How FPGAs compare to GPUs or CPUs in HFT?
ampheo3 min read
Here’s a direct comparison of FPGAs, GPUs, and CPUs in the context of High-Frequency Trading (HFT):
1. Latency
| Technology | Latency | Explanation |
| FPGA | ✅ Ultra-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)
| Technology | Determinism | Explanation |
| FPGA | ✅ Very deterministic | Fixed hardware logic yields consistent response times. |
| CPU | ❌ Low | Background processes, scheduling, and interrupts cause variability. |
| GPU | ❌ Very low | Thread and kernel management introduce significant jitter. |
3. Parallelism
| Technology | Parallelism | Explanation |
| FPGA | ✅ Fine-grained hardware-level parallelism | Can execute many logic paths simultaneously. |
| CPU | ⚠️ Limited | Few cores, with limited concurrency. |
| GPU | ✅ Massive data-level parallelism | Thousands of threads, but for large, similar workloads. |
4. Throughput (Volume of Data Processed)
| Technology | Throughput | Explanation |
| FPGA | ✅ High | Can handle line-rate data directly from NICs (e.g., 10–100 Gbps). |
| CPU | ⚠️ Medium | Good 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
| Technology | Flexibility | Explanation |
| FPGA | ❌ Low | Requires hardware description languages (VHDL/Verilog), long dev time. |
| CPU | ✅ High | Fast development using common languages (C++, Python). |
| GPU | ⚠️ Medium | Requires CUDA/OpenCL knowledge; suited for parallelizable algorithms. |
6. Best Use Cases in HFT
| Technology | Ideal Use Cases |
| FPGA | Ultra-low-latency market data parsing, order book management, order routing |
| CPU | Strategy logic, risk checks, logging, orchestration |
| GPU | Deep learning models, backtesting, pattern recognition (non-real-time) |
Summary Table
| Feature | FPGA | CPU | GPU |
| 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.
0
Subscribe to my newsletter
Read articles from ampheo directly inside your inbox. Subscribe to the newsletter, and don't miss out.
Written by