Scx_rusty Latency: Performance On 512-Core AMD System

by Alex Johnson 54 views

This article delves into a performance analysis comparing the scx_rusty scheduler against the kernel default scheduler (EEVDF) on a high-core-count AMD system. Specifically, we'll explore why scx_rusty exhibited higher latency under the schbench benchmark in a 512-core environment. This analysis is crucial for understanding the nuances of scheduler performance in modern multi-core systems and identifying scenarios where scx_rusty might excel or face challenges.

Background and Test Setup

The tests were conducted on a dual-socket AMD 9745 system, boasting a total of 512 logical CPUs. This system is characterized by 32 cores sharing a Last-Level Cache (LLC), making it a prime candidate for evaluating scheduler efficiency under heavy load. The kernel version used was 6.18-rc4, providing a relatively recent snapshot of the Linux kernel. The benchmark tool of choice was schbench, a utility designed for measuring scheduler performance.

Schbench was configured to simulate a multi-threaded workload, utilizing the following command: schbench -m 16 -t 32 -r 100. This command instructs schbench to run with 16 processes, each spawning 32 threads, and performing 100 scheduling operations. This setup aims to create a significant load on the scheduler, highlighting potential performance bottlenecks.

The core of the investigation lies in comparing the latency characteristics of the default kernel scheduler against scx_rusty. Latency, in this context, refers to the time it takes for a task to be scheduled and executed. Lower latency generally translates to better responsiveness and overall system performance.

Test Results: A Deep Dive into Latency Percentiles

The test results revealed a notable difference in latency between the two schedulers. Let's examine the key percentiles to understand the performance gap:

Default Scheduler Latency Percentiles (usec)

  • 50.0th: 37
  • 75.0th: 51
  • 90.0th: 64
  • 95.0th: 74
  • 99.0th: 2292
  • 99.5th: 3332
  • 99.9th: 3772
  • min=0, max=9923

scx_rusty Scheduler Latency Percentiles (usec)

  • 50.0th: 72
  • 75.0th: 101
  • 90.0th: 122
  • 95.0th: 138
  • 99.0th: 5032
  • 99.5th: 7320
  • 99.9th: 12048
  • min=0, max=20831

Analyzing these results, we observe that scx_rusty exhibits significantly higher latency across all percentiles, especially in the higher percentiles (99th, 99.5th, and 99.9th). This indicates that while the median latency (50th percentile) is higher for scx_rusty, the tail latency (99th percentile and above) is drastically increased. This is a critical observation, as tail latency often dictates the user experience in interactive applications and the performance of latency-sensitive workloads.

The significant increase in tail latency for scx_rusty suggests potential issues in handling edge cases or under high contention. It's crucial to investigate the underlying reasons for this behavior.

Core Questions and Potential Explanations

The observed latency disparity raises several important questions:

1. Why is there such a significant increase in latency (especially for 99th, 99.5th, and 99.9th percentiles) when using scx_rusty compared to the default scheduler?

Several factors could contribute to this increased latency:

  • Scheduling Algorithm Overhead: scx_rusty might employ a more complex scheduling algorithm than the default scheduler. While this complexity could offer benefits in specific scenarios, it might introduce overhead in general workloads, leading to higher latency. The intricacies of the scheduling decisions, such as task prioritization, CPU affinity, and load balancing, could contribute to the overall scheduling overhead.
  • Synchronization Overhead: Multi-core systems require synchronization mechanisms to manage shared resources and prevent race conditions. scx_rusty might utilize different synchronization primitives or strategies compared to the default scheduler. If these mechanisms are not optimized for the specific hardware architecture or workload, they could introduce significant latency, particularly under high contention.
  • Cache Locality Issues: Effective scheduling algorithms strive to maximize cache locality, ensuring that tasks are scheduled on CPUs where their data is readily available in the cache. If scx_rusty's scheduling decisions lead to frequent cache misses, the resulting memory access latency could significantly impact overall performance. The NUMA (Non-Uniform Memory Access) architecture of the AMD system, where memory access times vary depending on the location of the memory and the CPU core, could further exacerbate cache locality issues.
  • Context Switching Overhead: The frequency and cost of context switches, the process of switching the CPU's focus from one task to another, can significantly impact latency. scx_rusty might trigger more frequent context switches or incur a higher cost per context switch compared to the default scheduler. Understanding the context switching behavior of both schedulers is crucial for pinpointing potential bottlenecks.

2. Could there be an issue with my test scenario or configuration that leads to this performance gap?

The test setup and configuration play a vital role in the accuracy and relevance of performance benchmarks. Several aspects warrant scrutiny:

  • Workload Suitability: schbench's workload might not be representative of real-world applications. Different workloads exhibit varying characteristics, such as CPU-bound vs. I/O-bound, and varying degrees of parallelism. It's crucial to evaluate scx_rusty under a diverse range of workloads to assess its performance across different scenarios. Understanding the specific resource demands of the workload, such as CPU utilization, memory access patterns, and inter-process communication, is essential for interpreting the benchmark results.
  • Benchmarking Methodology: The methodology used to collect and analyze the latency data could influence the results. Factors such as the duration of the benchmark, the number of iterations, and the methods used to measure latency can introduce variability. Ensuring that the benchmarking methodology is sound and repeatable is crucial for obtaining reliable results.
  • System Configuration: The system configuration, including CPU frequency scaling, power management settings, and other kernel parameters, can impact scheduler performance. It's essential to ensure that the system is configured optimally for the target workload and that any potential performance-impacting settings are consistent across the two schedulers being compared. The BIOS settings, such as CPU power limits and memory timings, can also influence the overall system performance.

3. In which specific use cases or workload scenarios does scx_rusty typically excel and deliver better performance than the default scheduler?

scx_rusty, like any scheduler, is designed with specific goals and trade-offs in mind. Understanding these design goals is crucial for identifying scenarios where it might outperform the default scheduler:

  • Specific Workload Characteristics: scx_rusty might be optimized for workloads with specific characteristics, such as real-time applications, high-priority tasks, or workloads requiring strict service-level agreements (SLAs). These optimizations might come at the cost of performance in general-purpose workloads. Identifying the target workload profile for scx_rusty is essential for understanding its performance trade-offs.
  • System Resource Management: scx_rusty might excel in scenarios where efficient resource management is paramount. For instance, it might prioritize fairness among tasks, prevent resource starvation, or optimize energy consumption. These resource management strategies could be particularly beneficial in resource-constrained environments or in scenarios where fairness and predictability are critical.
  • Scalability: scx_rusty might be designed to scale efficiently to a large number of cores and CPUs. In highly parallel environments, the default scheduler might encounter scalability limitations, while scx_rusty could maintain performance by effectively distributing the workload across the available resources. Evaluating scx_rusty's scalability characteristics is crucial for understanding its applicability in modern multi-core systems.

Potential Solutions and Further Investigation

To understand the performance gap and identify potential solutions, several avenues of investigation are worth pursuing:

  • Profiling and Tracing: Utilizing profiling tools and tracing mechanisms can provide valuable insights into the behavior of the two schedulers. Profiling can identify CPU hotspots and performance bottlenecks, while tracing can reveal the sequence of scheduling decisions and context switches. Tools like perf, ftrace, and eBPF can be instrumental in this analysis.
  • Workload Parameter Tuning: Experimenting with different workload parameters within schbench can help isolate the factors contributing to the latency disparity. Varying the number of threads, processes, and scheduling operations can reveal the sensitivity of the two schedulers to different workload characteristics.
  • Scheduler Configuration: scx_rusty might offer configuration options that can be tuned to optimize performance for specific workloads. Exploring these configuration options and experimenting with different settings can potentially improve scx_rusty's latency characteristics.
  • Code Analysis: A deep dive into the source code of scx_rusty can provide valuable insights into its scheduling algorithms, synchronization mechanisms, and resource management strategies. Understanding the implementation details can help identify potential areas for optimization.

Conclusion

The initial results indicate that scx_rusty exhibits higher latency compared to the default scheduler under the schbench benchmark on a 512-core AMD system. This observation warrants further investigation to pinpoint the underlying causes and identify scenarios where scx_rusty might excel. By exploring the factors discussed in this article, such as scheduling algorithm overhead, synchronization overhead, cache locality issues, and workload suitability, we can gain a deeper understanding of scx_rusty's performance characteristics and its potential role in modern multi-core systems.

For more information on scheduler performance and benchmarking, visit Brendan Gregg's website. This site offers a wealth of resources on performance analysis and tuning.