ORC - Vorsprung durch Algorithmen

Version 1.4 ships with the so-called ORC memory management algorithm. ORC is the existing ARC algorithm (first shipped in version 1.2) plus a cycle collector. That’s also where the name comes from – the “O” stands for a cycle and “RC” stands for “reference counting”, which is the algorithm’s foundation.

The cycle collector is based on the pretty well known “trial deletion” algorithm by Lins and others. I won’t describe here how this algorithm works – you can read the paper for a good description.

As usual, I couldn’t resist the temptation to improve the algorithm and add more optimizations: The Nim compiler analyses the involved types and only if it is potentially cyclic, code is produced that calls into the cycle collector. This type analysis can be helped out by annotating a type as acyclic. For example, this is how a binary tree could be modeled:

  Node {.acyclic.} = ref object
    kids: array[2, Node]
    data: string

Unfortunately, the overhead of the cycle collector can be measurable in practice. This annotation can be crucial in order to get ORC’s performance close to ARC’s.

An innovation in ORC’s design is that cyclic root candidates can be registered and unregistered in constant time O(1). The consequence is that at runtime we exploit the fact that data in Nim is rarely cyclic.


ARC is Nim’s pure reference-counting GC, however, many reference count operations are optimized away: Thanks to move semantics, the construction of a data structure does not involve RC operations. And thanks to “cursor inference”, another innovation of Nim’s ARC implementation, common data structure traversals do not involve RC operations either! The performance of both ARC and ORC is independent of the size of the heap.


To put some weight behind my words, I wrote a simple benchmark showing off these algorithmic differences. Please note that the benchmark was written to stress the differences between ORC and Nim’s other GCs; it’s not supposed to model realistic workloads (yet!).

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 import asynchttpserver, asyncdispatch, strutils, json, tables, streams # about 135 MB of live data: var sessions: Table[string, JsonNode] for i in 0 ..< 10: sessions[$i] = parseJson(newFileStream("1.json", fmRead), "1.json") var served = 0 var server = newAsyncHttpServer() proc cb(req: Request) {.async.} = inc served await req.respond(Http200, "Hello World") if served mod 10 == 0: when not defined(memForSpeed): GC_fullCollect() waitFor server.serve(Port(8080), cb)

Lines 10-18 are the “Hello World” asynchronous HTTP server example from Nim’s standard library.

In lines 4-6, we load about 135MB of JSON data into the global sessions variable. ORC never touches this memory after it has been loaded, even though it remains alive for the rest of the program run. The older Nim GCs do have to touch this memory. I compare ORC to Nim’s “mark and sweep” GC (M&S) as M&S performs best on this benchmark.

GC_fullCollect is called frequently in order to keep the memory consumption close to the 135MB of RAM that the program needs in theory.

With the “wrk” benchmarking tool I get these numbers:

Metric / algorithm ORC M&S
Latency (Avg) 320.49 us 65.31 ms
Latency (Max) 6.24 ms 204.79 ms
Requests/sec 30963.96 282.69
Transfer/sec 1.48 MB 13.80 KB
Max memory 137 MiB 153 MiB

That’s right, ORC is over 100 times faster than the M&S GC. The reason is that ORC only touches memory that the mutator touches, too. This is a key feature that allows reasoning about performance on modern machines. A generational GC could probably offer comparable guarantees. In fact, ORC can be seen as a generational and incremental GC with the additional guarantee that acyclic structures are freed as soon as they become garbage.

Now what happens when the aggressive GC_fullCollect calls are not done? I get these numbers:

Metric / algorithm ORC M&S (memForSpeed)
Latency (Avg) 274.84 us 1.49 ms
Latency (Max) 1.10 ms 46.41 ms
Requests/sec 34948.95 39561.97
Transfer/sec 1.67 MB 1.89 MB
Max memory 137 MiB 333 MiB

M&S now wins in throughput, but not in latency. However, the memory consumption rises to about 330MB; more than twice as much memory as the program really requires!

ORC always wins on latency and memory consumption; plays nice with destructors, and hence with custom memory management; is independent of the heap sizes; tracks stack roots precisely and works cleanly with all sanitizers the C/C++ ecosystem offers.

These results are typical for what we see in other programs: Latency is reduced, there is little jitter and the memory consumption remains close to the required minimum that a program needs. Excellent results for embedded development!

Further advancements to the cycle collection algorithm itself are in development; it turns out there are lots of ideas that the GC research overlooked. Exciting times for Nim!


To compile your code with ORC, use --gc:orc on the command line.

  • ORC works out of the box with Valgrind and other C++ sanitizers. (Compile with --gc:orc -g -d:useMalloc for precise Valgrind checking.)
  • ORC uses 2x less memory than classical GCs.
  • ORC can be orders of magnitudes faster in throughput when memory consumption is important. It is comparable in throughput when memory consumption is not as important.
  • ORC uses no CPU specific tricks; it works without hacks even on limited targets like Webassembly.
  • ORC offers sub-millisecond latencies. It is well suited for (hard) realtime systems. There is no “stop the world” phase.
  • ORC is oblivious to the size of the heap or the used stack space.

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