Hi all,

I’ve been experimenting with BIP324 v2 encrypted transport and wanted to 
share some measurements around its performance characteristics, focusing on 
throughput, latency, and batching effects.

The goal was not to propose changes, but to better understand where the 
actual costs are and how they scale under different execution models.
------------------------------
Setup
   
   - 
   
   Full BIP324 v2 stack implemented (ChaCha20-Poly1305 AEAD, HKDF-SHA256, 
   ElligatorSwift, session management)
   - 
   
   CPU: x86-64, clang-19, -O3
   - 
   
   GPU: RTX 5060 Ti (CUDA), batch-oriented execution model
   - 
   
   Measurements use median of multiple runs (RTDSCP timing)
   
------------------------------
CPU baseline (single-thread)

Mixed traffic profile:

   - 
   
   ~715K packets/sec
   - 
   
   ~221 MB/s goodput
   - 
   
   ~5.5% protocol overhead
   
Selected primitives:

   - 
   
   ChaCha20: ~780–840 MB/s
   - 
   
   Poly1305: ~1.5–2.2 GB/s
   - 
   
   AEAD encrypt: ~265–580 MB/s
   - 
   
   AEAD decrypt: ~232–587 MB/s
   
One-time operations:

   - 
   
   HKDF (extract+expand): ~286 ns
   - 
   
   ElligatorSwift create: ~53 µs
   - 
   
   ElligatorSwift XDH: ~30 µs
   - 
   
   Full handshake (both sides): ~172 µs
   
------------------------------
GPU offload (batch processing)

With batching (128K packets):

   - 
   
   ~12.78M packets/sec
   - 
   
   ~3.9 GB/s goodput
   - 
   
   ~17–18x throughput increase vs CPU
   
After optimizations (state reuse, instruction-level tuning, memory layout):

   - 
   
   ~21.37M packets/sec
   - 
   
   ~6.6 GB/s goodput
   - 
   
   ~30x throughput vs CPU
   
Overhead remains roughly the same (~5.5–5.6%).
------------------------------
Latency vs batching

A key observation is the strong dependence on batch size:

   - 
   
   1 packet: ~17.6 µs (launch + transfer dominated)
   - 
   
   64 packets: ~0.5 µs/packet
   - 
   
   1024+ packets: ~63 ns/packet
   
This suggests:

GPU behaves as a throughput engine, not a latency engine.

Small workloads are dominated by launch and transfer overhead, while large 
batches amortize these costs effectively.
------------------------------
PCIe / data movement effects

End-to-end profiling shows:

   - 
   
   Kernel execution: ~55–58% of total time
   - 
   
   PCIe transfer: ~42–45%
   
Effective end-to-end throughput stabilizes around:

   - 
   
   ~3.2–3.6 GB/s
   
This indicates that once crypto is sufficiently optimized, *data movement 
becomes the dominant bottleneck*, not the cryptographic primitives 
themselves.
------------------------------
Additional observations
   
   - 
   
   Decoy traffic overhead is relatively small on GPU:
   ~20% decoy rate results in only ~1.4% throughput drop
   - 
   
   Multi-stream execution (overlapping copy + compute):
   ~1.37x improvement vs single stream
   - 
   
   Optimal batch size appears to be in the 4K–16K packet range for this 
   setup
   
------------------------------
Takeaways
   
   1. 
   
   BIP324 cryptographic overhead on CPU is measurable but not extreme 
   (~5–6%)
   2. 
   
   Throughput can scale significantly with parallel execution (30x in this 
   setup)
   3. 
   
   Latency and throughput behave very differently depending on batching
   4. 
   
   Once crypto is fast enough, *transport becomes memory/IO bound*
   5. 
   
   Batch size and execution model are critical factors in performance
   
------------------------------
Open questions
   
   - 
   
   Are there realistic node-level scenarios where large batch sizes 
   naturally occur?
   - 
   
   Would transport-level batching be compatible with current peer/message 
   handling models?
   - 
   
   How relevant are throughput optimizations vs latency in real-world node 
   deployments?
   
------------------------------

I’m happy to share more details or run additional measurements if useful.

For reference, the implementation used for these measurements is available 
here:
https://github.com/shrec/UltrafastSecp256k1/tree/dev

  Best, Ivane Chkheidze    

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