Line-Rate Post-Quantum Byzantine Consensus via L1D Balanced Ternary

This repository contains the reference eBPF/XDP drivers, Triton GPU lookup kernels, and microbenchmarking suites for the paper:

"Radix Economy and Balanced Ternary Microarchitectures: Resolving the Memory Wall in Line-Rate Post-Quantum Consensus and Nanoscale Computing"

Target Venues: SOSP / OSDI / ISCA / ASPLOS

64-Byte L1D Cache-Resident Frame: Compresses a 128-node consensus vote bitmask to 26 bytes ($3^5 = 243 \le 256$ ), enabling the entire synchronous descriptor (epoch + BLAKE3 accumulator + status flags) to fit within exactly one 64-byte L1D cache line.

Deterministic Wire Latency: Evaluated via eBPF XDP at 100GbE line-rate (99.2 Mpps aggregate across 8 queues, 12.4 Mpps/core) with $p50 = 40.0\text{ ns}$ and$p99.9 = 60.0\text{ ns}$ , safely below the$80.5\text{ ns}$ frame budget.

- State-Crypt Separation: Decouples the 64-byte synchronous consensus frame from asynchronous ML-DSA-44 (NIST FIPS 204) signature verification offloaded over 2MB hugepage lock-free SPSC rings.

- Conflict-Free GPU Decompression: Triton kernel maps 5-trit packed bytes into FP16 ternary weights with zero shared-memory bank conflicts using single-cycle hardware broadcast addressing.

- xdp_ternary_filter.c- Production eBPF XDP C driver for line-rate packet parsing, SipHash-2-4 pre-authentication, monotonic epoch tracking, and fast-path quorum accumulation.

- triton_lut_kernel.py- Triton GPU kernel for high-throughput 5-trit decompression on Tensor Cores.

- benchmark_harness.py- Microarchitectural evaluation reproducing the latency and throughput ablations across 64-byte ternary and 96-byte binary frames.

- LICENSE- MIT License.

# Requires clang and libbpf

clang -O2 -target bpf -c xdp_ternary_filter.c -o xdp_ternary_filter.o

# Attach to your 100GbE network interface (e.g. eth0) in native XDP mode

ip link set dev eth0 xdpgeneric obj xdp_ternary_filter.o sec xdp# Requires PyTorch and Triton

python3 triton_lut_kernel.pypython3 benchmark_harness.py@article{grimm2026radix,

title={Radix Economy and Balanced Ternary Microarchitectures: Resolving the Memory Wall in Line-Rate Post-Quantum Consensus and Nanoscale Computing},

author={Grimm, Justin},

year={2026}