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Looking at perf top it seems that the scheduler can spend a significant amount of time iterating over the CPU topology/cpumask information, especially when the system is running a significant amount of tasks: 2.57% scx_rustland [.] <scx_utils::cpumask::CpumaskIntoIterator as core::iter::traits::iterator::Iterator>::next Considering that scx_rustland doesn't support CPU hotplugging yet (it requires a full restart to properly handle CPU hotplug events), we can completely avoid this overhead by caching a TopologyMap object at the beginning, when the scheduler starts, instead of constantly re-evaluating the CPU topology information. This allows to reduce the scheduler overhead by ~5% CPU utilization under heavy load conditions (from ~65% -> ~60%, according to top). Signed-off-by: Andrea Righi <andrea.righi@canonical.com> |
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scx_rustland
This is a single user-defined scheduler used within sched_ext, which is a Linux kernel feature which enables implementing kernel thread schedulers in BPF and dynamically loading them. Read more about sched_ext.
Overview
scx_rustland is made of a BPF component (scx_rustland_core) that implements the low level sched-ext functionalities and a user-space counterpart (scheduler), written in Rust, that implements the actual scheduling policy.
How To Install
Available as a Rust crate: cargo add scx_rustland
Typical Use Case
scx_rustland is designed to prioritize interactive workloads over background CPU-intensive workloads. For this reason the typical use case of this scheduler involves low-latency interactive applications, such as gaming, video conferencing and live streaming.
scx_rustland is also designed to be an "easy to read" template that can be used by any developer to quickly experiment more complex scheduling policies fully implemented in Rust.
Production Ready?
Not quite. For production scenarios, other schedulers are likely to exhibit better performance, as offloading all scheduling decisions to user-space comes with a certain cost.
However, a scheduler entirely implemented in user-space holds the potential for seamless integration with sophisticated libraries, tracing tools, external services (e.g., AI), etc.
Hence, there might be situations where the benefits outweigh the overhead, justifying the use of this scheduler in a production environment.
Demo
The key takeaway of this demo is to demonstrate that , despite the overhead of running a scheduler in user-space, we can still obtain interesting results and, in this particular case, even outperform the default Linux scheduler (EEVDF) in terms of application responsiveness (fps), while a CPU intensive workload (parallel kernel build) is running in the background.