wifi-densepose/vendor/sublinear-time-solver/crates/temporal-compare/OPTIMIZATIONS.md

Advanced Optimizations for temporal-compare v0.5.0

Cutting-Edge Libraries to Integrate (2025)

1. Candle Integration

candle-core = "0.3"
candle-nn = "0.3"
candle-transformers = "0.3"

• GPU acceleration via CUDA/Metal/WebGPU
• Transformer models for temporal sequences
• Zero-copy tensor operations
• Quantized inference (INT8/INT4)

2. Burn Framework

burn = "0.13"
burn-import = "0.13"
burn-wgpu = "0.13"  # WebGPU backend

• ONNX model import
• Automatic kernel fusion
• WebAssembly deployment
• Multi-backend support (CPU/GPU/WASM)

3. DFDX for Automatic Differentiation

dfdx = "0.13"

• Compile-time shape checking
• Automatic differentiation
• Functional programming style
• GPU acceleration via CUDA

4. ONNX Runtime

ort = "2.0"

• Run PyTorch/TensorFlow models
• Hardware acceleration
• Quantization support
• Cross-platform deployment

Performance Optimizations

1. GPU Acceleration

// Using Candle for GPU operations
use candle_core::{Device, Tensor};
use candle_nn::{Module, VarBuilder};

pub struct GpuMlp {
    device: Device,
    layers: Vec<candle_nn::Linear>,
}

impl GpuMlp {
    pub fn new_cuda() -> Result<Self> {
        let device = Device::cuda_if_available(0)?;
        // Initialize on GPU
    }
}

2. Quantization (INT8/INT4)

// Reduce model size by 75% with INT8 quantization
pub struct QuantizedMlp {
    weights_int8: Vec<i8>,
    scale: f32,
    zero_point: i8,
}

impl QuantizedMlp {
    pub fn quantize(weights: &[f32]) -> Self {
        // Dynamic quantization
        let (min, max) = weights.iter().fold((f32::MAX, f32::MIN),
            |(min, max), &w| (min.min(w), max.max(w)));
        let scale = (max - min) / 255.0;
        let zero_point = (-min / scale) as i8;
        let weights_int8 = weights.iter()
            .map(|&w| ((w / scale) + zero_point as f32) as i8)
            .collect();
        Self { weights_int8, scale, zero_point }
    }
}

3. FlashAttention for Transformers

// O(N) memory instead of O(N²)
pub struct FlashAttention {
    block_size: usize,
}

impl FlashAttention {
    pub fn forward(&self, q: &Tensor, k: &Tensor, v: &Tensor) -> Tensor {
        // Tiled computation to fit in SRAM
        // 2-3x faster than standard attention
    }
}

4. Kernel Fusion

// Fuse multiple operations into single kernel
pub fn fused_linear_relu(x: &Tensor, w: &Tensor, b: &Tensor) -> Tensor {
    // Single GPU kernel for: matmul + bias + relu
    unsafe {
        let mut output = Tensor::zeros_like(x);
        cuda_fused_linear_relu(
            x.as_ptr(), w.as_ptr(), b.as_ptr(),
            output.as_mut_ptr(), x.shape()[0], w.shape()[1]
        );
        output
    }
}

5. Memory Pooling

use std::sync::Arc;
use parking_lot::Mutex;

pub struct TensorPool {
    pools: Arc<Mutex<HashMap<usize, Vec<Vec<f32>>>>>,
}

impl TensorPool {
    pub fn acquire(&self, size: usize) -> Vec<f32> {
        let mut pools = self.pools.lock();
        pools.entry(size).or_default().pop()
            .unwrap_or_else(|| vec![0.0; size])
    }

    pub fn release(&self, mut tensor: Vec<f32>) {
        let size = tensor.len();
        tensor.clear();
        tensor.resize(size, 0.0);
        self.pools.lock().entry(size).or_default().push(tensor);
    }
}

6. Mixed Precision Training

// Use FP16 for compute, FP32 for master weights
pub struct MixedPrecisionTrainer {
    fp32_weights: Vec<f32>,
    fp16_weights: Vec<f16>,
    loss_scale: f32,
}

impl MixedPrecisionTrainer {
    pub fn forward(&self, x: &[f16]) -> Vec<f16> {
        // Compute in FP16 (2x faster on modern GPUs)
    }

    pub fn backward(&mut self, grad: &[f16]) {
        // Scale gradients to prevent underflow
        let scaled_grad: Vec<f32> = grad.iter()
            .map(|&g| g.to_f32() * self.loss_scale)
            .collect();
        // Update FP32 master weights
    }
}

7. Graph Optimization

// Compile computation graph for optimal execution
use petgraph::graph::DiGraph;

pub struct ComputeGraph {
    graph: DiGraph<Op, Edge>,
}

impl ComputeGraph {
    pub fn optimize(&mut self) {
        // Constant folding
        self.fold_constants();
        // Common subexpression elimination
        self.eliminate_common_subexpressions();
        // Operator fusion
        self.fuse_operators();
        // Memory layout optimization
        self.optimize_memory_layout();
    }
}

8. WebAssembly Deployment

#[cfg(target_arch = "wasm32")]
pub mod wasm {
    use wasm_bindgen::prelude::*;

    #[wasm_bindgen]
    pub struct WasmModel {
        model: Box<dyn Predictor>,
    }

    #[wasm_bindgen]
    impl WasmModel {
        pub fn predict(&self, input: &[f32]) -> Vec<f32> {
            // Run in browser at near-native speed
            self.model.predict(input)
        }
    }
}

9. Distributed Training

use mpi::collective::CommunicatorCollectives;

pub struct DistributedTrainer {
    comm: mpi::Communicator,
    rank: i32,
    size: i32,
}

impl DistributedTrainer {
    pub fn all_reduce_gradients(&self, gradients: &mut [f32]) {
        // Average gradients across all nodes
        self.comm.all_reduce_into(gradients, gradients, mpi::collective::SystemOperation::sum());
        gradients.iter_mut().for_each(|g| *g /= self.size as f32);
    }
}

10. Neural Architecture Search (NAS)

pub struct NeuralArchitectureSearch {
    search_space: Vec<LayerConfig>,
    population_size: usize,
}

impl NeuralArchitectureSearch {
    pub fn evolve(&mut self, generations: usize) -> Architecture {
        // Evolutionary algorithm to find optimal architecture
        let mut population = self.initialize_population();
        for _ in 0..generations {
            self.evaluate_fitness(&mut population);
            self.selection(&mut population);
            self.crossover(&mut population);
            self.mutate(&mut population);
        }
        population[0].clone()
    }
}

Benchmark Targets

With these optimizations, we should achieve:

Metric	Current	Target	Method
Training Speed	0.5s/10k	0.05s/10k	GPU + Mixed Precision
Inference Speed	1ms/batch	0.1ms/batch	Quantization + Kernel Fusion
Model Size	100KB	25KB	INT8 Quantization
Accuracy	65.2%	75%+	Transformers + NAS
Memory Usage	100MB	10MB	Memory Pooling
Deployment Size	10MB	1MB	WASM + Quantization

Implementation Priority

1. Phase 1: Candle integration for GPU acceleration
2. Phase 2: Quantization for 4x size reduction
3. Phase 3: Transformer models for better accuracy
4. Phase 4: ONNX support for model portability
5. Phase 5: WebAssembly for browser deployment

References

• Candle Documentation
• Burn Framework
• DFDX
• ONNX Runtime Rust
• FlashAttention Paper
• Mixed Precision Training