Architecture Overview

VectorAlpha's architecture prioritizes predictable latency and maximum throughput for quantitative finance workloads. Built on Rust's zero-cost abstractions, our libraries achieve microsecond-level response times while maintaining memory safety.

Core Design Principles

Zero-Copy Operations

Every data structure in VectorAlpha is designed to minimize memory allocations and copies. We use slice references, memory-mapped files, and arena allocators to ensure data stays in CPU cache as long as possible.

Lock-Free Data Structures

For multi-threaded scenarios, we implement lock-free ring buffers and concurrent queues using atomic operations. This eliminates thread contention and ensures consistent latency even under heavy load.

Learn more: Introduction to Lock-Free Programming

Memory Layout Optimization

Cache-Friendly Structures

All core data structures are designed with CPU cache lines in mind. We use structure-of-arrays (SoA) layout for vectorized operations and ensure hot data fits within L1/L2 cache.

Custom Allocators

For hot paths, we implement custom allocators that pre-allocate memory pools and reuse buffers. This eliminates allocation overhead and reduces memory fragmentation.

Parallelization Strategy

CPU Affinity

Critical threads are pinned to specific CPU cores to minimize context switching and maximize cache locality. We support NUMA-aware thread placement for multi-socket systems.

Work Stealing

Our parallel algorithms use work-stealing queues to balance load across cores dynamically. This ensures all cores stay busy without explicit synchronization overhead.

GPU Acceleration Architecture

Heterogeneous Computing

Our CUDA kernels are designed for massive parallelism, processing millions of data points simultaneously. We use unified memory for seamless CPU-GPU data sharing and implement custom kernels for each indicator type.

Kernel Optimization

• Coalesced Memory Access: Ensures adjacent threads access adjacent memory locations
• Shared Memory Usage: Caches frequently accessed data in fast on-chip memory
• Warp Divergence Minimization: Structures conditionals to keep GPU threads synchronized
• Occupancy Tuning: Balances register usage with thread count for maximum throughput

Benchmarking Methodology

All performance claims are validated using industry-standard benchmarking practices:

1. Warm-up Runs: JIT compilation and cache warming before measurements
2. Statistical Rigor: Multiple runs with variance analysis
3. Real-world Data: Testing with actual market data including edge cases
4. Hardware Variety: Benchmarks across different CPU and GPU configurations

Integration Patterns

Event-Driven Architecture

VectorAlpha libraries are designed to integrate seamlessly with event-driven trading systems. We provide async interfaces for Rust's Tokio runtime and callback-based APIs for C++ integration.

Message Passing

Inter-process communication uses memory-mapped ring buffers for zero-copy message passing between components. This allows different parts of your trading system to run in separate processes for fault isolation.

Additional Resources

Next Steps