Low Latency Atomic Operations for JVM

This article presents an implementation of atomic operations optimized for low latency in multithreaded JVM environments. The implementation achieves approximately 2x lower latency under high concurrency through adaptive backoff mechanisms in compare and swap loops.

Implementation Strategy

The standard java.util.concurrent.atomic package implements operations such as getAndAdd and addAndGet using the lock addq CPU instruction or volatile variable increments, optimizing for single threaded contexts while degrading significantly under contention; all operations in this implementation, including increment and decrement variants, utilize compare and swap loops with adaptive backoff instead. The critical design decision involves inserting minimal thread suspension periods when a CAS operation fails to acquire the value, preventing wasteful spinning where threads continuously attempt updates while another thread holds the lock; this transforms CPU bound contention into a more efficient scheduling problem. The suspension mechanism requires careful calibration; excessive delays degrade performance in low contention scenarios, while insufficient delays fail to mitigate high contention pathologies.

Performance Characteristics

Performance analysis was conducted on an Intel(R) Xeon(R) CPU E5-1650 v3 @ 3.50GHz running OpenJDK 9.0.1 under Debian, comparing latency distributions across varying thread counts for both the standard java.util.concurrent.atomic implementation and the optimized variant.¹

Single threaded performance remains equivalent; the optimized implementation exhibits comparable latency to the standard library at all percentiles. As thread count increases, the backoff strategy provides substantial benefits; at 8 threads, median latency improves from 550ns to 402ns with the 99th percentile improving from 2'976ns to 1'612ns. The advantage becomes more pronounced at higher thread counts; with 64 concurrent threads, the 95th percentile latency improves from 4'848ns to 1'952ns, a reduction of approximately 60%.

JVM Sleep Mechanisms

Adaptive backoff requires suspending thread execution for minimal durations; the JVM provides several primitives for thread suspension, each with distinct performance characteristics and semantic guarantees. Platform dependent behavior, combined with highly variable relationships between requested and actual sleep duration, necessitates an adaptive approach rather than fixed sleep periods. The selected mechanism is Thread.yield() which instructs the scheduler to bypass the thread, effectively inserting a pause CPU instruction equivalent into the execution path.

Backoff Strategy Analysis

Detailed benchmarking across multiple backoff strategies reveals performance sensitivity to both the suspension primitive chosen and the number of iterations before suspension; the optimal strategy balances immediate retry attempts against thread suspension to minimize both busy waiting and scheduling overhead. Selected results for 16 concurrent threads:

• Plain CAS loop without backoff: median 921ns, p0.95 4'424ns
• Single Thread.onSpinWait() call: median 2'000ns, p0.95 2'772ns
• Single Thread.yield() call: median 2'240ns, p0.95 4'272ns
• Thread.onSpinWait() followed by LockSupport.parkNanos(1): median 1'368ns, p0.95 2'072ns

Pure spin waiting (Thread.onSpinWait() only) increases median latency but reduces tail latencies by preventing extended contention periods; combining spin waiting with minimal park durations provides the best balance, achieving lower median latencies than pure yielding while maintaining acceptable tail behavior. At 32 and 64 threads, performance characteristics shift; higher contention increases the value of more aggressive suspension strategies. With 64 threads, approaches using LockSupport.parkNanos() with values between 1 and 10 nanoseconds achieve median latencies around 1'200ns compared to 926ns for unoptimized code, but dramatically improve the 95th percentile from 4'848ns to approximately 2'000ns.

Afterword

Adaptive backoff in CAS loops provides substantial latency improvements in highly concurrent environments without degrading single threaded performance; the approximately 2x reduction in high percentile latencies at 64 concurrent threads validates the approach for applications requiring low latency atomic operations under contention.²