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wifi-densepose/examples/scipix/docs/09_OPTIMIZATION.md
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# OCR System Optimization Roadmap
## Executive Summary
This document outlines a comprehensive optimization strategy for the ruvector-scipix OCR system, targeting progressive performance improvements from baseline (1000ms/image) to production-ready (50ms/image) latency.
**Target Performance Metrics:**
- **Phase 1 (Baseline)**: 1000ms/image, 80% CPU utilization
- **Phase 2 (Optimized)**: 100ms/image, 60% CPU utilization, 10x throughput improvement
- **Phase 3 (Production)**: 50ms/image, 40% CPU utilization, 20x throughput improvement
---
## 1. Model Optimization
### 1.1 ONNX Model Quantization
**Objective**: Reduce model size and inference time while maintaining accuracy.
#### FP16 (Half-Precision) Quantization
```rust
// Expected Improvement: 2x speed, 50% memory reduction, <1% accuracy loss
use ort::quantization::{QuantizationConfig, QuantizationType};
pub struct ModelOptimizer {
quantization_config: QuantizationConfig,
}
impl ModelOptimizer {
pub fn quantize_fp16(model_path: &str) -> Result<String> {
let config = QuantizationConfig::new()
.with_quantization_type(QuantizationType::FP16)
.with_per_channel(true)
.with_reduce_range(false);
let output_path = model_path.replace(".onnx", "_fp16.onnx");
ort::quantization::quantize(model_path, &output_path, config)?;
Ok(output_path)
}
}
```
**Expected Results:**
- Model size: 500MB → 250MB (50% reduction)
- Inference time: 1000ms → 500ms (2x speedup)
- Accuracy degradation: <1%
- Memory usage: 50% reduction
#### INT8 Quantization
```rust
// Expected Improvement: 4x speed, 75% memory reduction, 2-5% accuracy loss
pub fn quantize_int8_dynamic(model_path: &str) -> Result<String> {
let config = QuantizationConfig::new()
.with_quantization_type(QuantizationType::DynamicINT8)
.with_per_channel(true)
.with_optimize_model(true);
let output_path = model_path.replace(".onnx", "_int8.onnx");
ort::quantization::quantize(model_path, &output_path, config)?;
Ok(output_path)
}
pub fn quantize_int8_static(
model_path: &str,
calibration_dataset: &[Tensor],
) -> Result<String> {
let config = QuantizationConfig::new()
.with_quantization_type(QuantizationType::StaticINT8)
.with_calibration_method(CalibrationMethod::MinMax)
.with_per_channel(true);
let output_path = model_path.replace(".onnx", "_int8_static.onnx");
// Calibrate using representative dataset
let calibrator = Calibrator::new(config, calibration_dataset);
calibrator.quantize(model_path, &output_path)?;
Ok(output_path)
}
```
**Expected Results:**
- Model size: 500MB → 125MB (75% reduction)
- Inference time: 1000ms → 250ms (4x speedup)
- Accuracy degradation: 2-5%
- Memory usage: 75% reduction
### 1.2 Model Pruning Strategies
**Objective**: Remove redundant weights and connections to reduce model complexity.
```rust
// Expected Improvement: 30-50% parameter reduction, 2-3x speed
pub struct ModelPruner {
sparsity_target: f32,
pruning_method: PruningMethod,
}
pub enum PruningMethod {
MagnitudeBased, // Remove smallest weights
StructuredPruning, // Remove entire neurons/filters
GradientBased, // Remove low-gradient weights
}
impl ModelPruner {
pub fn prune_magnitude_based(&self, model: &Model, threshold: f32) -> Model {
// 1. Analyze weight magnitudes
let weight_analysis = self.analyze_weight_importance(model);
// 2. Apply sparsity threshold
let pruned_weights = weight_analysis
.iter()
.map(|(layer, weights)| {
weights.iter().map(|w| {
if w.abs() < threshold { 0.0 } else { *w }
}).collect()
})
.collect();
// 3. Reconstruct model
self.rebuild_model(model, pruned_weights)
}
pub fn structured_pruning(&self, model: &Model, prune_ratio: f32) -> Model {
// Remove entire filter channels based on importance scores
let channel_importance = self.compute_channel_importance(model);
// Sort and prune least important channels
let channels_to_prune = self.select_channels_to_prune(
channel_importance,
prune_ratio
);
self.remove_channels(model, channels_to_prune)
}
}
```
**Expected Results:**
- Parameters: 200M → 100M (50% reduction)
- Inference time: 1000ms → 400ms (2.5x speedup)
- Accuracy degradation: 3-7%
- Fine-tuning required: Yes (10-20 epochs)
### 1.3 Knowledge Distillation
**Objective**: Train a smaller student model to match larger teacher model performance.
```rust
// Expected Improvement: 5-10x speed, 80-90% size reduction, <5% accuracy loss
pub struct KnowledgeDistiller {
teacher_model: Arc<Model>,
student_model: Arc<Model>,
temperature: f32,
alpha: f32, // Balance between hard and soft targets
}
impl KnowledgeDistiller {
pub async fn distill(&self, training_data: DataLoader) -> Result<Model> {
let mut student = self.student_model.clone();
for batch in training_data {
// Get teacher predictions (soft targets)
let teacher_output = self.teacher_model
.forward(&batch.images)
.await?
.apply_temperature(self.temperature);
// Get student predictions
let student_output = student.forward(&batch.images).await?;
// Compute distillation loss
let soft_loss = kl_divergence(
&student_output.apply_temperature(self.temperature),
&teacher_output
);
let hard_loss = cross_entropy(
&student_output,
&batch.labels
);
let loss = self.alpha * soft_loss + (1.0 - self.alpha) * hard_loss;
// Backpropagation and optimization
loss.backward();
student.optimize();
}
Ok(student)
}
}
// Example architecture reduction
pub fn create_distilled_model() -> StudentModel {
StudentModel::new()
.with_encoder_layers(6) // vs 12 in teacher
.with_hidden_size(384) // vs 768 in teacher
.with_attention_heads(6) // vs 12 in teacher
.with_intermediate_size(1536) // vs 3072 in teacher
}
```
**Expected Results:**
- Model size: 500MB → 50MB (10x reduction)
- Parameters: 200M → 20M (10x reduction)
- Inference time: 1000ms → 100ms (10x speedup)
- Accuracy degradation: 3-5%
### 1.4 TensorRT/OpenVINO Integration
**Objective**: Leverage hardware-specific optimizations for maximum performance.
#### TensorRT Integration (NVIDIA GPUs)
```rust
// Expected Improvement: 3-5x speed on NVIDIA GPUs
use tensorrt_rs::{Builder, NetworkDefinition, IOptimizationProfile};
pub struct TensorRTOptimizer {
builder: Builder,
precision: Precision,
}
pub enum Precision {
FP32,
FP16,
INT8,
}
impl TensorRTOptimizer {
pub fn optimize_for_tensorrt(&self, onnx_path: &str) -> Result<Vec<u8>> {
// 1. Create TensorRT builder
let network = self.builder
.create_network_from_onnx(onnx_path)?;
// 2. Configure optimization profile
let profile = self.builder
.create_optimization_profile()
.set_shape("input",
Dims::new(&[1, 3, 224, 224]), // min
Dims::new(&[4, 3, 224, 224]), // opt
Dims::new(&[16, 3, 224, 224]) // max
);
// 3. Build optimized engine
let config = self.builder.create_builder_config()
.set_max_workspace_size(1 << 30) // 1GB
.set_flag(BuilderFlag::FP16, self.precision == Precision::FP16)
.set_flag(BuilderFlag::INT8, self.precision == Precision::INT8)
.add_optimization_profile(profile);
let engine = self.builder.build_engine(&network, &config)?;
// 4. Serialize engine
Ok(engine.serialize())
}
}
```
**Expected Results (NVIDIA GPUs):**
- Inference time: 1000ms → 200ms (5x speedup)
- GPU utilization: 40% → 85%
- Memory bandwidth: Optimized kernel fusion
- Dynamic shape support: Yes
#### OpenVINO Integration (Intel CPUs/GPUs)
```rust
// Expected Improvement: 2-4x speed on Intel hardware
use openvino_rs::{Core, CompiledModel, InferRequest};
pub struct OpenVINOOptimizer {
core: Core,
device: String, // CPU, GPU, MYRIAD, etc.
}
impl OpenVINOOptimizer {
pub fn optimize_for_openvino(&self, onnx_path: &str) -> Result<CompiledModel> {
// 1. Read model
let model = self.core.read_model(onnx_path, None)?;
// 2. Configure optimization
let mut config = HashMap::new();
config.insert("PERFORMANCE_HINT", "THROUGHPUT");
config.insert("NUM_STREAMS", "AUTO");
config.insert("INFERENCE_PRECISION_HINT", "f16");
// 3. Compile for specific device
let compiled_model = self.core.compile_model(
&model,
&self.device,
&config
)?;
Ok(compiled_model)
}
pub async fn infer_optimized(&self,
compiled_model: &CompiledModel,
input: &Tensor
) -> Result<Tensor> {
let infer_request = compiled_model.create_infer_request()?;
// Set input tensor
infer_request.set_input_tensor(0, input)?;
// Asynchronous inference
infer_request.start_async()?;
infer_request.wait()?;
// Get output tensor
Ok(infer_request.get_output_tensor(0)?)
}
}
```
**Expected Results (Intel Hardware):**
- Inference time (CPU): 1000ms → 300ms (3.3x speedup)
- Inference time (GPU): 1000ms → 250ms (4x speedup)
- AVX-512 utilization: Automatic
- Multi-stream execution: Auto-tuned
---
## 2. Inference Optimization
### 2.1 Batch Processing for Throughput
**Objective**: Process multiple images simultaneously to maximize GPU/CPU utilization.
```rust
// Expected Improvement: 3-5x throughput with batch size 16-32
use tokio::sync::mpsc;
use rayon::prelude::*;
pub struct BatchProcessor {
batch_size: usize,
timeout_ms: u64,
inference_engine: Arc<InferenceEngine>,
}
impl BatchProcessor {
pub async fn process_with_batching(
&self,
input_stream: mpsc::Receiver<ImageRequest>
) -> mpsc::Receiver<OCRResult> {
let (tx, rx) = mpsc::channel(1000);
tokio::spawn(async move {
let mut batch_buffer = Vec::with_capacity(self.batch_size);
let mut timeout = tokio::time::interval(
Duration::from_millis(self.timeout_ms)
);
loop {
tokio::select! {
Some(request) = input_stream.recv() => {
batch_buffer.push(request);
if batch_buffer.len() >= self.batch_size {
self.process_batch(&batch_buffer, &tx).await;
batch_buffer.clear();
}
}
_ = timeout.tick() => {
if !batch_buffer.is_empty() {
self.process_batch(&batch_buffer, &tx).await;
batch_buffer.clear();
}
}
}
}
});
rx
}
async fn process_batch(
&self,
batch: &[ImageRequest],
tx: &mpsc::Sender<OCRResult>
) {
// 1. Preprocess in parallel
let preprocessed: Vec<Tensor> = batch
.par_iter()
.map(|req| self.preprocess(&req.image))
.collect();
// 2. Stack into single tensor
let batched_tensor = Tensor::stack(&preprocessed, 0);
// 3. Single inference call
let results = self.inference_engine
.infer(&batched_tensor)
.await
.unwrap();
// 4. Split and send results
for (request, result) in batch.iter().zip(results.split(0)) {
let ocr_result = self.postprocess(result);
tx.send(ocr_result).await.unwrap();
}
}
}
```
**Expected Results:**
- Throughput: 1 img/s → 15-20 img/s (batch size 16)
- Latency (p50): 1000ms → 150ms
- Latency (p99): 1000ms → 400ms (due to batching delay)
- GPU utilization: 40% → 90%
### 2.2 Model Caching and Warm-up
**Objective**: Eliminate cold-start latency and optimize model loading.
```rust
// Expected Improvement: First inference 5000ms → 100ms
pub struct ModelCache {
models: Arc<RwLock<LruCache<ModelKey, Arc<CompiledModel>>>>,
warm_up_batches: usize,
}
impl ModelCache {
pub async fn get_or_load_model(
&self,
model_key: ModelKey
) -> Result<Arc<CompiledModel>> {
// Try to get from cache
{
let cache = self.models.read().await;
if let Some(model) = cache.get(&model_key) {
return Ok(model.clone());
}
}
// Load and warm up model
let model = self.load_and_warmup(&model_key).await?;
let model = Arc::new(model);
// Cache for future use
{
let mut cache = self.models.write().await;
cache.put(model_key, model.clone());
}
Ok(model)
}
async fn load_and_warmup(&self, model_key: &ModelKey) -> Result<CompiledModel> {
// 1. Load model
let model = self.load_model(model_key).await?;
// 2. Warm-up with dummy inputs
let dummy_input = Tensor::zeros(&[1, 3, 224, 224]);
for _ in 0..self.warm_up_batches {
let _ = model.infer(&dummy_input).await?;
}
// 3. Model is now optimized in GPU memory
Ok(model)
}
pub async fn preload_models(&self, model_keys: &[ModelKey]) {
// Parallel model loading at startup
futures::future::join_all(
model_keys.iter().map(|key| self.get_or_load_model(key.clone()))
).await;
}
}
```
**Expected Results:**
- First inference: 5000ms → 100ms (50x improvement)
- Model loading: Asynchronous, non-blocking
- Memory usage: +500MB per cached model
- Cache hit rate: 95%+ in production
### 2.3 Dynamic Batching
**Objective**: Adaptively adjust batch size based on load and latency requirements.
```rust
// Expected Improvement: Optimal throughput/latency trade-off
pub struct DynamicBatcher {
min_batch_size: usize,
max_batch_size: usize,
target_latency_ms: u64,
adaptive_controller: AdaptiveController,
}
struct AdaptiveController {
current_batch_size: AtomicUsize,
latency_history: RwLock<VecDeque<Duration>>,
throughput_history: RwLock<VecDeque<f64>>,
}
impl DynamicBatcher {
pub async fn process_adaptive(
&self,
input_stream: mpsc::Receiver<ImageRequest>
) -> mpsc::Receiver<OCRResult> {
let (tx, rx) = mpsc::channel(1000);
tokio::spawn(async move {
loop {
// Determine optimal batch size
let batch_size = self.adaptive_controller
.compute_optimal_batch_size();
// Collect batch
let batch = self.collect_batch(
&input_stream,
batch_size
).await;
// Process and measure
let start = Instant::now();
self.process_batch(&batch, &tx).await;
let latency = start.elapsed();
// Update controller
self.adaptive_controller.update(
batch_size,
latency,
batch.len()
);
}
});
rx
}
}
impl AdaptiveController {
fn compute_optimal_batch_size(&self) -> usize {
let current = self.current_batch_size.load(Ordering::Relaxed);
let avg_latency = self.average_latency();
let avg_throughput = self.average_throughput();
// Gradient-based optimization
if avg_latency < self.target_latency_ms && avg_throughput.is_increasing() {
// Increase batch size
(current + 2).min(self.max_batch_size)
} else if avg_latency > self.target_latency_ms {
// Decrease batch size
(current.saturating_sub(2)).max(self.min_batch_size)
} else {
current
}
}
}
```
**Expected Results:**
- Batch size adaptation: 1-32 based on load
- Latency (low load): 100ms (batch size 1-4)
- Latency (high load): 200ms (batch size 16-32)
- Throughput optimization: Automatic
- SLA compliance: 99%+
### 2.4 Speculative Decoding
**Objective**: Accelerate autoregressive decoding for text generation tasks.
```rust
// Expected Improvement: 2-3x speed for LaTeX generation
pub struct SpeculativeDecoder {
draft_model: Arc<SmallModel>, // Fast, less accurate
target_model: Arc<LargeModel>, // Slow, accurate
num_speculative_tokens: usize,
}
impl SpeculativeDecoder {
pub async fn decode(&self, prompt: &Tensor) -> Result<String> {
let mut output_tokens = Vec::new();
let mut current_input = prompt.clone();
loop {
// 1. Draft model generates K tokens quickly
let draft_tokens = self.draft_model
.generate_n_tokens(&current_input, self.num_speculative_tokens)
.await?;
// 2. Target model verifies all K tokens in parallel
let verification_input = Tensor::concat(&[
current_input.clone(),
draft_tokens.clone()
], 0);
let target_logits = self.target_model
.forward(&verification_input)
.await?;
// 3. Accept tokens that match target model's top prediction
let mut accepted = 0;
for (i, draft_token) in draft_tokens.iter().enumerate() {
let target_prediction = target_logits[i].argmax();
if *draft_token == target_prediction {
output_tokens.push(*draft_token);
accepted += 1;
} else {
// Use target model's prediction and restart
output_tokens.push(target_prediction);
break;
}
}
// 4. Update input for next iteration
current_input = Tensor::from_slice(&output_tokens);
if self.is_complete(&output_tokens) {
break;
}
}
Ok(self.decode_tokens(&output_tokens))
}
}
```
**Expected Results:**
- LaTeX generation: 2000ms → 700ms (2.8x speedup)
- Acceptance rate: 60-80% of draft tokens
- Quality: Identical to target model
- Best for: Long-form LaTeX, chemical formulas
---
## 3. Memory Optimization
### 3.1 Memory-Mapped Model Loading
**Objective**: Reduce memory footprint and enable instant model loading.
```rust
// Expected Improvement: 90% memory reduction, instant loading
use memmap2::MmapOptions;
use std::fs::File;
pub struct MemoryMappedModel {
mmap: Mmap,
metadata: ModelMetadata,
}
impl MemoryMappedModel {
pub fn load(model_path: &str) -> Result<Self> {
// 1. Open file
let file = File::open(model_path)?;
// 2. Create memory-mapped region
let mmap = unsafe {
MmapOptions::new()
.populate() // Pre-fault pages
.map(&file)?
};
// 3. Parse metadata from header
let metadata = ModelMetadata::parse(&mmap[0..4096])?;
Ok(Self { mmap, metadata })
}
pub fn get_tensor(&self, layer_name: &str) -> Result<TensorView> {
let offset = self.metadata.tensor_offsets.get(layer_name)
.ok_or(Error::TensorNotFound)?;
let size = self.metadata.tensor_sizes.get(layer_name)?;
// Zero-copy tensor view
Ok(TensorView::from_bytes(
&self.mmap[offset.start..offset.end],
size
))
}
pub async fn infer(&self, input: &Tensor) -> Result<Tensor> {
// Inference operates directly on memory-mapped data
// No copying required
self.run_inference_on_mmap(input).await
}
}
```
**Expected Results:**
- Model loading time: 2000ms → 10ms (200x improvement)
- Memory usage: 500MB RAM → 50MB RAM (model stays on disk)
- Page faults: Minimal with `populate()` flag
- Shared memory: Multiple processes share same model
### 3.2 Tensor Arena Allocation
**Objective**: Pre-allocate fixed memory pools to eliminate runtime allocation overhead.
```rust
// Expected Improvement: 30% reduction in memory fragmentation
pub struct TensorArena {
memory_pool: Vec<u8>,
allocator: BumpAllocator,
checkpoints: Vec<usize>,
}
impl TensorArena {
pub fn new(size_bytes: usize) -> Self {
Self {
memory_pool: vec![0u8; size_bytes],
allocator: BumpAllocator::new(size_bytes),
checkpoints: Vec::new(),
}
}
pub fn allocate_tensor(&mut self, shape: &[usize], dtype: DType) -> TensorMut {
let size_bytes = shape.iter().product::<usize>() * dtype.size_bytes();
let offset = self.allocator.allocate(size_bytes)
.expect("Arena out of memory");
let slice = &mut self.memory_pool[offset..offset + size_bytes];
TensorMut::from_slice_mut(slice, shape, dtype)
}
pub fn checkpoint(&mut self) {
// Save current allocation position
self.checkpoints.push(self.allocator.position());
}
pub fn restore(&mut self) {
// Restore to previous checkpoint (free all allocations since)
if let Some(position) = self.checkpoints.pop() {
self.allocator.reset_to(position);
}
}
pub fn reset(&mut self) {
// Reset entire arena
self.allocator.reset();
self.checkpoints.clear();
}
}
// Usage in inference pipeline
impl InferenceEngine {
pub async fn infer_with_arena(&self, input: &Tensor) -> Result<Tensor> {
let mut arena = TensorArena::new(100 * 1024 * 1024); // 100MB
arena.checkpoint();
// All intermediate tensors allocated from arena
let preprocessed = self.preprocess_to_arena(input, &mut arena);
let features = self.extract_features_to_arena(&preprocessed, &mut arena);
let output = self.decode_to_arena(&features, &mut arena);
// Clone final output (arena will be freed)
let result = output.to_owned();
arena.restore(); // Free all intermediate allocations
Ok(result)
}
}
```
**Expected Results:**
- Memory allocations: 1000+ calls → 1 allocation
- Allocation time: 50ms → 1ms (50x improvement)
- Memory fragmentation: Eliminated
- Cache locality: Improved
### 3.3 Zero-Copy Image Processing
**Objective**: Eliminate unnecessary data copies in preprocessing pipeline.
```rust
// Expected Improvement: 40% reduction in preprocessing time
use image::DynamicImage;
use ndarray::ArrayView3;
pub struct ZeroCopyPreprocessor {
target_size: (usize, usize),
normalization: NormalizationParams,
}
impl ZeroCopyPreprocessor {
pub fn preprocess_inplace(&self, image: &DynamicImage) -> TensorView {
// 1. Get raw pixel data (no copy)
let rgb_image = image.to_rgb8();
let raw_pixels = rgb_image.as_raw();
// 2. Create tensor view over raw data
let tensor_view = unsafe {
TensorView::from_raw_parts(
raw_pixels.as_ptr() as *const f32,
&[1, 3, image.height() as usize, image.width() as usize]
)
};
// 3. Apply transformations in-place
let resized = self.resize_inplace(tensor_view, self.target_size);
let normalized = self.normalize_inplace(resized, &self.normalization);
normalized
}
fn resize_inplace(&self, input: TensorView, target_size: (usize, usize)) -> TensorView {
// Use SIMD-accelerated resize operations
// Operating directly on input buffer when possible
simd_resize::resize_rgb_inplace(input, target_size)
}
pub fn batch_preprocess_zero_copy(
&self,
images: &[DynamicImage]
) -> Vec<TensorView> {
images
.par_iter()
.map(|img| self.preprocess_inplace(img))
.collect()
}
}
// SIMD-accelerated normalization
#[cfg(target_arch = "x86_64")]
use std::arch::x86_64::*;
pub fn normalize_simd(data: &mut [f32], mean: [f32; 3], std: [f32; 3]) {
unsafe {
let mean_vec = _mm_set_ps(0.0, mean[2], mean[1], mean[0]);
let std_vec = _mm_set_ps(1.0, std[2], std[1], std[0]);
for chunk in data.chunks_exact_mut(4) {
let values = _mm_loadu_ps(chunk.as_ptr());
let normalized = _mm_div_ps(
_mm_sub_ps(values, mean_vec),
std_vec
);
_mm_storeu_ps(chunk.as_mut_ptr(), normalized);
}
}
}
```
**Expected Results:**
- Preprocessing time: 100ms → 60ms (40% improvement)
- Memory copies: 3 copies → 0 copies
- Memory bandwidth: 50% reduction
- SIMD utilization: 90%+
### 3.4 Streaming for Large Documents
**Objective**: Process multi-page documents without loading entire document into memory.
```rust
// Expected Improvement: Process unlimited document sizes with constant memory
use tokio::io::{AsyncRead, AsyncReadExt};
use futures::stream::{Stream, StreamExt};
pub struct StreamingOCRProcessor {
page_buffer_size: usize,
max_concurrent_pages: usize,
inference_engine: Arc<InferenceEngine>,
}
impl StreamingOCRProcessor {
pub async fn process_document_stream<R: AsyncRead + Unpin>(
&self,
pdf_stream: R
) -> impl Stream<Item = Result<PageResult>> {
// 1. Create page stream
let page_stream = self.extract_pages_streaming(pdf_stream);
// 2. Process with bounded concurrency
page_stream
.map(|page_result| async move {
let page = page_result?;
// Preprocess page
let preprocessed = self.preprocess_page(&page).await?;
// Run OCR
let ocr_result = self.inference_engine
.infer(&preprocessed)
.await?;
// Free page immediately
drop(page);
drop(preprocessed);
Ok(PageResult {
page_num: page.page_num,
text: ocr_result,
})
})
.buffer_unordered(self.max_concurrent_pages)
}
async fn extract_pages_streaming<R: AsyncRead + Unpin>(
&self,
mut pdf_stream: R
) -> impl Stream<Item = Result<Page>> {
futures::stream::unfold(
(pdf_stream, 0usize),
move |(mut stream, page_num)| async move {
// Read next page from stream
let mut page_buffer = vec![0u8; self.page_buffer_size];
match stream.read(&mut page_buffer).await {
Ok(0) => None, // End of stream
Ok(n) => {
let page = self.decode_page(&page_buffer[..n], page_num).ok()?;
Some((Ok(page), (stream, page_num + 1)))
}
Err(e) => Some((Err(e.into()), (stream, page_num)))
}
}
)
}
pub async fn process_large_pdf(&self, pdf_path: &str) -> Result<Vec<PageResult>> {
let file = tokio::fs::File::open(pdf_path).await?;
let stream = self.process_document_stream(file);
stream.collect().await
}
}
```
**Expected Results:**
- Memory usage: O(n) → O(1) (constant)
- Max document size: Unlimited (was limited by RAM)
- Concurrent page processing: 4-8 pages
- Throughput: 5-10 pages/second
---
## 4. Parallelization Strategy
### 4.1 Rayon for CPU Parallelism
**Objective**: Maximize CPU core utilization for data-parallel operations.
```rust
// Expected Improvement: Near-linear scaling with CPU cores
use rayon::prelude::*;
pub struct ParallelPreprocessor {
thread_pool: rayon::ThreadPool,
}
impl ParallelPreprocessor {
pub fn new(num_threads: usize) -> Self {
let thread_pool = rayon::ThreadPoolBuilder::new()
.num_threads(num_threads)
.build()
.unwrap();
Self { thread_pool }
}
pub fn batch_preprocess(&self, images: &[DynamicImage]) -> Vec<Tensor> {
self.thread_pool.install(|| {
images
.par_iter()
.map(|img| {
// Each image processed on separate thread
self.preprocess_single(img)
})
.collect()
})
}
pub fn parallel_postprocess(&self, outputs: &[Tensor]) -> Vec<OCRResult> {
outputs
.par_iter()
.map(|output| {
// Parallel decoding, NMS, text extraction
self.decode_output(output)
})
.collect()
}
}
// Nested parallelism for complex operations
pub fn parallel_nms(boxes: &[BoundingBox], threshold: f32) -> Vec<BoundingBox> {
boxes
.par_chunks(1000)
.flat_map(|chunk| {
// Each chunk processed independently
nms_sequential(chunk, threshold)
})
.collect()
}
```
**Expected Results (8-core CPU):**
- Preprocessing throughput: 1 img/s → 7-8 img/s (7-8x)
- CPU utilization: 12% → 95%
- Scaling efficiency: 90%+ up to 16 cores
- Memory overhead: Minimal
### 4.2 Tokio for Async I/O
**Objective**: Overlap I/O operations with computation for maximum throughput.
```rust
// Expected Improvement: 3-5x throughput with I/O-bound operations
use tokio::sync::Semaphore;
use futures::stream::{FuturesUnordered, StreamExt};
pub struct AsyncOCRService {
inference_semaphore: Arc<Semaphore>,
io_semaphore: Arc<Semaphore>,
model: Arc<InferenceEngine>,
}
impl AsyncOCRService {
pub async fn process_batch_async(
&self,
image_urls: Vec<String>
) -> Vec<Result<OCRResult>> {
let mut futures = FuturesUnordered::new();
for url in image_urls {
let model = self.model.clone();
let inference_sem = self.inference_semaphore.clone();
let io_sem = self.io_semaphore.clone();
futures.push(async move {
// 1. Download image (I/O bound)
let _io_permit = io_sem.acquire().await?;
let image_data = Self::download_image(&url).await?;
drop(_io_permit);
// 2. Preprocess (CPU bound)
let preprocessed = Self::preprocess(&image_data)?;
// 3. Inference (GPU/CPU bound)
let _inference_permit = inference_sem.acquire().await?;
let result = model.infer(&preprocessed).await?;
drop(_inference_permit);
// 4. Postprocess (CPU bound)
Ok(Self::postprocess(result))
});
}
futures.collect().await
}
async fn download_image(url: &str) -> Result<Vec<u8>> {
let response = reqwest::get(url).await?;
Ok(response.bytes().await?.to_vec())
}
}
// Pipeline with async/await
pub struct AsyncPipeline {
stages: Vec<Box<dyn AsyncStage>>,
}
impl AsyncPipeline {
pub async fn execute(&self, input: Input) -> Result<Output> {
let mut current = input;
for stage in &self.stages {
current = stage.process(current).await?;
}
Ok(current)
}
pub async fn execute_batch(&self, inputs: Vec<Input>) -> Vec<Result<Output>> {
futures::future::join_all(
inputs.into_iter().map(|input| self.execute(input))
).await
}
}
```
**Expected Results:**
- Throughput (I/O bound): 5 img/s → 20 img/s (4x)
- Concurrent operations: 50-100 in-flight requests
- Resource utilization: Balanced I/O and compute
- Latency (p50): Unchanged
### 4.3 Pipeline Parallelism
**Objective**: Overlap different pipeline stages for continuous processing.
```rust
// Expected Improvement: 2-3x throughput with 4-stage pipeline
use tokio::sync::mpsc;
pub struct PipelineProcessor {
decode_workers: usize,
preprocess_workers: usize,
inference_workers: usize,
postprocess_workers: usize,
}
impl PipelineProcessor {
pub async fn start_pipeline(
&self,
input_rx: mpsc::Receiver<Vec<u8>>
) -> mpsc::Receiver<OCRResult> {
// Create channels for each stage
let (decode_tx, decode_rx) = mpsc::channel(100);
let (preprocess_tx, preprocess_rx) = mpsc::channel(100);
let (inference_tx, inference_rx) = mpsc::channel(100);
let (postprocess_tx, postprocess_rx) = mpsc::channel(100);
// Stage 1: Image decoding
for _ in 0..self.decode_workers {
let mut rx = input_rx.clone();
let tx = decode_tx.clone();
tokio::spawn(async move {
while let Some(image_bytes) = rx.recv().await {
let decoded = image::load_from_memory(&image_bytes).unwrap();
tx.send(decoded).await.unwrap();
}
});
}
// Stage 2: Preprocessing
for _ in 0..self.preprocess_workers {
let mut rx = decode_rx.clone();
let tx = preprocess_tx.clone();
tokio::spawn(async move {
while let Some(image) = rx.recv().await {
let preprocessed = preprocess_image(&image);
tx.send(preprocessed).await.unwrap();
}
});
}
// Stage 3: Inference (GPU bottleneck)
for _ in 0..self.inference_workers {
let mut rx = preprocess_rx.clone();
let tx = inference_tx.clone();
let model = self.model.clone();
tokio::spawn(async move {
while let Some(tensor) = rx.recv().await {
let output = model.infer(&tensor).await.unwrap();
tx.send(output).await.unwrap();
}
});
}
// Stage 4: Postprocessing
for _ in 0..self.postprocess_workers {
let mut rx = inference_rx.clone();
let tx = postprocess_tx.clone();
tokio::spawn(async move {
while let Some(output) = rx.recv().await {
let result = postprocess_output(&output);
tx.send(result).await.unwrap();
}
});
}
postprocess_rx
}
}
```
**Pipeline Configuration:**
```
Decode (4 workers) → Preprocess (4 workers) → Inference (2 workers) → Postprocess (4 workers)
20ms/img 30ms/img 100ms/img 20ms/img
```
**Expected Results:**
- Throughput: Limited by slowest stage (inference: 10 img/s with 2 workers)
- Latency: 170ms (sum of all stages)
- CPU utilization: 80-90% (balanced across stages)
- GPU utilization: 90%+
### 4.4 GPU Batch Scheduling
**Objective**: Optimize GPU utilization with intelligent batch scheduling.
```rust
// Expected Improvement: 40% better GPU utilization
pub struct GPUBatchScheduler {
gpu_memory_limit: usize,
max_batch_size: usize,
scheduler: Arc<Mutex<Scheduler>>,
}
struct Scheduler {
pending_queue: VecDeque<InferenceRequest>,
current_gpu_memory: usize,
}
impl GPUBatchScheduler {
pub async fn schedule_batch(&self) -> Option<Vec<InferenceRequest>> {
let mut scheduler = self.scheduler.lock().await;
let mut batch = Vec::new();
let mut batch_memory = 0;
while let Some(request) = scheduler.pending_queue.front() {
let request_memory = self.estimate_memory(request);
// Check constraints
if batch.len() >= self.max_batch_size {
break;
}
if batch_memory + request_memory > self.gpu_memory_limit {
break;
}
// Add to batch
let request = scheduler.pending_queue.pop_front().unwrap();
batch_memory += request_memory;
batch.push(request);
}
if batch.is_empty() {
None
} else {
scheduler.current_gpu_memory += batch_memory;
Some(batch)
}
}
pub async fn execute_with_scheduling(&self) {
loop {
if let Some(batch) = self.schedule_batch().await {
let batch_memory = batch.iter()
.map(|r| self.estimate_memory(r))
.sum();
// Execute batch
self.execute_batch(batch).await;
// Free GPU memory
let mut scheduler = self.scheduler.lock().await;
scheduler.current_gpu_memory -= batch_memory;
} else {
tokio::time::sleep(Duration::from_millis(10)).await;
}
}
}
fn estimate_memory(&self, request: &InferenceRequest) -> usize {
// Estimate GPU memory for this request
let input_size = request.input_shape.iter().product::<usize>();
let activation_size = input_size * 4; // Rough estimate
(input_size + activation_size) * std::mem::size_of::<f32>()
}
}
```
**Expected Results:**
- GPU utilization: 60% → 85% (40% improvement)
- Memory efficiency: 70% → 95%
- Batch size variance: Reduced
- OOM errors: Eliminated
---
## 5. Caching Strategy
### 5.1 LRU Cache for Repeated Queries
**Objective**: Cache OCR results for frequently accessed images.
```rust
// Expected Improvement: 100% speedup on cache hits (0.1ms vs 100ms)
use lru::LruCache;
use std::hash::{Hash, Hasher};
use sha2::{Sha256, Digest};
pub struct OCRCache {
cache: Arc<Mutex<LruCache<ImageHash, CachedResult>>>,
ttl: Duration,
}
#[derive(Clone, Hash, Eq, PartialEq)]
struct ImageHash([u8; 32]);
struct CachedResult {
result: OCRResult,
timestamp: Instant,
}
impl OCRCache {
pub fn new(capacity: usize, ttl: Duration) -> Self {
Self {
cache: Arc::new(Mutex::new(LruCache::new(capacity))),
ttl,
}
}
pub async fn get_or_compute<F>(
&self,
image: &DynamicImage,
compute_fn: F
) -> Result<OCRResult>
where
F: FnOnce(&DynamicImage) -> Result<OCRResult>
{
// 1. Compute image hash
let hash = self.hash_image(image);
// 2. Check cache
{
let mut cache = self.cache.lock().await;
if let Some(cached) = cache.get(&hash) {
// Check if still valid
if cached.timestamp.elapsed() < self.ttl {
return Ok(cached.result.clone());
}
}
}
// 3. Compute result
let result = compute_fn(image)?;
// 4. Store in cache
{
let mut cache = self.cache.lock().await;
cache.put(hash, CachedResult {
result: result.clone(),
timestamp: Instant::now(),
});
}
Ok(result)
}
fn hash_image(&self, image: &DynamicImage) -> ImageHash {
let mut hasher = Sha256::new();
hasher.update(image.as_bytes());
ImageHash(hasher.finalize().into())
}
pub async fn warm_cache(&self, common_images: Vec<(DynamicImage, OCRResult)>) {
let mut cache = self.cache.lock().await;
for (image, result) in common_images {
let hash = self.hash_image(&image);
cache.put(hash, CachedResult {
result,
timestamp: Instant::now(),
});
}
}
}
```
**Expected Results:**
- Cache hit latency: 0.1ms (1000x speedup)
- Cache hit rate: 30-40% in production
- Memory overhead: ~100MB for 1000 cached results
- TTL: 1 hour (configurable)
### 5.2 Vector Embedding Cache (ruvector-core)
**Objective**: Cache embeddings for semantic search and deduplication.
```rust
// Expected Improvement: 95% faster similarity search
use ruvector_core::VectorDB;
pub struct EmbeddingCache {
vector_db: VectorDB,
embedding_model: Arc<EmbeddingModel>,
}
impl EmbeddingCache {
pub async fn get_or_compute_embedding(
&self,
text: &str
) -> Result<Vec<f32>> {
// 1. Search for existing embedding
let query_hash = self.hash_text(text);
if let Some(cached) = self.vector_db.get_by_id(&query_hash)? {
return Ok(cached.vector);
}
// 2. Compute new embedding
let embedding = self.embedding_model.encode(text).await?;
// 3. Store in vector DB
self.vector_db.insert(
query_hash,
embedding.clone(),
HashMap::from([
("text".to_string(), text.to_string()),
("timestamp".to_string(), Utc::now().to_rfc3339()),
])
)?;
Ok(embedding)
}
pub async fn find_similar_results(
&self,
text: &str,
top_k: usize
) -> Result<Vec<OCRResult>> {
// 1. Get embedding
let embedding = self.get_or_compute_embedding(text).await?;
// 2. Search vector DB
let similar = self.vector_db.search(&embedding, top_k)?;
// 3. Return cached results
Ok(similar.into_iter()
.map(|item| self.deserialize_result(&item.metadata))
.collect())
}
pub async fn deduplicate_results(
&self,
results: Vec<OCRResult>,
similarity_threshold: f32
) -> Vec<OCRResult> {
let mut deduplicated = Vec::new();
for result in results {
let embedding = self.get_or_compute_embedding(&result.text).await.unwrap();
// Check if similar result already exists
let similar = self.vector_db.search(&embedding, 1).unwrap();
if similar.is_empty() || similar[0].score < similarity_threshold {
deduplicated.push(result.clone());
// Add to vector DB
self.vector_db.insert(
Uuid::new_v4().to_string(),
embedding,
HashMap::from([
("text".to_string(), result.text.clone()),
])
).unwrap();
}
}
deduplicated
}
}
```
**Expected Results:**
- Similarity search: 500ms → 25ms (20x speedup)
- Deduplication accuracy: 98%
- Storage efficiency: 768 dimensions × 4 bytes per embedding
- Scalability: Millions of embeddings
### 5.3 Result Memoization
**Objective**: Cache intermediate computation results for common patterns.
```rust
// Expected Improvement: 60% faster for repeated patterns
use moka::sync::Cache;
pub struct MemoizedOCR {
preprocessing_cache: Cache<PreprocessKey, Tensor>,
inference_cache: Cache<InferenceKey, Tensor>,
postprocessing_cache: Cache<PostprocessKey, OCRResult>,
}
#[derive(Clone, Hash, Eq, PartialEq)]
struct PreprocessKey {
image_hash: [u8; 32],
target_size: (usize, usize),
normalization: NormalizationParams,
}
impl MemoizedOCR {
pub fn new() -> Self {
Self {
preprocessing_cache: Cache::builder()
.max_capacity(1000)
.time_to_live(Duration::from_secs(3600))
.build(),
inference_cache: Cache::builder()
.max_capacity(500)
.time_to_live(Duration::from_secs(1800))
.build(),
postprocessing_cache: Cache::builder()
.max_capacity(2000)
.time_to_live(Duration::from_secs(3600))
.build(),
}
}
pub async fn process_with_memoization(
&self,
image: &DynamicImage
) -> Result<OCRResult> {
// 1. Memoized preprocessing
let preprocess_key = self.create_preprocess_key(image);
let preprocessed = self.preprocessing_cache
.get_or_insert_with(preprocess_key, || {
self.preprocess(image)
});
// 2. Memoized inference
let inference_key = self.create_inference_key(&preprocessed);
let inference_output = self.inference_cache
.get_or_insert_with(inference_key, || async {
self.model.infer(&preprocessed).await.unwrap()
}.await);
// 3. Memoized postprocessing
let postprocess_key = self.create_postprocess_key(&inference_output);
let result = self.postprocessing_cache
.get_or_insert_with(postprocess_key, || {
self.postprocess(&inference_output)
});
Ok(result)
}
pub fn get_cache_stats(&self) -> CacheStats {
CacheStats {
preprocessing_hit_rate: self.preprocessing_cache.hit_rate(),
inference_hit_rate: self.inference_cache.hit_rate(),
postprocessing_hit_rate: self.postprocessing_cache.hit_rate(),
}
}
}
```
**Expected Results:**
- Preprocessing cache hit rate: 40%
- Inference cache hit rate: 25%
- Postprocessing cache hit rate: 50%
- Overall speedup: 60% on cached patterns
---
## 6. Platform-Specific Optimizations
### 6.1 x86_64 AVX-512 Acceleration
**Objective**: Leverage AVX-512 for vectorized operations on modern Intel CPUs.
```rust
// Expected Improvement: 8-16x speedup for SIMD operations
#[cfg(target_arch = "x86_64")]
use std::arch::x86_64::*;
pub struct AVX512Processor {
_phantom: std::marker::PhantomData<()>,
}
impl AVX512Processor {
#[target_feature(enable = "avx512f")]
pub unsafe fn batch_normalize_avx512(
data: &mut [f32],
mean: f32,
std: f32
) {
let mean_vec = _mm512_set1_ps(mean);
let std_vec = _mm512_set1_ps(std);
// Process 16 floats at a time
for chunk in data.chunks_exact_mut(16) {
let values = _mm512_loadu_ps(chunk.as_ptr());
let normalized = _mm512_div_ps(
_mm512_sub_ps(values, mean_vec),
std_vec
);
_mm512_storeu_ps(chunk.as_mut_ptr(), normalized);
}
// Handle remainder with scalar operations
let remainder_offset = (data.len() / 16) * 16;
for i in remainder_offset..data.len() {
data[i] = (data[i] - mean) / std;
}
}
#[target_feature(enable = "avx512f")]
pub unsafe fn matrix_multiply_avx512(
a: &[f32],
b: &[f32],
c: &mut [f32],
m: usize,
n: usize,
k: usize
) {
for i in 0..m {
for j in (0..n).step_by(16) {
let mut sum = _mm512_setzero_ps();
for p in 0..k {
let a_val = _mm512_set1_ps(a[i * k + p]);
let b_vals = _mm512_loadu_ps(&b[p * n + j]);
sum = _mm512_fmadd_ps(a_val, b_vals, sum);
}
_mm512_storeu_ps(&mut c[i * n + j], sum);
}
}
}
#[target_feature(enable = "avx512f", enable = "avx512bw")]
pub unsafe fn convert_u8_to_f32_avx512(
input: &[u8],
output: &mut [f32]
) {
// Process 16 bytes at a time
for (chunk_in, chunk_out) in input.chunks_exact(16)
.zip(output.chunks_exact_mut(16))
{
// Load 16 u8 values
let u8_values = _mm_loadu_si128(chunk_in.as_ptr() as *const __m128i);
// Convert to u32
let u32_values = _mm512_cvtepu8_epi32(u8_values);
// Convert to f32
let f32_values = _mm512_cvtepi32_ps(u32_values);
// Store result
_mm512_storeu_ps(chunk_out.as_mut_ptr(), f32_values);
}
}
}
```
**Expected Results:**
- Normalization: 100ms → 8ms (12.5x speedup)
- Matrix multiplication: 500ms → 35ms (14x speedup)
- Type conversion: 50ms → 4ms (12.5x speedup)
- Throughput: 16 operations per cycle
### 6.2 ARM NEON for Mobile
**Objective**: Optimize for mobile devices using ARM NEON SIMD.
```rust
// Expected Improvement: 4-8x speedup on ARM devices
#[cfg(target_arch = "aarch64")]
use std::arch::aarch64::*;
pub struct NEONProcessor {
_phantom: std::marker::PhantomData<()>,
}
impl NEONProcessor {
#[target_feature(enable = "neon")]
pub unsafe fn batch_normalize_neon(
data: &mut [f32],
mean: f32,
std: f32
) {
let mean_vec = vdupq_n_f32(mean);
let std_vec = vdupq_n_f32(std);
// Process 4 floats at a time
for chunk in data.chunks_exact_mut(4) {
let values = vld1q_f32(chunk.as_ptr());
let sub_result = vsubq_f32(values, mean_vec);
let div_result = vdivq_f32(sub_result, std_vec);
vst1q_f32(chunk.as_mut_ptr(), div_result);
}
}
#[target_feature(enable = "neon")]
pub unsafe fn resize_bilinear_neon(
src: &[u8],
dst: &mut [u8],
src_width: usize,
src_height: usize,
dst_width: usize,
dst_height: usize
) {
let x_ratio = (src_width << 16) / dst_width;
let y_ratio = (src_height << 16) / dst_height;
for y in 0..dst_height {
let src_y = (y * y_ratio) >> 16;
let y_diff = ((y * y_ratio) >> 8) & 0xFF;
for x in (0..dst_width).step_by(4) {
// NEON-accelerated bilinear interpolation
let src_x = (x * x_ratio) >> 16;
let x_diff = ((x * x_ratio) >> 8) & 0xFF;
// Load 4 pixels
let pixels = vld1_u8(&src[src_y * src_width + src_x]);
// Interpolate (simplified)
vst1_u8(&mut dst[y * dst_width + x], pixels);
}
}
}
}
```
**Expected Results:**
- Mobile CPU usage: 80% → 40%
- Battery impact: 50% reduction
- Latency on mobile: 2000ms → 500ms (4x)
- Temperature: Reduced
### 6.3 WebAssembly SIMD
**Objective**: Enable high-performance OCR in browser environments.
```rust
// Expected Improvement: 2-4x speedup in browsers
#[cfg(target_arch = "wasm32")]
use std::arch::wasm32::*;
pub struct WasmSimdProcessor {
_phantom: std::marker::PhantomData<()>,
}
#[cfg(target_arch = "wasm32")]
impl WasmSimdProcessor {
pub fn batch_normalize_wasm_simd(
data: &mut [f32],
mean: f32,
std: f32
) {
unsafe {
let mean_vec = f32x4_splat(mean);
let std_vec = f32x4_splat(std);
// Process 4 floats at a time
for chunk in data.chunks_exact_mut(4) {
let values = v128_load(chunk.as_ptr() as *const v128);
let sub_result = f32x4_sub(values, mean_vec);
let div_result = f32x4_div(sub_result, std_vec);
v128_store(chunk.as_mut_ptr() as *mut v128, div_result);
}
}
}
pub fn rgb_to_grayscale_wasm_simd(
rgb: &[u8],
gray: &mut [u8]
) {
unsafe {
let weights = u8x16(
77, 150, 29, 0, // R, G, B weights (scaled)
77, 150, 29, 0,
77, 150, 29, 0,
77, 150, 29, 0
);
for (chunk_rgb, chunk_gray) in rgb.chunks_exact(12)
.zip(gray.chunks_exact_mut(4))
{
let pixels = v128_load(chunk_rgb.as_ptr() as *const v128);
let weighted = u8x16_mul(pixels, weights);
// Sum RGB components
let result = u8x16_add_sat(
u8x16_add_sat(
u8x16_extract_lane::<0>(weighted),
u8x16_extract_lane::<1>(weighted)
),
u8x16_extract_lane::<2>(weighted)
);
// Store grayscale value
chunk_gray[0] = (result >> 8) as u8;
}
}
}
}
// Compile with: --target wasm32-unknown-unknown -C target-feature=+simd128
```
**Expected Results:**
- Browser latency: 3000ms → 800ms (3.75x)
- CPU usage: 100% → 50%
- Memory: 200MB → 150MB
- Compatibility: Chrome 91+, Firefox 89+
### 6.4 GPU Acceleration
**Objective**: Leverage GPU compute for massive parallelism.
#### CUDA (NVIDIA)
```rust
// Expected Improvement: 10-50x speedup on high-end GPUs
use cudarc::driver::*;
pub struct CudaAccelerator {
device: CudaDevice,
kernel: CudaFunction,
}
impl CudaAccelerator {
pub fn new() -> Result<Self> {
let device = CudaDevice::new(0)?;
// Load CUDA kernel
let ptx = include_str!("kernels/ocr.ptx");
device.load_ptx(ptx.into(), "ocr_module", &["preprocess_kernel"])?;
let kernel = device.get_func("ocr_module", "preprocess_kernel")?;
Ok(Self { device, kernel })
}
pub async fn preprocess_gpu(&self, images: &[u8]) -> Result<Tensor> {
// 1. Allocate GPU memory
let d_input = self.device.htod_copy(images.to_vec())?;
let d_output = self.device.alloc_zeros::<f32>(images.len())?;
// 2. Launch kernel
let cfg = LaunchConfig {
grid_dim: (images.len() / 256 + 1, 1, 1),
block_dim: (256, 1, 1),
shared_mem_bytes: 0,
};
unsafe {
self.kernel.launch(cfg, (
&d_input,
&d_output,
images.len(),
))?;
}
// 3. Copy result back
let output = self.device.dtoh_sync_copy(&d_output)?;
Ok(Tensor::from_vec(output))
}
}
// CUDA kernel (OCR preprocessing)
/*
__global__ void preprocess_kernel(
const unsigned char* input,
float* output,
int size
) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < size) {
// Normalize to [0, 1]
output[idx] = input[idx] / 255.0f;
// Apply mean/std normalization
output[idx] = (output[idx] - 0.5f) / 0.5f;
}
}
*/
```
**Expected Results:**
- Preprocessing: 100ms → 5ms (20x speedup)
- Batch processing: 1000 img/s on RTX 4090
- Memory bandwidth: 1TB/s (GPU memory)
- Power efficiency: 5x better than CPU
#### Metal (Apple Silicon)
```rust
// Expected Improvement: 15-30x speedup on M1/M2/M3
use metal::*;
pub struct MetalAccelerator {
device: Device,
command_queue: CommandQueue,
pipeline: ComputePipelineState,
}
impl MetalAccelerator {
pub fn new() -> Result<Self> {
let device = Device::system_default()
.ok_or(Error::NoMetalDevice)?;
let command_queue = device.new_command_queue();
// Load Metal shader
let library = device.new_library_with_source(
include_str!("shaders/ocr.metal"),
&CompileOptions::new()
)?;
let kernel = library.get_function("preprocess_kernel", None)?;
let pipeline = device.new_compute_pipeline_state_with_function(&kernel)?;
Ok(Self { device, command_queue, pipeline })
}
pub async fn preprocess_metal(&self, images: &[u8]) -> Result<Vec<f32>> {
// 1. Create buffers
let input_buffer = self.device.new_buffer_with_data(
images.as_ptr() as *const _,
images.len() as u64,
MTLResourceOptions::StorageModeShared
);
let output_buffer = self.device.new_buffer(
(images.len() * std::mem::size_of::<f32>()) as u64,
MTLResourceOptions::StorageModeShared
);
// 2. Create command buffer
let command_buffer = self.command_queue.new_command_buffer();
let encoder = command_buffer.new_compute_command_encoder();
// 3. Encode kernel
encoder.set_compute_pipeline_state(&self.pipeline);
encoder.set_buffer(0, Some(&input_buffer), 0);
encoder.set_buffer(1, Some(&output_buffer), 0);
let grid_size = MTLSize::new(images.len() as u64, 1, 1);
let threadgroup_size = MTLSize::new(256, 1, 1);
encoder.dispatch_threads(grid_size, threadgroup_size);
encoder.end_encoding();
// 4. Execute
command_buffer.commit();
command_buffer.wait_until_completed();
// 5. Read results
let output_ptr = output_buffer.contents() as *const f32;
let output = unsafe {
std::slice::from_raw_parts(output_ptr, images.len())
};
Ok(output.to_vec())
}
}
```
**Expected Results (M2 Pro):**
- Preprocessing: 100ms → 4ms (25x speedup)
- Inference: 1000ms → 50ms (20x with CoreML)
- Power consumption: 10W vs 40W on Intel
- Unified memory: Zero-copy possible
---
## 7. Progressive Loading
### 7.1 Lazy Model Loading
**Objective**: Load model components on-demand to reduce initialization time.
```rust
// Expected Improvement: Startup time 5000ms → 500ms
use std::sync::OnceLock;
pub struct LazyModelLoader {
encoder: OnceLock<Arc<EncoderModel>>,
decoder: OnceLock<Arc<DecoderModel>>,
postprocessor: OnceLock<Arc<Postprocessor>>,
model_path: String,
}
impl LazyModelLoader {
pub fn new(model_path: String) -> Self {
Self {
encoder: OnceLock::new(),
decoder: OnceLock::new(),
postprocessor: OnceLock::new(),
model_path,
}
}
pub async fn get_encoder(&self) -> &Arc<EncoderModel> {
self.encoder.get_or_init(|| {
Arc::new(EncoderModel::load(&self.model_path).unwrap())
})
}
pub async fn get_decoder(&self) -> &Arc<DecoderModel> {
self.decoder.get_or_init(|| {
Arc::new(DecoderModel::load(&self.model_path).unwrap())
})
}
pub async fn preload_all(&self) {
// Parallel loading
let (encoder, decoder, postprocessor) = tokio::join!(
async { self.get_encoder().await },
async { self.get_decoder().await },
async { self.get_postprocessor().await }
);
}
}
// Application with lazy loading
pub struct OCRApplication {
model_loader: LazyModelLoader,
feature_flags: FeatureFlags,
}
impl OCRApplication {
pub async fn startup(&self) -> Result<()> {
// Only load components needed for initial features
if self.feature_flags.math_ocr_enabled {
self.model_loader.get_encoder().await;
}
// Decoder loaded on first use
Ok(())
}
pub async fn process_first_request(&self, image: &Image) -> Result<String> {
// Triggers lazy loading of decoder if not yet loaded
let encoder = self.model_loader.get_encoder().await;
let decoder = self.model_loader.get_decoder().await;
// Process normally
let features = encoder.encode(image).await?;
let text = decoder.decode(&features).await?;
Ok(text)
}
}
```
**Expected Results:**
- Initial startup: 5000ms → 500ms (10x faster)
- First request latency: +500ms (one-time cost)
- Memory usage: Reduced by 60% if not all features used
- User experience: App responsive immediately
### 7.2 Feature-Based Loading
**Objective**: Load only the model components needed for specific features.
```rust
// Expected Improvement: 70% memory reduction for specialized use cases
pub struct FeatureBasedModel {
config: ModelConfig,
loaded_features: Arc<RwLock<HashSet<Feature>>>,
model_registry: Arc<RwLock<HashMap<Feature, Arc<dyn ModelComponent>>>>,
}
#[derive(Hash, Eq, PartialEq, Clone)]
pub enum Feature {
MathOCR,
HandwritingRecognition,
DocumentLayout,
TableExtraction,
ChemicalFormulas,
MusicNotation,
}
impl FeatureBasedModel {
pub async fn load_feature(&self, feature: Feature) -> Result<()> {
// Check if already loaded
{
let loaded = self.loaded_features.read().await;
if loaded.contains(&feature) {
return Ok(());
}
}
// Load feature-specific model
let model_component = match feature {
Feature::MathOCR => {
Arc::new(MathOCRModel::load(&self.config.math_model_path)?)
as Arc<dyn ModelComponent>
}
Feature::HandwritingRecognition => {
Arc::new(HandwritingModel::load(&self.config.handwriting_model_path)?)
as Arc<dyn ModelComponent>
}
Feature::DocumentLayout => {
Arc::new(LayoutModel::load(&self.config.layout_model_path)?)
as Arc<dyn ModelComponent>
}
// ... other features
};
// Register model
{
let mut registry = self.model_registry.write().await;
registry.insert(feature.clone(), model_component);
}
// Mark as loaded
{
let mut loaded = self.loaded_features.write().await;
loaded.insert(feature);
}
Ok(())
}
pub async fn process_with_features(
&self,
image: &Image,
required_features: &[Feature]
) -> Result<OCRResult> {
// Load all required features
for feature in required_features {
self.load_feature(feature.clone()).await?;
}
// Process with loaded features
let registry = self.model_registry.read().await;
let mut result = OCRResult::new();
for feature in required_features {
if let Some(model) = registry.get(feature) {
let feature_result = model.process(image).await?;
result.merge(feature_result);
}
}
Ok(result)
}
pub async fn unload_feature(&self, feature: Feature) {
let mut registry = self.model_registry.write().await;
registry.remove(&feature);
let mut loaded = self.loaded_features.write().await;
loaded.remove(&feature);
}
}
// Usage example
pub async fn process_math_document(image: &Image) -> Result<OCRResult> {
let model = FeatureBasedModel::new(config);
// Only load math OCR feature (much smaller than full model)
model.process_with_features(
image,
&[Feature::MathOCR, Feature::DocumentLayout]
).await
}
```
**Model Sizes:**
- Full model: 500MB
- Math OCR only: 80MB (84% reduction)
- Handwriting only: 120MB (76% reduction)
- Document layout only: 50MB (90% reduction)
**Expected Results:**
- Memory usage: 500MB → 80-150MB (70-84% reduction)
- Loading time: 5000ms → 800ms (specialized features)
- Flexibility: Load/unload features dynamically
- Use case optimization: Perfect for specialized applications
---
## 8. Optimization Milestones
### Phase 1: Baseline (Current State)
**Target Metrics:**
- Inference latency: 1000ms/image
- Throughput: 1 image/second
- CPU utilization: 80%
- GPU utilization: 40%
- Memory usage: 2GB
- Model size: 500MB
**Implementation Status:**
- ✅ Basic ONNX Runtime integration
- ✅ Single-threaded inference
- ✅ Standard preprocessing
- ⬜ No caching
- ⬜ No batching
- ⬜ No SIMD optimizations
**Bottlenecks Identified:**
1. Sequential image processing
2. No GPU utilization optimization
3. Repeated preprocessing computations
4. Large model size
5. Memory allocation overhead
---
### Phase 2: Optimized (Target: 3 months)
**Target Metrics:**
- Inference latency: 100ms/image (10x improvement)
- Throughput: 15 images/second (15x improvement)
- CPU utilization: 60%
- GPU utilization: 85%
- Memory usage: 1GB (50% reduction)
- Model size: 125MB (75% reduction via INT8)
**Implementation Roadmap:**
#### Month 1: Model Optimization
- [ ] Implement INT8 quantization
- Expected: 4x speedup, 75% size reduction
- Risk: 2-5% accuracy loss
- Priority: HIGH
- [ ] Integrate TensorRT/OpenVINO
- Expected: 3-5x speedup
- Risk: Platform dependency
- Priority: HIGH
- [ ] Model warm-up and caching
- Expected: Eliminate cold start (5000ms → 100ms)
- Risk: Memory overhead
- Priority: MEDIUM
#### Month 2: Parallelization & Batching
- [ ] Implement batch processing
- Expected: 3-5x throughput improvement
- Risk: Increased latency for small loads
- Priority: HIGH
- [ ] Add pipeline parallelism
- Expected: 2-3x throughput
- Risk: Complexity
- Priority: MEDIUM
- [ ] Rayon for CPU parallelism
- Expected: 7-8x on 8-core CPU
- Risk: None
- Priority: HIGH
#### Month 3: Memory & Caching
- [ ] Implement LRU cache
- Expected: 100% speedup on cache hits
- Risk: Memory overhead (100MB)
- Priority: HIGH
- [ ] Memory-mapped model loading
- Expected: 200x faster loading
- Risk: Platform compatibility
- Priority: MEDIUM
- [ ] Zero-copy preprocessing
- Expected: 40% faster preprocessing
- Risk: Complexity
- Priority: LOW
**Success Criteria:**
- ✅ Latency < 150ms (target: 100ms)
- ✅ Throughput > 10 img/s (target: 15 img/s)
- ✅ Memory < 1.5GB (target: 1GB)
- ✅ Accuracy degradation < 5%
---
### Phase 3: Production (Target: 6 months)
**Target Metrics:**
- Inference latency: 50ms/image (20x improvement)
- Throughput: 30 images/second (30x improvement)
- CPU utilization: 40%
- GPU utilization: 90%
- Memory usage: 500MB (75% reduction)
- Model size: 50MB (90% reduction via distillation)
**Implementation Roadmap:**
#### Month 4: Advanced Model Optimization
- [ ] Knowledge distillation
- Expected: 10x speedup, 80% size reduction
- Risk: 3-5% accuracy loss, requires retraining
- Priority: HIGH
- [ ] Structured pruning
- Expected: 2.5x speedup, 50% parameter reduction
- Risk: Requires fine-tuning
- Priority: MEDIUM
- [ ] Speculative decoding
- Expected: 2-3x faster text generation
- Risk: Complexity
- Priority: LOW
#### Month 5: Platform-Specific Optimization
- [ ] AVX-512 implementation
- Expected: 8-16x SIMD speedup
- Risk: Limited CPU support
- Priority: MEDIUM
- [ ] ARM NEON for mobile
- Expected: 4-8x speedup on mobile
- Risk: None
- Priority: MEDIUM
- [ ] Metal/CUDA acceleration
- Expected: 15-30x speedup
- Risk: Platform dependency
- Priority: HIGH
#### Month 6: Advanced Features
- [ ] Dynamic batching
- Expected: Optimal latency/throughput trade-off
- Risk: Complexity
- Priority: HIGH
- [ ] Streaming for large documents
- Expected: Unlimited document size
- Risk: Complexity
- Priority: MEDIUM
- [ ] Vector embedding cache
- Expected: 95% faster similarity search
- Risk: Memory overhead
- Priority: LOW
**Success Criteria:**
- ✅ Latency < 75ms (target: 50ms)
- ✅ Throughput > 25 img/s (target: 30 img/s)
- ✅ Memory < 750MB (target: 500MB)
- ✅ Accuracy degradation < 5% total
- ✅ 99.9% uptime in production
- ✅ Sub-100ms p99 latency
---
## Performance Benchmarking Suite
### Benchmark Implementation
```rust
use criterion::{black_box, criterion_group, criterion_main, Criterion, BenchmarkId};
pub fn benchmark_preprocessing(c: &mut Criterion) {
let mut group = c.benchmark_group("preprocessing");
for size in [224, 384, 512, 1024].iter() {
group.bench_with_input(
BenchmarkId::new("baseline", size),
size,
|b, &size| {
let image = create_test_image(size, size);
b.iter(|| preprocess_baseline(black_box(&image)))
}
);
group.bench_with_input(
BenchmarkId::new("simd", size),
size,
|b, &size| {
let image = create_test_image(size, size);
b.iter(|| preprocess_simd(black_box(&image)))
}
);
group.bench_with_input(
BenchmarkId::new("zero_copy", size),
size,
|b, &size| {
let image = create_test_image(size, size);
b.iter(|| preprocess_zero_copy(black_box(&image)))
}
);
}
group.finish();
}
pub fn benchmark_inference(c: &mut Criterion) {
let mut group = c.benchmark_group("inference");
group.bench_function("baseline", |b| {
let model = load_baseline_model();
let input = create_test_tensor();
b.iter(|| model.infer(black_box(&input)))
});
group.bench_function("int8_quantized", |b| {
let model = load_int8_model();
let input = create_test_tensor();
b.iter(|| model.infer(black_box(&input)))
});
group.bench_function("distilled", |b| {
let model = load_distilled_model();
let input = create_test_tensor();
b.iter(|| model.infer(black_box(&input)))
});
group.finish();
}
pub fn benchmark_batching(c: &mut Criterion) {
let mut group = c.benchmark_group("batching");
for batch_size in [1, 4, 8, 16, 32].iter() {
group.bench_with_input(
BenchmarkId::from_parameter(batch_size),
batch_size,
|b, &batch_size| {
let images = create_test_batch(batch_size);
b.iter(|| process_batch(black_box(&images)))
}
);
}
group.finish();
}
criterion_group!(
benches,
benchmark_preprocessing,
benchmark_inference,
benchmark_batching
);
criterion_main!(benches);
```
### Expected Benchmark Results
#### Phase 1 (Baseline)
```
preprocessing/baseline/224 100.5 ms
preprocessing/baseline/512 245.8 ms
inference/baseline 1000.2 ms
batching/1 1000.2 ms
batching/16 N/A (not implemented)
```
#### Phase 2 (Optimized)
```
preprocessing/simd/224 12.4 ms (8.1x improvement)
preprocessing/simd/512 31.2 ms (7.9x improvement)
inference/int8_quantized 248.5 ms (4.0x improvement)
batching/1 100.5 ms (10x improvement)
batching/16 65.2 ms/img (15.4x throughput)
```
#### Phase 3 (Production)
```
preprocessing/zero_copy/224 3.8 ms (26.4x improvement)
preprocessing/zero_copy/512 9.1 ms (27.0x improvement)
inference/distilled 98.3 ms (10.2x improvement)
inference/distilled+gpu 47.8 ms (20.9x improvement)
batching/1 50.2 ms (19.9x improvement)
batching/32 31.5 ms/img (31.8x throughput)
```
---
## Monitoring and Metrics
### Key Performance Indicators (KPIs)
1. **Latency Metrics**
- p50: Median latency
- p95: 95th percentile
- p99: 99th percentile
- p99.9: 99.9th percentile
2. **Throughput Metrics**
- Images/second
- Requests/second
- Tokens/second (for text generation)
3. **Resource Utilization**
- CPU usage (%)
- GPU usage (%)
- Memory usage (MB)
- Disk I/O (MB/s)
4. **Quality Metrics**
- Accuracy
- Character Error Rate (CER)
- Word Error Rate (WER)
- F1 Score
5. **Cost Metrics**
- Cost per 1000 images
- Infrastructure cost/month
- Power consumption (W)
### Continuous Monitoring
```rust
use prometheus::{Registry, Histogram, Counter, Gauge};
pub struct PerformanceMonitor {
latency_histogram: Histogram,
throughput_counter: Counter,
memory_gauge: Gauge,
accuracy_gauge: Gauge,
}
impl PerformanceMonitor {
pub fn record_inference(&self, duration: Duration, accuracy: f32) {
self.latency_histogram.observe(duration.as_secs_f64());
self.throughput_counter.inc();
self.accuracy_gauge.set(accuracy as f64);
}
pub fn get_report(&self) -> PerformanceReport {
PerformanceReport {
p50_latency: self.latency_histogram.get_sample_sum() / 2.0,
p99_latency: self.calculate_percentile(99.0),
throughput: self.throughput_counter.get() / 60.0, // per second
avg_accuracy: self.accuracy_gauge.get(),
}
}
}
```
---
## Conclusion
This optimization roadmap provides a systematic approach to improving the ruvector-scipix OCR system from baseline (1000ms/image) to production-ready (50ms/image) performance. The three-phase approach ensures:
1. **Quick Wins (Phase 1)**: Foundation with basic optimizations
2. **Substantial Improvements (Phase 2)**: 10x speedup through parallelization and quantization
3. **Production Excellence (Phase 3)**: 20x speedup with advanced techniques
**Key Success Factors:**
- Prioritize high-impact optimizations first
- Maintain accuracy within 5% degradation
- Benchmark continuously
- Monitor production metrics
- Iterate based on real-world usage
**Expected ROI:**
- **Performance**: 20x faster inference
- **Cost**: 75% reduction in compute costs
- **User Experience**: Sub-100ms latency
- **Scalability**: 30x throughput improvement
Implementation should follow agile methodology with 2-week sprints, continuous integration, and regular performance regression testing.
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