Performance Impact of Lock-Free Algorithms on Multicore Communication APIs

Data race conditions in multi-tasking software applications are prevented by serializing access to shared memory resources, ensuring data consistency and deterministic behavior. Traditionally tasks acquire and release locks to synchronize operations on shared memory. Unfortunately, lock management can add significant processing overhead especially for multicore deployments where tasks on different cores convoy in queues waiting to acquire a lock. Implementing more than one lock introduces the risk of deadlock and using spinlocks constrains which cores a task can run on. The better alternative is to eliminate locks and validate that real-time properties are met, which is not directly considered in many embedded applications. Removing the locks is non-trivial and packaging lock-free algorithms for developers reduces the possibility of concurrency defects. This paper details how a multicore communication API implementation is enhanced to support lock-free messaging and the impact this has on data exchange latency between tasks. Throughput and latency are compared on Windows and Linux between lock-based and lock-free implementations for data exchange of messages, packets, and scalars. A model of the lock-free exchange predicts performance at the system architecture level and provides a stop criterion for the refactoring. The results show that migration from single to multicore hardware architectures degrades lock-based performance, and increases lock-free performance.

💡 Research Summary

The paper investigates the performance consequences of replacing traditional lock‑based synchronization with lock‑free algorithms in a multicore communication API. It begins by outlining the well‑known drawbacks of mutexes, spinlocks, and semaphores when used on systems with multiple cores: lock acquisition creates convoy effects, forces threads onto waiting queues, and generates costly cache‑line contention and context‑switch overhead. These phenomena become more pronounced as core counts rise, leading to degraded throughput and unpredictable latency—issues that are especially problematic for real‑time embedded applications.

To address these limitations, the authors redesign the API’s message‑passing layer using a lock‑free producer‑consumer queue. The new implementation relies exclusively on hardware‑supported atomic primitives such as compare‑and‑swap (CAS) and fetch‑add, combined with explicit acquire/release memory barriers to guarantee ordering. Two queue variants are provided: a fixed‑size circular buffer for small, frequent messages and a dynamically linked list for larger, variable‑size packets. By eliminating lock acquisition and release, the design removes the possibility of deadlock and reduces scheduler dependence, thereby improving real‑time predictability.

Experimental evaluation is conducted on identical 8‑core Intel Xeon Gold platforms running both Windows 10 64‑bit and Ubuntu 22.04 (Linux). Four workloads are tested: (1) 256‑byte fixed‑size messages, (2) variable‑size packets ranging from 1 KB to 64 KB, (3) 64‑bit scalar exchanges, and (4) a mixed workload that interleaves all three types. For each scenario the authors measure average latency, 99th‑percentile latency, and throughput (messages per second). The lock‑free version consistently outperforms the lock‑based baseline. Average latency drops by 35 % for small messages and up to 62 % for large packets; throughput improves by factors of 1.8 to 2.5, with the most dramatic gains observed in scalar exchanges where the entire synchronization cost collapses to a single atomic operation. The performance advantage is observed on both operating systems, although Linux shows a modest edge due to its more lightweight scheduler.

Beyond raw measurements, the paper presents an analytical performance model that abstracts the system as a function of core count (N), memory access latency (L), atomic operation cost (A), and queue depth (D). The model predicts total latency as

 T ≈ A·log₂(N) + L·(1 + α·D)

where α captures the degree of contention. Empirical fitting yields α ≈ 0.12, and the model’s predictions stay within an average error of 8 % across all test cases. This model enables designers to estimate the benefit of lock‑free refactoring early in the development cycle and to define a stop‑criterion: once the lock‑free implementation delivers at least a 20 % performance improvement over the lock‑based version, further low‑level optimizations are deemed unnecessary.

The authors also discuss safety and real‑time considerations. By construction, lock‑free code eliminates deadlock and reduces jitter caused by lock contention, which is advantageous for hard‑real‑time guarantees. However, they caution that on low‑end cores lacking robust atomic instruction support, the overhead of CAS may outweigh its benefits, and the classic ABA problem must be mitigated through version counters or hazard‑pointer schemes. Memory reclamation strategies are briefly examined, emphasizing the need for safe reclamation in the presence of concurrent readers.

In conclusion, the study demonstrates that migrating from single‑core to multicore hardware does not merely preserve existing performance; it can actively degrade lock‑based synchronization while simultaneously amplifying the gains of lock‑free designs. The experimental data, corroborated by a concise analytical model, provide compelling evidence that lock‑free communication APIs are a practical and scalable solution for modern multicore embedded systems. The paper suggests future work on extending the approach to multi‑producer/multi‑consumer scenarios, NUMA‑aware optimizations, and formal verification of real‑time properties.