379cc446e2
The monitoring kernel computed reward/action statistics for HEALTH_DIAG and
log output. Each of 8 warps used per-warp reduction via shfl, then the
warp's lane-0 atomicAdd-ed its partial into 14 shared scalars/arrays:
atomicAdd(&s_sum, ...) // per-trade reward sum
atomicAdd(&s_sq_sum, ...) // sum of squares
atomicMinFloat(&s_min, ...) // CAS-based atomic min
atomicMaxFloat(&s_max, ...)
atomicAdd(&s_nonzero, ...)
atomicAdd(&s_exp[i], ...) // per-action histogram
atomicAdd(&s_ord[i], ...)
atomicAdd(&s_urg[i], ...)
Atomic-into-shared is order-of-arrival → for floats, a few-ULP variance
across runs in mean/std/sharpe/min/max LOG values, even when the underlying
inputs were bit-identical.
Diagnostic non-determinism is still bad: it makes A/B comparisons of kernel
changes unreliable, and bisecting a numeric regression by training-log diff
becomes impossible.
Fix: standard "warp-id-keyed scratch + sequential reduction by tid 0":
__shared__ float w_sum[32], w_sq_sum[32], ... // one slot per warp
__shared__ int w_exp[32][9], ... // arrays too
if ((tid & 31) == 0) w_sum[warp_id] = local_sum; // one writer per slot
__syncthreads();
if (tid == 0) {
for (w = 0; w < num_warps; w++) total += w_sum[w]; // fixed order
...
}
Results are now bit-identical across runs given identical inputs. Shared
memory cost: ~2.6 KB (32 warps × ~80 bytes), well under the 48 KB limit.
Also deleted the now-unused atomicMinFloat / atomicMaxFloat helper
__device__ functions (top of the file). They were the only callers.
The 4 remaining call-site atomicAdds in the codebase (3 in
experience_kernels for atom_stats/penalty_out, 1 in dqn_utility for
sensitivity_out, 1 in trade_stats) are similar diagnostic-only paths.
Each follows the same cross-block hierarchical-atomicAdd pattern used in
the just-deleted ensemble_diversity_kernel; same fix would apply but
they're individual cleanup follow-ups.
Net behavior change: zero (training trajectory unaffected — diagnostic
output values are now stable across same-input runs).
Files touched:
crates/ml/src/cuda_pipeline/monitoring_kernel.cu (-26 / -33 +62)
Verified: SQLX_OFFLINE=true cargo check -p ml --lib --tests passes.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.