Why Memory Copy Matters in Low-Latency Trading Systems | Trading Engineering #9

Why Memory Copy Matters in Low-Latency Trading Systems

Trading Engineering Series — 09

In a low-latency trading system, performance is often discussed in terms of CPU speed, network latency, or message-processing time.

However, there is another important factor that is sometimes overlooked:

How many times does the data have to move?

A trading system continuously moves data between network interfaces, processes, threads, and components. Every unnecessary memory copy adds work to the critical path.

This article explains why memory copy matters and how efficient data movement can improve the performance of low-latency trading systems.

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1. Data Continuously Moves Through the Trading System

A typical trading system may have a data path such as:

Network → FEP → Application → Strategy → Risk Check → Order Manager → FEP → Network

Market data enters through the network and passes through multiple components.

An order generated by a strategy follows the opposite direction toward the exchange.

At each stage, data may be copied into another buffer or converted into another data structure.

One copy may seem insignificant.

But when the same operation happens thousands or millions of times per second, the accumulated cost becomes significant.

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2. Why Does Memory Copy Cost Performance?

A memory copy is not free.

It consumes CPU instructions and moves data through the memory hierarchy.

Depending on the system architecture, repeated copying can increase:

• CPU cycles
• Memory bandwidth usage
• Cache traffic
• Cache misses
• Cache-line movement
• Memory latency

For a low-latency system, these costs can directly affect the time required to process a message.

The important point is not simply:

“How fast is memcpy()?”

The more important question is:

“How many times do we need to copy this data?”

Reducing unnecessary data movement is often more effective than optimizing a single copy operation.

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3. Small Messages Can Still Create a Large Cost

Trading messages are often relatively small.

This may make memory copying appear unimportant.

However, trading systems process messages at very high frequencies.

For example:

1 KB × 1,000,000 messages = approximately 1 GB of data movement

And that is only one copy.

If the same data is copied several times across different components, the amount of memory traffic can increase rapidly.

Therefore, in high-throughput trading systems, message frequency can be just as important as message size.

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4. Process-to-Process Data Transfer

Memory copying becomes particularly important when data moves between processes.

A conventional IPC design may involve:

Process A → Kernel/IPC Buffer → Process B

Depending on the IPC mechanism, additional copying, synchronization, or context switching may be involved.

For a latency-sensitive application, these operations can become part of the critical path.

This is one reason why high-performance trading systems often pay close attention to how data is exchanged between processes.

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5. Shared Memory Can Reduce Unnecessary Copies

One common approach is Shared Memory.

Instead of repeatedly copying data between processes, multiple processes can access a shared memory region.

For example:

Market Data Process → Shared Memory → Strategy Process

The data can remain in the shared region while the producer and consumer coordinate access to it.

Combined with a Ring Buffer, this approach can provide an efficient communication path for high-throughput data.

However, Shared Memory does not automatically make a system faster.

Synchronization, memory ordering, cache behavior, data ownership, and recovery mechanisms must also be carefully designed.

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6. What Does “Zero-Copy” Really Mean?

The term Zero-Copy is often used in high-performance systems.

It is important not to interpret it too literally.

In practice, the goal is often not to eliminate every possible memory copy.

Instead, the goal is:

Remove unnecessary copies from the latency-critical data path.

Some copies may still be useful.

For example, copying data can provide:

• Clear ownership
• Process isolation
• Simpler memory management
• Better fault isolation
• Easier debugging

Therefore, blindly eliminating every copy can actually make a system more complicated without producing meaningful performance gains.

The right question is:

Which copies are necessary, and which copies are simply overhead?

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7. Efficient Buffer Design

Efficient memory usage starts with good buffer design.

Several techniques are commonly useful in low-latency systems:

Fixed-Size Structures

Using predictable, fixed-size structures can simplify memory management and reduce dynamic allocation.

Buffer Reuse

Instead of repeatedly allocating and freeing memory, buffers can be reused.

Memory Pools

A memory pool can reduce the overhead and unpredictability associated with frequent dynamic allocation.

Ring Buffers

Ring buffers are useful for high-throughput producer-consumer communication.

Clear Data Ownership

Clearly defining who owns and modifies a piece of data can reduce unnecessary synchronization and copying.

The objective is not simply to use fewer buffers.

The objective is to design a data path where data moves only when it needs to move.

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8. Data Movement Is More Than memcpy()

When analyzing memory-related latency, it is easy to focus only on memcpy().

But the real cost of moving data can include much more:

• Cache Miss
• Cache-Line Transfer
• IPC
• Context Switch
• Synchronization
• Memory Allocation
• Memory Deallocation
• Cache Coherency Traffic
• Memory Bandwidth Pressure

For example, eliminating one memcpy() may have little effect if the system still performs multiple context switches and synchronization operations around that data.

Therefore, optimization should focus on the entire data path, rather than one function.

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9. Not Every Memory Copy Should Be Removed

There is an important engineering trade-off here.

Removing a copy may improve latency, but it can also increase coupling between components.

For example:

Shared Buffer

may provide better performance, but requires careful management of:

• Data ownership
• Synchronization
• Process failure
• Buffer state
• Memory visibility
• Recovery

In some situations, a copy is a reasonable price to pay for simplicity and isolation.

Therefore:

Do not remove copies simply because they exist. Remove unnecessary copies from the critical path.

This principle is especially important when designing production trading systems.

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10. CPU Cache Also Matters

Memory performance cannot be discussed without considering the CPU cache.

Modern CPUs operate through multiple levels of cache, and accessing data already present in the cache is generally much faster than accessing main memory.

Poor data layout or excessive data movement can increase:

• Cache Misses
• Cache-Line Transfers
• Cache Coherency Traffic
• Memory Access Latency

For high-performance trading applications, data structures should therefore be designed with cache behavior in mind.

This includes considerations such as:

• Data locality
• Structure layout
• Alignment
• Cache-line size
• False Sharing
• Producer/consumer access patterns

Memory optimization is therefore closely connected to CPU architecture.

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11. Applying This to a Trading System

Consider a simplified low-latency trading architecture:

Market Data
↓
Shared Memory / Ring Buffer
↓
Trading Strategy
↓
Pre-Trade Risk Check
↓
Order Manager
↓
DMA / FEP
↓
Exchange

At each stage, we should ask:

• Does this component really need to copy the data?
• Can the existing buffer be reused?
• Can Shared Memory be used?
• Can a Ring Buffer reduce synchronization overhead?
• Is dynamic allocation occurring on the critical path?
• Is the data structure cache-friendly?
• Who owns the data?

These questions can reveal performance bottlenecks that are not obvious from CPU utilization or network latency alone.

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12. Our Approach at FontesFintech

At FontesFintech, we consider memory movement as part of the overall trading-system architecture.

Our approach includes:

• Minimizing unnecessary memory copies
• Efficient IPC design
• Shared Memory
• Ring Buffer
• Buffer reuse
• Fixed-size data structures
• Memory allocation optimization
• Cache-aware data structures
• Lock and synchronization minimization
• Clear data ownership
• End-to-end latency measurement

Rather than optimizing a single component in isolation, we analyze the complete data path from market data input to order execution.

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Conclusion

Low-latency performance is not simply about having a faster CPU or a faster network.

It is also about how efficiently data moves through the system.

The key principles are:

Less Copy
Less Allocation
Less Data Movement

Zero-Copy does not mean that every memory copy must disappear.

The real goal is to eliminate unnecessary data movement from the latency-critical path while maintaining reliability, maintainability, and clear system architecture.

Ultimately, a high-performance trading system should move the required data to the required place with the lowest possible cost.

Efficient Trading Starts with Efficient Data Movement.

FontesFintech
Trading System · DMA · FEP · Algorithmic Trading · Risk Management