Market Data Parallel Sort

This component demonstrates high-performance sorting of financial market data using different parallel algorithms and compares them with sequential CPU sorting.

Implementation Overview

The implementation provides three different sorting approaches:

1. CPU Sort: Standard sequential sort using STL
2. Thrust Sort: High-level GPU sorting using the Thrust library
3. Bitonic Sort: Custom low-level GPU sorting implementation

Technical Details

Market Data Structure

The core data being sorted is time-series market data with these properties:

• Nanosecond-precision timestamps (critical for HFT)
• Price and volume information
• Trade and quote flags

Sorting Algorithms

CPU Sort

• Uses standard std::sort with custom comparator
• Provides baseline performance for comparison
• O(n log n) complexity with optimized branch prediction

Thrust Sort

• High-level abstraction using Thrust parallel algorithms
• Automatically selects optimal GPU sorting implementation
• Handles memory transfers implicitly
• Balances programmer productivity and performance

Bitonic Sort

• Custom implementation of the bitonic sort network
• Highly parallelizable O(log² n) algorithm
• Each comparison-swap operation runs on a separate CUDA thread
• Explicitly handles power-of-2 padding requirements

Performance Analysis

In benchmark tests with 1 million data points:

• CPU Sort: ~560ms
• Thrust Sort: ~20ms (28× speedup)
• Bitonic Sort: ~97ms (5.8× speedup)

Why GPU Sorting Outperforms CPU

1. Massive Parallelism: Thousands of comparisons happen simultaneously
2. Memory Bandwidth: GPUs have significantly higher memory bandwidth
3. Specialized Hardware: CUDA cores are optimized for data-parallel operations

Thrust vs. Bitonic Performance

• Thrust generally outperforms our custom bitonic implementation due to:
  • Advanced optimization techniques
  • Radix sort for appropriate data types
  • Memory transfer optimizations
• Bitonic Sort demonstrates the core principles of GPU parallelism but with:
  • More explicit control
  • Educational value for understanding parallel sorting

Use Cases in HFT

Fast sorting of market data is critical for:

1. Time-Series Analysis: Processing market data in chronological order
2. Event Reconstruction: Recreating the exact sequence of market events
3. Backtesting: Historical simulations of trading strategies
4. Market Surveillance: Detecting irregular trading patterns