Performance Optimization

Performance work in this stack is mostly about removing waste before it is about adding tricks. The big gains usually come from better data layout, better execution boundaries, and a clearer split between the reference path and the accelerated path. That is less glamorous than a single benchmark headline, but it is how the measured speed survives contact with real workloads.

The easiest mistake is to treat all speedups as the same kind of win. Some changes reduce algorithmic cost. Some changes improve constant factors. Some changes only look fast because they quietly changed the workload. If you blur those cases together, the optimization process drifts into scoreboard watching and stops improving the system.

Start by measuring the right contract

Before optimizing anything, decide what exactly is being timed. A single indicator over a warm in-memory slice is a valid benchmark if that is the question. Full parameter sweeps, strategy backtests, and desktop workflows ask different questions because they also load data, move results, and update a UI. The benchmark is only useful when the contract is stated clearly. 

cargo build --release
cargo test --release
cargo run --release --example benchmark

Release builds, stable input sets, and repeated runs are the floor. After that, keep the benchmark narrow enough that you can explain what changed when a result moves. If one run includes parsing, allocation, kernel dispatch, and reporting while another times only the inner loop, the comparison is already broken.

Separate algorithmic wins from constant-factor wins

Vectorization, fused multiply-add instructions, and better cache behavior are all constant-factor work. They matter a great deal, especially in indicator code that runs at scale, but the bigger wins often come from search structure and data pipelines that stop copying too much. Likewise, a better search strategy may reduce total work dramatically even if the inner loop is unchanged.

The right order is usually to fix the shape of the computation first and tune the hot loop second. Otherwise you end up polishing a path that should have been shorter rather than faster.

Keep data movement visible

A lot of fake performance comes from ignoring the cost of moving data between formats, layers, or devices. In the CPU path that often means hidden allocations, badly aligned buffers, or layout changes that kill cache locality. In the GPU path it usually means the transfer boundary was left out of the story. Throughput only matters if the full path to that throughput is still practical.

This is one reason the VectorAlpha material keeps returning to resident buffers, contiguous traversal, and explicit fallback paths. Those choices are structural. They are what makes a speedup survive outside a microbenchmark.

Keep Correctness Central

A performance optimization is finished only when it is fast and still agrees with the reference contract. That matters even more in quantitative work because a numerically wrong indicator can still look plausible at a glance. The scalar path, deterministic fixtures, and cross-path comparisons exist to keep the faster code accountable.

If a SIMD path, a CUDA path, and a scalar path disagree, resolve the contract change before trusting any benchmark result.

Where GPU Helps

GPU acceleration pays when the workload is wide enough and repeated enough to justify keeping the device busy. That usually means many indicators, many parameter combinations, or enough data that the host-device boundary stops dominating the cost. Small or highly irregular workloads often do better on a well-vectorized CPU path because the setup cost is lower and the control flow stays simpler.

Ask whether the whole workload can be arranged so that the GPU is doing sustained useful work. The stronger performance story in this stack is the VRAM-resident execution path, where the device stays busy long enough to justify the transfer and orchestration cost.

A practical optimization order

  1. Make the scalar reference simple enough that you trust it.
  2. Benchmark the real bottleneck, even when it is harder to isolate cleanly.
  3. Fix wasteful data movement and layout problems before adding new execution modes.
  4. Add SIMD or GPU paths only where the measured workload keeps proving they matter.
  5. Recheck correctness after every speed-focused change.

Next reads

For CPU-side engineering details, read SIMD Optimization Explained and SIMD vectorization for technical indicators . For the GPU side, continue with GPU Acceleration Setup, GPU accelerated technical indicators , and VRAM resident CUDA dispatch for technical indicators .