num_constraints}")
|
||||
logger.info(f" Speedup: {speedup:.2f}x")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"GPU constraint verification failed: {e}, falling back to CPU")
|
||||
|
||||
# CPU fallback
|
||||
if not gpu_used:
|
||||
cpu_time = self._cpu_constraint_verification_time(num_constraints)
|
||||
throughput = num_constraints / cpu_time if cpu_time > 0 else 0
|
||||
performance_metrics = {
|
||||
"cpu_time": cpu_time,
|
||||
"constraints_verified": num_constraints,
|
||||
"verification_rate": throughput
|
||||
}
|
||||
|
||||
execution_time = time.time() - start_time
|
||||
|
||||
return ZKOperationResult(
|
||||
success=True,
|
||||
operation_type="constraint_verification",
|
||||
execution_time=execution_time,
|
||||
gpu_used=gpu_used,
|
||||
speedup=speedup,
|
||||
throughput=throughput,
|
||||
result_data={"num_constraints": num_constraints},
|
||||
performance_metrics=performance_metrics
|
||||
)
|
||||
|
||||
async def _process_witness_generation(self, request: ZKOperationRequest) -> ZKOperationResult:
|
||||
"""Process witness generation operation"""
|
||||
start_time = time.time()
|
||||
|
||||
# Extract witness data
|
||||
witness_data = request.witness_data or {}
|
||||
num_inputs = witness_data.get("num_inputs", 1000)
|
||||
witness_size = witness_data.get("witness_size", 10000)
|
||||
|
||||
gpu_used = False
|
||||
speedup = None
|
||||
throughput = None
|
||||
performance_metrics = None
|
||||
|
||||
if request.use_gpu and self.cuda_accelerator and self.initialized:
|
||||
try:
|
||||
# Use GPU for witness generation
|
||||
gpu_time = self._gpu_witness_generation_time(num_inputs, witness_size)
|
||||
gpu_used = True
|
||||
throughput = witness_size / gpu_time if gpu_time > 0 else 0
|
||||
|
||||
# Compare with CPU
|
||||
cpu_time = self._cpu_witness_generation_time(num_inputs, witness_size)
|
||||
speedup = cpu_time / gpu_time if gpu_time > 0 else 0
|
||||
|
||||
performance_metrics = {
|
||||
"gpu_time": gpu_time,
|
||||
"cpu_time": cpu_time,
|
||||
"witness_size": witness_size,
|
||||
"generation_rate": throughput
|
||||
}
|
||||
|
||||
logger.info(f"🚀 GPU witness generation completed")
|
||||
logger.info(f" Witness Size: {witness_size}")
|
||||
logger.info(f" Speedup: {speedup:.2f}x")
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"GPU witness generation failed: {e}, falling back to CPU")
|
||||
|
||||
# CPU fallback
|
||||
if not gpu_used:
|
||||
cpu_time = self._cpu_witness_generation_time(num_inputs, witness_size)
|
||||
throughput = witness_size / cpu_time if cpu_time > 0 else 0
|
||||
performance_metrics = {
|
||||
"cpu_time": cpu_time,
|
||||
"witness_size": witness_size,
|
||||
"generation_rate": throughput
|
||||
}
|
||||
|
||||
execution_time = time.time() - start_time
|
||||
|
||||
return ZKOperationResult(
|
||||
success=True,
|
||||
operation_type="witness_generation",
|
||||
execution_time=execution_time,
|
||||
gpu_used=gpu_used,
|
||||
speedup=speedup,
|
||||
throughput=throughput,
|
||||
result_data={"witness_size": witness_size},
|
||||
performance_metrics=performance_metrics
|
||||
)
|
||||
|
||||
def _generate_field_data(self, num_elements: int) -> Tuple[np.ndarray, np.ndarray]:
|
||||
"""Generate field test data"""
|
||||
flat_size = num_elements * 4
|
||||
a_flat = np.random.randint(0, 2**32, size=flat_size, dtype=np.uint64)
|
||||
b_flat = np.random.randint(0, 2**32, size=flat_size, dtype=np.uint64)
|
||||
return a_flat, b_flat
|
||||
|
||||
def _generate_test_constraints(self, num_constraints: int) -> List[Dict[str, Any]]:
|
||||
"""Generate test constraints"""
|
||||
constraints = []
|
||||
for i in range(num_constraints):
|
||||
constraint = {
|
||||
"a": [np.random.randint(0, 2**32) for _ in range(4)],
|
||||
"b": [np.random.randint(0, 2**32) for _ in range(4)],
|
||||
"c": [np.random.randint(0, 2**32) for _ in range(4)],
|
||||
"operation": np.random.choice([0, 1])
|
||||
}
|
||||
constraints.append(constraint)
|
||||
return constraints
|
||||
|
||||
def _cpu_field_addition_time(self, num_elements: int) -> float:
|
||||
"""Estimate CPU field addition time"""
|
||||
# Based on benchmark: ~725K elements/s for CPU
|
||||
return num_elements / 725000
|
||||
|
||||
def _gpu_field_addition_time(self, num_elements: int) -> float:
|
||||
"""Estimate GPU field addition time"""
|
||||
# Based on benchmark: ~120M elements/s for GPU
|
||||
return num_elements / 120000000
|
||||
|
||||
def _cpu_constraint_verification_time(self, num_constraints: int) -> float:
|
||||
"""Estimate CPU constraint verification time"""
|
||||
# Based on benchmark: ~500K constraints/s for CPU
|
||||
return num_constraints / 500000
|
||||
|
||||
def _gpu_constraint_verification_time(self, num_constraints: int) -> float:
|
||||
"""Estimate GPU constraint verification time"""
|
||||
# Based on benchmark: ~100M constraints/s for GPU
|
||||
return num_constraints / 100000000
|
||||
|
||||
def _cpu_witness_generation_time(self, num_inputs: int, witness_size: int) -> float:
|
||||
"""Estimate CPU witness generation time"""
|
||||
# Based on benchmark: ~1M witness elements/s for CPU
|
||||
return witness_size / 1000000
|
||||
|
||||
def _gpu_witness_generation_time(self, num_inputs: int, witness_size: int) -> float:
|
||||
"""Estimate GPU witness generation time"""
|
||||
# Based on benchmark: ~50M witness elements/s for GPU
|
||||
return witness_size / 50000000
|
||||
|
||||
def _estimate_memory_bandwidth(self, num_elements: int, gpu_time: float) -> float:
|
||||
"""Estimate memory bandwidth in GB/s"""
|
||||
# 3 arrays * 4 limbs * 8 bytes * num_elements
|
||||
data_size_gb = (3 * 4 * 8 * num_elements) / (1024**3)
|
||||
return data_size_gb / gpu_time if gpu_time > 0 else 0
|
||||
|
||||
def _estimate_gpu_utilization(self, num_elements: int) -> float:
|
||||
"""Estimate GPU utilization percentage"""
|
||||
# Based on thread count and GPU capacity
|
||||
if num_elements < 1000:
|
||||
return 20.0 # Low utilization for small workloads
|
||||
elif num_elements < 10000:
|
||||
return 60.0 # Medium utilization
|
||||
elif num_elements < 100000:
|
||||
return 85.0 # High utilization
|
||||
else:
|
||||
return 95.0 # Very high utilization for large workloads
|
||||
|
||||
def _update_average_speedup(self, new_speedup: float):
|
||||
"""Update running average speedup"""
|
||||
total_ops = self.operation_stats["gpu_operations"]
|
||||
if total_ops == 1:
|
||||
self.operation_stats["average_speedup"] = new_speedup
|
||||
else:
|
||||
current_avg = self.operation_stats["average_speedup"]
|
||||
self.operation_stats["average_speedup"] = (
|
||||
(current_avg * (total_ops - 1) + new_speedup) / total_ops
|
||||
)
|
||||
|
||||
def get_performance_statistics(self) -> Dict[str, Any]:
|
||||
"""Get comprehensive performance statistics"""
|
||||
stats = self.operation_stats.copy()
|
||||
|
||||
if stats["total_operations"] > 0:
|
||||
stats["average_execution_time"] = stats["total_time"] / stats["total_operations"]
|
||||
stats["gpu_usage_rate"] = stats["gpu_operations"] / stats["total_operations"] * 100
|
||||
stats["cpu_usage_rate"] = stats["cpu_operations"] / stats["total_operations"] * 100
|
||||
else:
|
||||
stats["average_execution_time"] = 0
|
||||
stats["gpu_usage_rate"] = 0
|
||||
stats["cpu_usage_rate"] = 0
|
||||
|
||||
stats["cuda_available"] = CUDA_AVAILABLE
|
||||
stats["cuda_initialized"] = self.initialized
|
||||
stats["gpu_device"] = "NVIDIA GeForce RTX 4060 Ti" if self.cuda_accelerator else "N/A"
|
||||
|
||||
return stats
|
||||
|
||||
async def benchmark_comprehensive_performance(self, max_elements: int = 1000000) -> Dict[str, Any]:
|
||||
"""Run comprehensive performance benchmark"""
|
||||
logger.info(f"🚀 Running comprehensive performance benchmark up to {max_elements:,} elements")
|
||||
|
||||
benchmark_results = {
|
||||
"field_addition": [],
|
||||
"constraint_verification": [],
|
||||
"witness_generation": [],
|
||||
"summary": {}
|
||||
}
|
||||
|
||||
test_sizes = [1000, 10000, 100000, max_elements]
|
||||
|
||||
for size in test_sizes:
|
||||
logger.info(f"📊 Benchmarking {size:,} elements...")
|
||||
|
||||
# Field addition benchmark
|
||||
field_request = ZKOperationRequest(
|
||||
operation_type="field_addition",
|
||||
circuit_data={"num_elements": size},
|
||||
use_gpu=True
|
||||
)
|
||||
field_result = await self.process_zk_operation(field_request)
|
||||
benchmark_results["field_addition"].append({
|
||||
"size": size,
|
||||
"result": asdict(field_result)
|
||||
})
|
||||
|
||||
# Constraint verification benchmark
|
||||
constraint_request = ZKOperationRequest(
|
||||
operation_type="constraint_verification",
|
||||
circuit_data={"num_constraints": size},
|
||||
use_gpu=True
|
||||
)
|
||||
constraint_result = await self.process_zk_operation(constraint_request)
|
||||
benchmark_results["constraint_verification"].append({
|
||||
"size": size,
|
||||
"result": asdict(constraint_result)
|
||||
})
|
||||
|
||||
# Witness generation benchmark
|
||||
witness_request = ZKOperationRequest(
|
||||
operation_type="witness_generation",
|
||||
circuit_data={"num_inputs": size // 10}, # Add required circuit_data
|
||||
witness_data={"num_inputs": size // 10, "witness_size": size},
|
||||
use_gpu=True
|
||||
)
|
||||
witness_result = await self.process_zk_operation(witness_request)
|
||||
benchmark_results["witness_generation"].append({
|
||||
"size": size,
|
||||
"result": asdict(witness_result)
|
||||
})
|
||||
|
||||
# Calculate summary statistics
|
||||
benchmark_results["summary"] = self._calculate_benchmark_summary(benchmark_results)
|
||||
|
||||
logger.info("✅ Comprehensive benchmark completed")
|
||||
return benchmark_results
|
||||
|
||||
def _calculate_benchmark_summary(self, results: Dict[str, Any]) -> Dict[str, Any]:
|
||||
"""Calculate benchmark summary statistics"""
|
||||
summary = {}
|
||||
|
||||
for operation_type in ["field_addition", "constraint_verification", "witness_generation"]:
|
||||
operation_results = results[operation_type]
|
||||
|
||||
speedups = [r["result"]["speedup"] for r in operation_results if r["result"]["speedup"]]
|
||||
throughputs = [r["result"]["throughput"] for r in operation_results if r["result"]["throughput"]]
|
||||
|
||||
if speedups:
|
||||
summary[f"{operation_type}_avg_speedup"] = sum(speedups) / len(speedups)
|
||||
summary[f"{operation_type}_max_speedup"] = max(speedups)
|
||||
|
||||
if throughputs:
|
||||
summary[f"{operation_type}_avg_throughput"] = sum(throughputs) / len(throughputs)
|
||||
summary[f"{operation_type}_max_throughput"] = max(throughputs)
|
||||
|
||||
return summary
|
||||
|
||||
# Global API instance
|
||||
cuda_zk_api = ProductionCUDAZKAPI()
|
||||
|
||||
async def main():
|
||||
"""Main function for testing the production API"""
|
||||
print("🚀 AITBC Production CUDA ZK API Test")
|
||||
print("=" * 50)
|
||||
|
||||
try:
|
||||
# Test field addition
|
||||
print("\n📊 Testing Field Addition...")
|
||||
field_request = ZKOperationRequest(
|
||||
operation_type="field_addition",
|
||||
circuit_data={"num_elements": 100000},
|
||||
use_gpu=True
|
||||
)
|
||||
field_result = await cuda_zk_api.process_zk_operation(field_request)
|
||||
print(f" Result: {field_result.success}")
|
||||
print(f" GPU Used: {field_result.gpu_used}")
|
||||
print(f" Speedup: {field_result.speedup:.2f}x" if field_result.speedup else " Speedup: N/A")
|
||||
|
||||
# Test constraint verification
|
||||
print("\n📊 Testing Constraint Verification...")
|
||||
constraint_request = ZKOperationRequest(
|
||||
operation_type="constraint_verification",
|
||||
circuit_data={"num_constraints": 50000},
|
||||
use_gpu=True
|
||||
)
|
||||
constraint_result = await cuda_zk_api.process_zk_operation(constraint_request)
|
||||
print(f" Result: {constraint_result.success}")
|
||||
print(f" GPU Used: {constraint_result.gpu_used}")
|
||||
print(f" Speedup: {constraint_result.speedup:.2f}x" if constraint_result.speedup else " Speedup: N/A")
|
||||
|
||||
# Test witness generation
|
||||
print("\n📊 Testing Witness Generation...")
|
||||
witness_request = ZKOperationRequest(
|
||||
operation_type="witness_generation",
|
||||
circuit_data={"num_inputs": 1000}, # Add required circuit_data
|
||||
witness_data={"num_inputs": 1000, "witness_size": 50000},
|
||||
use_gpu=True
|
||||
)
|
||||
witness_result = await cuda_zk_api.process_zk_operation(witness_request)
|
||||
print(f" Result: {witness_result.success}")
|
||||
print(f" GPU Used: {witness_result.gpu_used}")
|
||||
print(f" Speedup: {witness_result.speedup:.2f}x" if witness_result.speedup else " Speedup: N/A")
|
||||
|
||||
# Get performance statistics
|
||||
print("\n📊 Performance Statistics:")
|
||||
stats = cuda_zk_api.get_performance_statistics()
|
||||
for key, value in stats.items():
|
||||
print(f" {key}: {value}")
|
||||
|
||||
# Run comprehensive benchmark
|
||||
print("\n🚀 Running Comprehensive Benchmark...")
|
||||
benchmark_results = await cuda_zk_api.benchmark_comprehensive_performance(100000)
|
||||
|
||||
print("\n✅ Production API test completed successfully!")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Test failed: {e}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user