Why Lock Contention Matters in Low-Latency Trading Systems | Trading Engineering #8

폰테스 핀테크 :: 금융거래의 안전한 미래 2026. 9. 10. 21:27

Why Lock Contention Matters in Low-Latency Trading Systems

Trading Engineering Series — 08

In low-latency trading systems, synchronization is just as important as data transfer performance.

When multiple threads or CPU cores need to access shared data, synchronization mechanisms such as locks and atomic operations may be required to maintain data consistency.

However, excessive synchronization can introduce additional latency.

In particular, lock contention—when multiple execution units compete for the same lock—can become an important performance bottleneck in high-performance trading systems.

1. Why Are Locks Necessary?

Consider a situation where multiple threads need to modify the same piece of data.

For example:

Thread A → Order Quantity
Thread B → Order Quantity

If both threads modify the value simultaneously, the result may become inconsistent.

A lock can be used to ensure that only one thread accesses the shared data at a time.

Conceptually:

Lock → Access Shared Data → Unlock

Locks are an important tool for maintaining data consistency.

The problem is not the existence of locks themselves.

The important question is:

How often do execution units have to wait for a lock?

2. What Is Lock Contention?

Suppose several threads attempt to acquire the same lock:

Thread A → Lock → Processing

Thread B → Waiting

Thread C → Waiting

Thread D → Waiting

While Thread A holds the lock, the other threads may have to wait.

This situation is known as lock contention.

If the actual work protected by the lock is very short but the waiting time is relatively long, the synchronization overhead can become a significant part of the total latency.

This is particularly important in systems that repeatedly process large numbers of events.

3. Why Does It Matter in Trading Systems?

In many conventional applications, a difference of a few microseconds may not be particularly important.

Latency-sensitive trading systems are different.

Consider a simplified order path:

Market Data → Strategy → Signal → Risk Check → Order → FEP

If multiple stages on this path repeatedly wait for shared locks, the overall critical-path latency can increase.

The higher the message and order frequency, the more important these small overheads can become.

For this reason, low-latency system design is not only about using faster hardware.

It is also about reducing unnecessary waiting.

4. Reducing Locks on the Critical Path

This does not mean that every lock should be removed.

The objective is to identify unnecessary synchronization on the latency-critical path.

For example:

Market Data
↓
Lock
↓
Strategy
↓
Lock
↓
Order Manager
↓
Lock
↓
FEP

If every stage requires waiting for shared data, synchronization itself can become part of the latency.

One approach is to clearly define data ownership and reduce the amount of data that must be shared.

The goal is not simply:

“Use fewer locks.”

The goal is:

“Avoid unnecessary waiting.”

5. Keep the Lock Scope Small

When a lock cannot be avoided, reducing its scope is often important.

Instead of:

Lock → Perform Many Operations → Unlock

a better design may be:

Lock → Modify Required Data → Unlock

The longer a lock is held, the longer other threads may have to wait.

Operations such as:

Complex calculations
Network I/O
File I/O
Long loops
Unnecessary function calls

should generally be avoided while holding a latency-sensitive lock.

A critical section should be kept as small and predictable as possible.

6. The Single-Writer Approach

One way to reduce synchronization is to use a Single Writer design.

Instead of allowing multiple threads to modify the same data:

Multiple Writers → Shared Data

a system can assign ownership to a single writer:

Single Writer → Shared Data → Multiple Readers

With clear ownership, the amount of synchronization required can be reduced.

It can also make the data flow easier to reason about and the system behavior more predictable.

Of course, Single Writer is not suitable for every architecture.

The appropriate design depends on the data model and processing requirements.

7. Ring Buffers and Synchronization

As discussed in the previous article, Shared Memory and Ring Buffers can also play an important role in reducing synchronization overhead.

For example, a Single Producer / Single Consumer Ring Buffer clearly separates the producer and consumer responsibilities.

Producer
↓
Ring Buffer
↓
Consumer

When ownership and read/write positions are clearly defined, complex locking mechanisms may not be necessary.

This can be useful for high-frequency market data or trading signals.

8. Are Atomic Operations Always Better?

In some situations, atomic operations can reduce the need for traditional mutex-based synchronization.

They can be useful for managing simple state variables, counters, flags, and similar data.

However, atomic operations are not free.

When data is shared across CPU cores, cache coherence and memory ordering can also affect performance.

Therefore, it is too simplistic to assume:

Mutex = Slow
Atomic = Fast

The right choice depends on the actual access pattern and the bottleneck being addressed.

9. False Sharing

In multi-core systems, false sharing can also affect performance.

Two threads may be working with completely different variables, yet those variables may reside on the same CPU cache line.

For example:

Thread A → Variable A

Thread B → Variable B

Although the variables are logically independent, frequent updates to the same cache line can cause unnecessary cache-coherence traffic between CPU cores.

Therefore, optimizing synchronization is not only about reducing the number of locks.

It can also require careful consideration of:

Memory layout
Cache lines
Data locality
Core-to-core communication

10. Is Lock-Free Always Better?

The term lock-free is often associated with high-performance systems.

However, lock-free does not automatically mean better.

Removing locks can increase:

Algorithm complexity
Debugging difficulty
Memory-ordering complexity
Data-consistency risks
Operational complexity

In financial systems, correctness and reliability are often just as important as raw performance.

The objective is therefore not to eliminate every lock.

It is to:

Maintain the synchronization that is necessary while minimizing unnecessary waiting.

11. How Should Lock Contention Be Optimized?

The first step is to measure the system.

Useful metrics may include:

Lock acquisition time
Lock wait time
Lock hold time
Thread-level throughput
Context switches
CPU utilization
Cache-related performance
Critical-path latency

Once the actual bottleneck has been identified, the synchronization design can be improved.

A practical optimization cycle is:

Measure → Identify → Redesign → Benchmark

Without measurement, it is easy to optimize the wrong part of the system.

12. Our Approach at FontesFintech

At FontesFintech, when designing low-latency trading systems, we do not start by asking:

“How many locks should we use?”

Instead, we ask:

“Why does this data need to be shared?”

Our design considerations include:

Minimizing locks on the critical path
Reducing lock scope
Clear data ownership
Single Writer architecture where appropriate
Shared Memory
Ring Buffers
Appropriate use of atomic operations
Minimizing unnecessary shared data
Cache-aware memory design
End-to-end latency measurement

The objective is not to apply sophisticated technology simply because it is available.

The objective is to understand the actual data flow and remove unnecessary waiting from the system.

Conclusion

Synchronization is a fundamental part of reliable software, but it can also become a source of latency.

In low-latency trading systems, the key is not to eliminate locks blindly.

It is to minimize unnecessary contention while maintaining data consistency and system reliability.

Less Contention
Smaller Critical Sections
Clear Data Ownership
Efficient Synchronization

And one principle remains especially important:

Do not optimize what you cannot measure.

A well-designed low-latency trading system is not a system with no locks.

It is a system that synchronizes precisely where necessary and avoids unnecessary waiting wherever possible.