Crates.io | scx_rustland |
lib.rs | scx_rustland |
version | 1.0.16 |
created_at | 2023-12-22 08:20:19.819917+00 |
updated_at | 2025-09-05 23:15:44.815634+00 |
description | A BPF component (dispatcher) that implements the low level sched-ext functionalities and a user-space counterpart (scheduler), written in Rust, that implements the actual scheduling policy. This is used within sched_ext, which is a Linux kernel feature which enables implementing kernel thread schedulers in BPF and dynamically loading them. https://github.com/sched-ext/scx/tree/main |
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id | 1078180 |
size | 92,679 |
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
.
scx_rustland
is based on scx_rustland_core
, a BPF component that abstracts
the low-level sched_ext
functionalities. The actual scheduling policy is
entirely implemented in user space and it is written in Rust.
Available as a Rust crate: cargo add scx_rustland
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.
For performance-critical production scenarios, other schedulers are likely to exhibit better performance, as offloading all scheduling decisions to user-space comes with a certain cost (even if it's minimal).
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.
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.