![]() If performance fluctuates over time, providing the benchmark with more time may allow it to stabilize and reveal the true performance of the algorithm. Extending benchmark durationĪnother option is to run benchmarks for a longer duration. Even if this method works today, it may become more challenging in the future. As hardware evolves, it becomes increasingly complex and intricate. It is difficult to guarantee that all influencing factors remain constant, and it is not a sustainable solution in the long run. While this approach can yield positive results, it is not foolproof. This includes measures such as disabling Turbo Boost and similar features, statically throttling the CPU to bypass dynamic power and thermal limitations, dedicating specific cores to the benchmark, and ensuring no other tasks are running concurrently. ![]() One approach involves exerting greater control over the system in which the benchmarks are executed. There are several approaches we are employing to tackle these challenges, each with its drawbacks. My suspicion is that the active recompilation process triggered by adding the dependency puts the CPU in a state where it experiences more aggressive thermal limitations. However, with each subsequent benchmark run, the performance gradually returns to normal. When running the benchmark after adding a dependency to Cargo.toml, the performance drops by around 10%.Could this be attributed to scheduling latency? It is worth noting that all benchmarks are single-threaded. Playing music in the background leads to a performance decrease of approximately 3-5%.Here are some observations I have encountered during performance benchmarking on my laptop: Interruptions from the operating system.Unpredictable latencies in the memory hierarchy caused by preemptive task execution and migration between NUMA nodes.Several factors contribute to the lack of reproducible results, including: Computer systems are complex and stateful, with various interactions between their components, such as hardware and the operating system.Īchieving complete control over the system state to ensure consistent performance across benchmark runs is nearly impossible. Why is benchmarking challenging?īenchmarking the performance of an algorithm is a difficult task due to the need for a controlled environment. While paired testing is a well-known statistical technique, as far as I am aware, it has not yet been implemented in any benchmarking tools.įinally, I present the experimental results and share the Rust source code for those interested in experimenting with this concept. Subsequently, I introduce an alternative method of performance testing called paired benchmarking, which effectively tackles some of these challenges. I provide a brief overview of efforts to address these challenges and highlight some limitations we’re encountering. In this article, I discuss the challenges associated with testing algorithm performance, focusing primarily on microbenchmarks rather than overall application performance, although some principles apply to both.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |