EPSO: A Caching-Based Efficient Superoptimizer for BPF Bytecode
By: Qian Zhu , Yuxuan Liu , Ziyuan Zhu and more
Potential Business Impact:
Makes computer programs run faster and smaller.
Extended Berkeley Packet Filter (eBPF) allows developers to extend Linux kernel functionality without modifying its source code. To ensure system safety, an in-kernel safety checker, the verifier, enforces strict safety constraints (for example, a limited program size) on eBPF programs loaded into the kernel. These constraints, combined with eBPF's performance-critical use cases, make effective optimization essential. However, existing compilers (such as Clang) offer limited optimization support, and many semantics-preserving transformations are rejected by the verifier, which makes handcrafted optimization rule design both challenging and limited in effectiveness. Superoptimization overcomes the limitations of rule-based methods by automatically discovering optimal transformations, but its high computational cost limits scalability. To address this, we propose EPSO, a caching-based superoptimizer that discovers rewrite rules via offline superoptimization and reuses them to achieve high-quality optimizations with minimal runtime overhead. We evaluate EPSO on benchmarks from the Linux kernel and several eBPF-based projects, including Cilium, Katran, hXDP, Sysdig, Tetragon, and Tracee. EPSO discovers 795 rewrite rules and achieves up to 68.87 percent (average 24.37 percent) reduction in program size compared to Clang's output, outperforming the state-of-the-art BPF optimizer K2 on all benchmarks and Merlin on 92.68 percent of them. Additionally, EPSO reduces program runtime by an average of 6.60 percent, improving throughput and lowering latency in network applications.
Similar Papers
ASPO: Constraint-Aware Bayesian Optimization for FPGA-based Soft Processors
Hardware Architecture
Speeds up computer chip design by 74%.
gpu_ext: Extensible OS Policies for GPUs via eBPF
Operating Systems
Makes computer graphics run much faster.
Compiler-supported reduced precision and AoS-SoA transformations for heterogeneous hardware
Programming Languages
Speeds up computer simulations by changing how data is stored.