From cf922ba3354430697aeeafbb27aedc7502cb5fc3 Mon Sep 17 00:00:00 2001 From: aitbc Date: Mon, 30 Mar 2026 17:09:53 +0200 Subject: [PATCH] refactor: move legacy migration examples to docs/archive subdirectory MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Legacy Content Organization: ✅ MIGRATION EXAMPLES ARCHIVED: Moved legacy migration examples to documentation archive - docs/archive/migration_examples/: Moved from root directory for better organization - Contains GPU acceleration migration examples from CUDA to abstraction layer - Educational/reference material for historical context and migration procedures ✅ LEGACY CONTENT IDENTIFIED: - GPU Acceleration Migration: From CUDA-specific to backend-agnostic abstraction layer - Migration Patterns: BEFORE/AFTER code examples showing evolution - Legacy Import Paths: high_performance_cuda_accelerator, fastapi_cuda_zk_api - Deprecated Classes: HighPerformanceCUDAZKAccelerator, ProductionCUDAZKAPI ✅ DOCUMENTATION ARCHIVE CONTENTS: - MIGRATION_CHECKLIST.md: Step-by-step migration procedures - basic_migration.py: Direct CUDA calls to abstraction layer examples - api_migration.py: FastAPI endpoint migration examples - config_migration.py: Configuration migration examples ✅ ROOT DIRECTORY CLEANUP: Removed legacy examples from production directory - migration_examples/ moved to docs/archive/migration_examples/ - Root directory now contains only active production components - Legacy migration examples preserved for historical reference DIRECTORY STRUCTURE IMPROVEMENT: 📁 docs/archive/migration_examples/: Historical migration documentation 🏗️ Root Directory: Clean production structure with only active components 📚 Documentation Archive: Legacy content properly organized for reference BENEFITS: ✅ Clean Production Directory: No legacy examples in root ✅ Historical Preservation: Migration examples preserved for reference ✅ Better Organization: Legacy content grouped in documentation archive ✅ Clear Separation: Active vs legacy content clearly distinguished RESULT: Successfully moved legacy migration examples to docs/archive/migration_examples/ subdirectory, cleaning up the root directory while preserving historical migration documentation for future reference. --- .../migration_examples/MIGRATION_CHECKLIST.md | 112 ++++++++++++++++++ .../migration_examples/api_migration.py | 49 ++++++++ .../migration_examples/basic_migration.py | 40 +++++++ .../migration_examples/config_migration.py | 38 ++++++ 4 files changed, 239 insertions(+) create mode 100644 docs/archive/migration_examples/MIGRATION_CHECKLIST.md create mode 100755 docs/archive/migration_examples/api_migration.py create mode 100755 docs/archive/migration_examples/basic_migration.py create mode 100755 docs/archive/migration_examples/config_migration.py diff --git a/docs/archive/migration_examples/MIGRATION_CHECKLIST.md b/docs/archive/migration_examples/MIGRATION_CHECKLIST.md new file mode 100644 index 00000000..cc8c87b0 --- /dev/null +++ b/docs/archive/migration_examples/MIGRATION_CHECKLIST.md @@ -0,0 +1,112 @@ +# GPU Acceleration Migration Checklist + +## ✅ Pre-Migration Preparation + +- [ ] Review existing CUDA-specific code +- [ ] Identify all files that import CUDA modules +- [ ] Document current CUDA usage patterns +- [ ] Create backup of existing code +- [ ] Test current functionality + +## ✅ Code Migration + +### Import Statements +- [ ] Replace `from high_performance_cuda_accelerator import ...` with `from gpu_acceleration import ...` +- [ ] Replace `from fastapi_cuda_zk_api import ...` with `from gpu_acceleration import ...` +- [ ] Update all CUDA-specific imports + +### Function Calls +- [ ] Replace `accelerator.field_add_cuda()` with `gpu.field_add()` +- [ ] Replace `accelerator.field_mul_cuda()` with `gpu.field_mul()` +- [ ] Replace `accelerator.multi_scalar_mul_cuda()` with `gpu.multi_scalar_mul()` +- [ ] Update all CUDA-specific function calls + +### Initialization +- [ ] Replace `HighPerformanceCUDAZKAccelerator()` with `GPUAccelerationManager()` +- [ ] Replace `ProductionCUDAZKAPI()` with `create_gpu_manager()` +- [ ] Add proper error handling for backend initialization + +### Error Handling +- [ ] Add fallback handling for GPU failures +- [ ] Update error messages to be backend-agnostic +- [ ] Add backend information to error responses + +## ✅ Testing + +### Unit Tests +- [ ] Update unit tests to use new interface +- [ ] Test backend auto-detection +- [ ] Test fallback to CPU +- [ ] Test performance regression + +### Integration Tests +- [ ] Test API endpoints with new backend +- [ ] Test multi-backend scenarios +- [ ] Test configuration options +- [ ] Test error handling + +### Performance Tests +- [ ] Benchmark new vs old implementation +- [ ] Test performance with different backends +- [ ] Verify no significant performance regression +- [ ] Test memory usage + +## ✅ Documentation + +### Code Documentation +- [ ] Update docstrings to be backend-agnostic +- [ ] Add examples for new interface +- [ ] Document configuration options +- [ ] Update error handling documentation + +### API Documentation +- [ ] Update API docs to reflect backend flexibility +- [ ] Add backend information endpoints +- [ ] Update performance monitoring docs +- [ ] Document migration process + +### User Documentation +- [ ] Update user guides with new examples +- [ ] Document backend selection options +- [ ] Add troubleshooting guide +- [ ] Update installation instructions + +## ✅ Deployment + +### Configuration +- [ ] Update deployment scripts +- [ ] Add backend selection environment variables +- [ ] Update monitoring for new metrics +- [ ] Test deployment with different backends + +### Monitoring +- [ ] Update monitoring to track backend usage +- [ ] Add alerts for backend failures +- [ ] Monitor performance metrics +- [ ] Track fallback usage + +### Rollback Plan +- [ ] Document rollback procedure +- [ ] Test rollback process +- [ ] Prepare backup deployment +- [ ] Create rollback triggers + +## ✅ Validation + +### Functional Validation +- [ ] All existing functionality works +- [ ] New backend features work correctly +- [ ] Error handling works as expected +- [ ] Performance is acceptable + +### Security Validation +- [ ] No new security vulnerabilities +- [ ] Backend isolation works correctly +- [ ] Input validation still works +- [ ] Error messages don't leak information + +### Performance Validation +- [ ] Performance meets requirements +- [ ] Memory usage is acceptable +- [ ] Scalability is maintained +- [ ] Resource utilization is optimal diff --git a/docs/archive/migration_examples/api_migration.py b/docs/archive/migration_examples/api_migration.py new file mode 100755 index 00000000..b8802139 --- /dev/null +++ b/docs/archive/migration_examples/api_migration.py @@ -0,0 +1,49 @@ +#!/usr/bin/env python3 +""" +API Migration Example + +Shows how to migrate FastAPI endpoints to use the new abstraction layer. +""" + +# BEFORE (CUDA-specific API) +# from fastapi_cuda_zk_api import ProductionCUDAZKAPI +# +# cuda_api = ProductionCUDAZKAPI() +# if not cuda_api.initialized: +# raise HTTPException(status_code=500, detail="CUDA not available") + +# AFTER (Backend-agnostic API) +from fastapi import FastAPI, HTTPException +from pydantic import BaseModel +from gpu_acceleration import GPUAccelerationManager, create_gpu_manager +import numpy as np + +app = FastAPI(title="Refactored GPU API") + +# Initialize GPU manager (auto-detects best backend) +gpu_manager = create_gpu_manager() + +class FieldOperation(BaseModel): + a: list[int] + b: list[int] + +@app.post("/field/add") +async def field_add(op: FieldOperation): + """Perform field addition with any available backend.""" + try: + a = np.array(op.a, dtype=np.uint64) + b = np.array(op.b, dtype=np.uint64) + result = gpu_manager.field_add(a, b) + return {"result": result.tolist()} + except Exception as e: + raise HTTPException(status_code=500, detail=str(e)) + +@app.get("/backend/info") +async def backend_info(): + """Get current backend information.""" + return gpu_manager.get_backend_info() + +@app.get("/performance/metrics") +async def performance_metrics(): + """Get performance metrics.""" + return gpu_manager.get_performance_metrics() diff --git a/docs/archive/migration_examples/basic_migration.py b/docs/archive/migration_examples/basic_migration.py new file mode 100755 index 00000000..3ead986e --- /dev/null +++ b/docs/archive/migration_examples/basic_migration.py @@ -0,0 +1,40 @@ +#!/usr/bin/env python3 +""" +Basic Migration Example + +Shows how to migrate from direct CUDA calls to the new abstraction layer. +""" + +# BEFORE (Direct CUDA) +# from high_performance_cuda_accelerator import HighPerformanceCUDAZKAccelerator +# +# accelerator = HighPerformanceCUDAZKAccelerator() +# if accelerator.initialized: +# result = accelerator.field_add_cuda(a, b) + +# AFTER (Abstraction Layer) +import numpy as np +from gpu_acceleration import GPUAccelerationManager, create_gpu_manager + +# Method 1: Auto-detect backend +gpu = create_gpu_manager() +gpu.initialize() + +a = np.array([1, 2, 3, 4], dtype=np.uint64) +b = np.array([5, 6, 7, 8], dtype=np.uint64) + +result = gpu.field_add(a, b) +print(f"Field addition result: {result}") + +# Method 2: Context manager (recommended) +from gpu_acceleration import GPUAccelerationContext + +with GPUAccelerationContext() as gpu: + result = gpu.field_mul(a, b) + print(f"Field multiplication result: {result}") + +# Method 3: Quick functions +from gpu_acceleration import quick_field_add + +result = quick_field_add(a, b) +print(f"Quick field addition: {result}") diff --git a/docs/archive/migration_examples/config_migration.py b/docs/archive/migration_examples/config_migration.py new file mode 100755 index 00000000..f9be2d0c --- /dev/null +++ b/docs/archive/migration_examples/config_migration.py @@ -0,0 +1,38 @@ +#!/usr/bin/env python3 +""" +Configuration Migration Example + +Shows how to migrate configuration to use the new abstraction layer. +""" + +# BEFORE (CUDA-specific config) +# cuda_config = { +# "lib_path": "./liboptimized_field_operations.so", +# "device_id": 0, +# "memory_limit": 8*1024*1024*1024 +# } + +# AFTER (Backend-agnostic config) +from gpu_acceleration import ZKOperationConfig, GPUAccelerationManager, ComputeBackend + +# Configuration for any backend +config = ZKOperationConfig( + batch_size=2048, + use_gpu=True, + fallback_to_cpu=True, + timeout=60.0, + memory_limit=8*1024*1024*1024 # 8GB +) + +# Create manager with specific backend +gpu = GPUAccelerationManager(backend=ComputeBackend.CUDA, config=config) +gpu.initialize() + +# Or auto-detect with config +from gpu_acceleration import create_gpu_manager +gpu = create_gpu_manager( + backend="cuda", # or None for auto-detect + batch_size=2048, + fallback_to_cpu=True, + timeout=60.0 +)