Does Nim’s experimental ARC GC reduce latency spikes under high async load?

Short Answer

Nim's experimental ARC garbage collector can reduce maximum pause durations compared to the default Boehm GC in high-concurrency async workloads. Because ARC uses reference counting for immediate deallocation, it eliminates the "stop-the-world" pauses characteristic of tracing collectors. However, the overall latency profile depends on the allocation rate: each allocation and deallocation incurs reference-count overhead, which may increase baseline CPU usage and potentially raise average latency even as tail latency spikes shrink.

Likely Explanation: ARC vs. Boehm

The difference in latency behavior stems from how memory is reclaimed:

• Boehm (Tracing): Periodically halts all execution to trace the object graph. Under bursty async loads, a surge of short-lived objects can trigger frequent, unpredictable pauses that scale with the size of the live heap.
• ARC (Reference Counting): Increments and decrements counters on every assignment and scope exit. Objects are freed the moment their count reaches zero. This removes the global pause, smoothing out the latency distribution.

The trade-off is a shift from periodic spikes to constant overhead. In workloads involving heavy object churning (e.g., parsing large JSON payloads per request), the cumulative cost of reference-count maintenance may offset the benefits of removing pauses.

Impact on asyncdispatch

Nim's asyncdispatch event loop runs on a single OS thread. Switching to ARC does not change the fundamental scheduling of the loop:

• CPU-bound tasks: Async tasks that perform heavy computation without yielding (using await) will still monopolize the loop and cause latency spikes, regardless of the GC used.
• Predictability: ARC makes deallocation costs deterministic. While it doesn't prevent loop blocking, it ensures that the GC itself isn't the source of the block.

Stability and Production Constraints

ARC is experimental in Nim 2.x. Several factors may preclude its use in production:

• C Interop: Libraries expecting Boehm (e.g., those using GC_add_roots) may experience memory leaks or double-free errors.
• Feature Parity: Finalizers and weak references may behave differently or be unsupported compared to the Boehm implementation.
• Version Variance: Optimization passes for reference-count elision vary between point releases, meaning performance may shift between compiler versions.

Verification Steps

1. Compile the application with both GC strategies for comparison:

   # Boehm build
   nim c -d:boehm --gc:boehm myapp.nim
   
   # ARC build
   nim c -d:arc --gc:arc myapp.nim

2. Execute a load test mirroring your async burst pattern using a tool like wrk.
3. Compare the p99 and p999 latency metrics. A significant drop in tail latency with stable CPU usage indicates ARC is effective for your specific workload.
4. Use --gc:arc -p:arc to monitor reference-count operations and allocation rates during the test.

Missing Diagnostic Detail

What is the typical allocation rate per request (objects/bytes)? If requests allocate heavily, the reference-count overhead may dominate; if allocations are modest, ARC is significantly more likely to solve your latency spikes.