Implementation Plan: Mini-Inference Engine

Overview

This implementation plan breaks down the Mini-Inference Engine into progressive coding tasks, from project infrastructure to complete optimization paths.

Progress Summary

Phase Status Completion
1. Project Infrastructure ✅ Complete 100%
2-3. Naive MatMul ✅ Complete 100%
4-8. GEMM Optimization ✅ Complete 100%
9. Kernel Fusion ✅ Complete 100%
10-11. Inference Validation ✅ Complete 100%
12-13. Performance Testing ✅ Complete 100%
14-18. Engineering Enhments ✅ Complete 100%

Phase 1: Project Infrastructure

Task 1.1: CMake Build System ✅

  • Create CMakeLists.txt with CUDA compilation configuration
  • Create directories: src/, include/, tests/, benchmarks/
  • Configure Google Test dependency

Requirements: R9.1, R9.3

Task 1.2: Core Data Structures ✅

  • Implement MatrixDesc, GemmConfig, FusionConfig, PerfStats
  • Implement CUDA_CHECK macro and CudaException class
  • Implement DeviceMemory RAII wrapper class

Requirements: R9.1, R9.2, R9.3, R9.5

Task 1.3: Input Validation and Reference Implementation ✅

  • Implement validate_gemm_inputs() function
  • Implement CPU version matrix multiplication
  • Implement matrix comparison function

Requirements: R1.4, R9.4

Phase 2: Naive MatMul

Task 2.1: Naive MatMul Kernel ✅

  • Write naive_matmul CUDA kernel
  • Each thread computes one output element
  • Implement kernel launch wrapper function

Requirements: R1.1, R1.2, R1.3

Task 2.2: Performance Measurement Tools ✅

  • Use CUDA Events for timing
  • Calculate GFLOPS: 2*M*N*K / time / 1e9

Requirements: R1.5

Phase 3: Tiled GEMM

Task 3.1: Tiled GEMM Kernel ✅

  • Write tiled_gemm kernel using shared memory
  • Implement 32×32 tile blocking strategy
  • Handle boundary conditions

Requirements: R2.1, R2.2, R2.3, R2.4

Task 3.2: Performance Comparison ✅

  • Compare Naive vs Tiled performance
  • Verify ≥ 5x performance improvement

Requirements: R2.5

Phase 4: Memory Coalescing

Task 4.1: Optimize Memory Access Patterns ✅

  • Modify Tiled GEMM to ensure coalesced memory access
  • Optimize row-major loading for matrix A
  • Optimize access pattern for matrix B

Requirements: R3.1, R3.2, R3.3

Task 4.2: Performance Validation ✅

  • Verify ≥ 20% performance improvement

Requirements: R3.4

Phase 5: Double Buffering

Task 5.1: Double Buffering GEMM Kernel ✅

  • Implement two sets of shared memory buffers
  • Implement overlap between computation and data prefetching
  • Use asynchronous memory operations

Requirements: R4.1, R4.2, R4.3

Task 5.2: Performance Validation ✅

  • Verify ≥ 15% performance improvement

Requirements: R4.4

Phase 6: Register Blocking

Task 6.1: Register Blocked GEMM Kernel ✅

  • Implement templated optimized_gemm<BM, BN, BK, TM, TN>
  • Each thread computes TM×TN output block
  • Use vectorized loads (float4)
  • Avoid shared memory bank conflicts

Requirements: R5.1, R5.2, R5.3

Task 6.2: cuBLAS Performance Comparison ✅

  • Integrate cuBLAS reference
  • Verify achievement of 70%-80% of cuBLAS performance

Requirements: R5.4

Phase 7: Kernel Fusion

Task 7.1: Fusion Kernel ✅

  • Write fused_gemm_bias_relu template kernel
  • Support optional bias addition
  • Support optional ReLU activation

Requirements: R6.1, R6.2, R6.3, R6.5

Task 7.2: Fusion Performance Validation ✅

  • Compare fused vs separate kernels
  • Verify ≥ 30% time reduction

Requirements: R6.4

Phase 8: Weight Loading and Inference

Task 8.1: Weight File Format ✅

  • Define binary weight file format
  • Implement load_weights() / save_weights()

Requirements: R7.1

Task 8.2: InferenceEngine ✅

  • Implement init(), cleanup() lifecycle
  • Implement forward() multi-layer forward propagation
  • Support MNIST network architecture

Requirements: R7.2, R7.3

Phase 9: MNIST Validation

Task 9.1: Data Preparation ✅

  • Create weight export script
  • Prepare test image data

Requirements: R7.4

Task 9.2: End-to-End Testing ✅

  • Validate inference accuracy
  • Report per-layer execution time

Requirements: R7.4, R7.5

Phase 10: Performance Benchmarking

Task 10.1: Benchmark Framework ✅

  • Test matrix sizes: 256, 512, 1024, 2048, 4096
  • Report mean and standard deviation over multiple iterations
  • Generate performance comparison report

Requirements: R8.1, R8.2, R8.3, R8.5

Task 10.2: Correctness Validation ✅

  • Verify all optimized versions produce consistent results

Requirements: R8.4

Phase 11: Engineering Enhments (Complete)

Task 11.1: Logging System ✅

  • Multiple log levels
  • Console and file output
  • Colored output

Task 11.2: Configuration Management ✅

  • Load configuration from file
  • Support environment variables
  • GEMM preset configurations

Task 11.3: GPU Memory Pool ✅

  • Cached allocation
  • Thread safety
  • Statistics

Task 11.4: Tensor Class ✅

  • GPU storage
  • Shape management
  • Mathematical operations

Task 11.5: Advanced Features ✅

  • Vectorized GEMM
  • Half-precision GEMM
  • Profiler
  • Auto-tuner
  • Stream manager
  • Batched GEMM
  • INT8 quantization

Notes

  • Tasks marked ✅ are complete
  • Each task references specific requirements for traceability
  • Property testing tasks are optional and can be skipped to accelerate development

Next Steps

See Roadmap for future planned features.

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