NEW RESEARCH REPORT: Overcoming the limitations of coding agents in complex software systems
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Undo Performance Benchmarks
The table below shows benchmarks of Undo’s recording technology on several common applications, showing:
Runtime (native) – the original workload, running without recording
Runtime (record) – the same workload, recorded by Undo
Once a deterministic recording has been captured by Undo, a bug can be efficiently time travel debugged to resolve critical issues with orders of magnitude less engineering time.
Workload | Runtime (native) | Runtime (record) | Overall slowdown | Per-thread slowdown |
Mixed syscall workload sqlite – 100,000 insertions | 3.8s | 9.8s | 2.6x | 2.6x |
Compute-intensive workload gzip – 128MB input file | 4.8s | 6.5s | 1.33x | 1.33x |
Compute-intensive workload ffmpeg – VP9 to H264 transcode | 1.8s | 8.4s | 4.8x | ~1.6x |
In these results, the per-thread slowdown is kept low (at 1.33 – 2.9x) by Undo’s dynamic just-in-time instrumentation, which captures only the minimum data required to replay the process.
Multithreaded workloads may see an additional overall slowdown since Undo serializes their execution (using pre-emptive scheduling, so that race conditions are still reproduced). Compute-intensive applications like ffmpeg (shown above) show the most overhead due to this serialization. Common uses of threads for IO concurrency show less impact.
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