How FPGAs compare to GPUs or CPUs in HFT?

ampheoampheo
3 min read

Here’s a direct comparison of FPGAs, GPUs, and CPUs in the context of High-Frequency Trading (HFT):


1. Latency

TechnologyLatencyExplanation
FPGAUltra-low (10s–100s of ns)Processes data in hardware with near-instant reaction time.
CPU❌ High (microseconds to milliseconds)General-purpose; incurs OS overhead, context switching, etc.
GPU❌ Very high (milliseconds)Designed for batch, parallel tasks; not real-time sensitive.

2. Determinism (Consistency of Execution Time)

TechnologyDeterminismExplanation
FPGAVery deterministicFixed hardware logic yields consistent response times.
CPU❌ LowBackground processes, scheduling, and interrupts cause variability.
GPU❌ Very lowThread and kernel management introduce significant jitter.

3. Parallelism

TechnologyParallelismExplanation
FPGA✅ Fine-grained hardware-level parallelismCan execute many logic paths simultaneously.
CPU⚠️ LimitedFew cores, with limited concurrency.
GPU✅ Massive data-level parallelismThousands of threads, but for large, similar workloads.

4. Throughput (Volume of Data Processed)

TechnologyThroughputExplanation
FPGA✅ HighCan handle line-rate data directly from NICs (e.g., 10–100 Gbps).
CPU⚠️ MediumGood general performance but bottlenecked under extreme load.
GPU✅ High (for bulk processing)Ideal for processing huge batches of data in parallel.

5. Flexibility and Ease of Development

TechnologyFlexibilityExplanation
FPGA❌ LowRequires hardware description languages (VHDL/Verilog), long dev time.
CPU✅ HighFast development using common languages (C++, Python).
GPU⚠️ MediumRequires CUDA/OpenCL knowledge; suited for parallelizable algorithms.

6. Best Use Cases in HFT

TechnologyIdeal Use Cases
FPGAUltra-low-latency market data parsing, order book management, order routing
CPUStrategy logic, risk checks, logging, orchestration
GPUDeep learning models, backtesting, pattern recognition (non-real-time)

Summary Table

FeatureFPGACPUGPU
Latency✅ Best❌ Worst❌ Worst
Determinism✅ Best❌ Poor❌ Very poor
Parallelism✅ Custom logic⚠️ Moderate✅ Excellent (for bulk)
Flexibility❌ Low✅ High⚠️ Medium
Dev Time❌ Long✅ Short⚠️ Medium
HFT Fit✅ Excellent⚠️ Good (supporting)❌ Not ideal for real-time

Bottom Line

  • FPGAs are unmatched in ultra-low latency and deterministic execution, making them the weapon of choice for real-time market data handling and order execution in HFT.

  • CPUs handle the strategy logic and orchestration efficiently, serving as the control layer.

  • GPUs are rarely used in live trading, but can be valuable for offline analysis, deep learning, and pattern detection.

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