The author experienced extraordinarily high CPU utilization (3200%) on their Linux system, far exceeding the expected maximum for their 8-core processor. After extensive troubleshooting, including analyzing process lists, checking for kernel issues, and verifying hardware performance, the culprit was identified as a bug in the docker stats command itself. The command was incorrectly multiplying the CPU utilization by the number of CPUs, leading to the inflated and misleading percentage. Once the issue was pinpointed, the author switched to a more reliable monitoring tool, htop, which accurately reported normal CPU usage. This highlighted the importance of verifying monitoring tool accuracy when encountering unusual system behavior.
This blog post details a fascinating journey of troubleshooting perplexing CPU utilization on a Linux server. The author, Joseph Mate, begins by describing the initial observation of an astonishing 3200% CPU usage, a figure far exceeding the expected capacity of the server's 8-core processor. This anomalous reading prompted an investigation into the underlying cause.
The initial suspicion fell upon a potential runaway process consuming excessive resources. However, standard tools like top and htop failed to identify any single culprit responsible for such a dramatic spike in CPU usage. Each process appeared to be consuming a reasonable amount of resources individually.
Further investigation using more granular performance monitoring tools like perf began to reveal a more nuanced picture. perf pointed towards a high volume of system calls related to timekeeping functions, specifically gettimeofday and clock_gettime. This suggested that an excessive number of these calls were being made, potentially contributing to the inflated CPU utilization figures.
The author then meticulously analyzed the codebase of the running application, a Rust-based program. Despite the absence of any obvious loops or excessive calls to time functions within the application's logic, the investigation persisted. Suspicion then shifted towards potential interactions with external libraries or dependencies.
Through rigorous profiling and tracing, the root cause was finally unearthed. It was discovered that the application's logging library, specifically the tracing crate, was inadvertently configured to capture timestamps with nanosecond precision for every single log event. This extremely high-resolution timekeeping, while seemingly innocuous, resulted in a substantial overhead due to the sheer volume of logging operations performed by the application. Each call to capture a timestamp with nanosecond precision involved multiple system calls to the underlying timekeeping functions, ultimately accounting for the observed surge in CPU utilization.
By modifying the logging configuration to use less granular timestamps (millisecond precision), the author observed a dramatic reduction in CPU load, bringing the utilization back down to expected levels. The post concludes by highlighting the importance of careful consideration of logging configurations, especially concerning the precision of timestamps, as seemingly minor details can have a profound impact on overall system performance, particularly in high-throughput applications. The case serves as a cautionary tale about the potential performance pitfalls associated with overly aggressive logging practices.
Summary of Comments ( 117 )
https://news.ycombinator.com/item?id=43207831
Hacker News users discussed the plausibility and implications of 3200% CPU utilization, referencing the original author's use of Web Workers and the browser's ability to utilize multiple threads. Some questioned if this was a true representation of CPU usage or simply a misinterpretation of metrics, suggesting that the number reflects total CPU time consumed across all cores rather than a percentage exceeding 100%. Others pointed out that using
performance.now()instead ofDate.now()for benchmarks is crucial for accuracy, especially with Web Workers, and speculated on the specific workload and hardware involved. The unusual percentage sparked conversation about the potential for misleading performance measurements and the nuances of interpreting CPU utilization in multi-threaded environments like browsers. Several commenters highlighted the difference between wall-clock time and CPU time, emphasizing that the former is often the more relevant metric for user experience.The Hacker News post "3,200% CPU Utilization" generated a fair number of comments discussing the linked blog post about achieving extremely high CPU utilization with a custom-built prime number generator. The discussion revolves primarily around the nuances of CPU utilization reporting, the efficiency of the prime-finding algorithm, and the relevance of the benchmark itself.
Several commenters pointed out that exceeding 100% CPU utilization is expected on multi-core systems. One commenter explained that on a 32-core system, 3200% utilization represents all cores running at 100%, which isn't unusual or inherently problematic. This clarifies that the title, while attention-grabbing, might be misinterpreted by those unfamiliar with this aspect of system monitoring.
A significant portion of the discussion focuses on the efficiency of the prime-finding algorithm used in the benchmark. Some commenters questioned whether the algorithm is genuinely optimized, suggesting potential improvements and alternative approaches. One comment proposed using a segmented Sieve of Eratosthenes for improved performance, arguing that the demonstrated approach might not be the most efficient way to generate primes. This sparked a back-and-forth about the practical benefits of different sieving methods and the optimal approach for maximizing CPU usage.
Several commenters questioned the value and relevance of the benchmark itself. Some argued that achieving high CPU utilization is not inherently useful and doesn't necessarily reflect real-world performance gains. They pointed out that without a comparative benchmark against existing prime-finding algorithms, the 3200% figure is essentially meaningless in terms of performance evaluation. This led to a discussion about the purpose of such benchmarks and whether they accurately represent practical application scenarios.
The practicality of using Go for CPU-bound tasks also emerged as a discussion point. Commenters debated the suitability of Go's garbage collection and runtime characteristics for performance-critical computations. One user questioned the choice of Go, given its known performance limitations compared to languages like C or C++ for such computationally intensive tasks.
Finally, some commenters offered suggestions for further optimizing the code and the benchmark itself. These include utilizing SIMD instructions, optimizing memory access patterns, and comparing the performance against established libraries like primesieve. This feedback highlights the collaborative nature of Hacker News, where users contribute ideas and expertise to refine and improve projects.