AI × Quant Trader Series — Day 17¶
What is Shared Memory IPC?¶
Reading time: ~18 minutes
Prerequisites: Basic Operating Systems, What is Market Data, What is an Order Book, What is a Risk Engine
Focus: understanding one of the fastest communication mechanisms used in High Frequency Trading systems
Part 1: Introduction¶
Modern trading systems are rarely a single program.
Instead, they are composed of multiple specialized processes.
For example:
- Market Data Engine
- Strategy Engine
- Risk Engine
- Order Management System
- Execution Engine
- Logger
These processes constantly exchange information.
The question is simple:
How can processes communicate without introducing unnecessary latency?
For ordinary applications,
TCP sockets may be sufficient.
For High Frequency Trading,
they are often too slow.
This is where Shared Memory IPC becomes one of the most important technologies in modern trading infrastructure.
Part 2: What is Shared Memory IPC?¶
Shared Memory is an Inter-Process Communication (IPC) mechanism that allows multiple processes to access the same region of physical memory.
Instead of sending messages over the network,
multiple processes simply read and write the same memory.
Conceptually,
instead of this:
we have:
The operating system maps the same physical memory pages into multiple processes.
No data copying is required.
Part 3: Why Shared Memory Is Fast¶
Every communication mechanism has a cost.
Consider sending market data through a socket.
The operating system must:
- Copy user memory
- Enter kernel mode
- Copy kernel buffers
- Switch processes
- Copy data again
Even on modern hardware,
this introduces additional latency.
Shared Memory removes most of these steps.
Instead of transmitting messages,
both processes access the same bytes already stored in memory.
The result is:
- Lower latency
- Higher throughput
- Lower CPU usage
Part 4: Shared Memory in Trading Systems¶
A simplified HFT architecture might look like:
The Market Data Engine writes updates into shared memory.
Strategies immediately read the newest market state.
No serialization.
No network transmission.
No additional copies.
This architecture is one of the foundations of modern low-latency trading systems.
Part 5: Shared Memory vs Sockets¶
Both mechanisms transfer information.
Their design goals are very different.
TCP Socket¶
Advantages:
- Works across machines
- Reliable communication
- Easy to use
Disadvantages:
- Multiple memory copies
- Kernel overhead
- Higher latency
Shared Memory¶
Advantages:
- Zero network overhead
- Extremely low latency
- Very high throughput
Disadvantages:
- Same machine only
- Synchronization required
- More difficult to implement
Professional trading systems often combine both.
Sockets communicate with exchanges.
Shared memory distributes data internally.
Part 6: Typical Data Flow¶
Imagine a market data update arrives.
Without shared memory:
Every step copies data.
With shared memory:
Each process reads the same data directly.
No unnecessary transmission occurs.
Part 7: Ring Buffers¶
Shared memory alone is not enough.
Processes also need a way to organize messages efficiently.
One common solution is the Ring Buffer.
+----------------------------------+
| Msg | Msg | Msg | Msg | Msg |
+----------------------------------+
^ ^
Read Write
The write pointer moves forward as new messages arrive.
Readers consume messages independently.
Ring buffers are simple, cache-friendly, and highly efficient.
Many HFT systems rely on them for real-time communication.
Part 8: Synchronization¶
Because multiple processes access the same memory,
coordination becomes necessary.
Typical synchronization techniques include:
- Atomic variables
- Memory barriers
- Lock-free queues
- Sequence numbers
Professional systems avoid traditional mutexes whenever possible.
Blocking operations introduce unpredictable latency.
The goal is deterministic communication.
Part 9: Where Shared Memory Is Used¶
Shared memory appears throughout modern trading infrastructure.
Typical applications include:
- Market Data Distribution
- Local Order Book
- Risk Updates
- Position Information
- Order Events
- Logging
- Monitoring
- Performance Metrics
Almost every low-latency component exchanges information through shared memory.
Part 10: Engineering Challenges¶
Building a production shared memory system is more difficult than allocating memory.
Typical challenges include:
- Memory alignment
- Cache coherence
- False sharing
- NUMA awareness
- Process recovery
- Crash consistency
- Lock-free synchronization
A poorly designed shared memory system may perform worse than a well-designed socket implementation.
Architecture matters.
Part 11: Where godzilla.dev Fits¶
One of the core design principles of godzilla.dev is minimizing communication overhead between trading components.
Rather than passing market data through multiple network sockets or repeatedly copying messages between processes, the framework uses shared-memory-based communication to distribute information efficiently across the trading system.
This architecture offers several advantages:
- Lower latency
- Reduced CPU utilization
- Higher message throughput
- Cleaner separation between components
- Better scalability as additional strategies are introduced
By treating shared memory as the backbone of internal communication, godzilla.dev enables trading applications to process market events with predictable and consistent performance.
Part 12: Key Takeaways¶
Shared Memory IPC is one of the fastest communication mechanisms available for processes running on the same machine.
Instead of copying data between applications, multiple processes access the same memory directly.
For High Frequency Trading systems, this provides:
- Lower latency
- Higher throughput
- Better CPU efficiency
- More deterministic performance
Although more complex to implement than sockets, shared memory has become one of the fundamental building blocks of modern electronic trading infrastructure.
What's Next?¶
The next article explores the synchronization technique that makes high-performance shared memory systems possible:
- What is Lock-Free Programming?