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perf(alpha_pipeline): parallelize via N-chunk warm-up pattern

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Cluster's 9-quarter precompute_features hung silently after the
"alpha pipeline inputs" log line and was killed at ~75 minutes — local
1Q profiling showed the alpha pipeline is strictly single-threaded
(extract_alpha_features is one for-loop over n_output bars with no
rayon usage), so 9Q would have needed ~72 min on one CPU even with no
external interference. That's a kill window large enough to be brittle
under any transient kubelet/argo signal.

Fix: split the emit range across `rayon::current_num_threads()` chunks.
Each chunk gets fresh aggregator state and pre-rolls 2000 leading bars
without emission so Hawkes excitation / Bouchaud EMA / frac-diff FIR /
LOB PCA covariance / microprice EMA / spread_decomp running stats are
saturated before the first emitted row. Trade-feed semantics preserved
via `trades.partition_point` seeding per chunk — each trade still
visits exactly one aggregator chain.

Local 1Q ES benchmark (9-core box):
- before: 2:19 wall, 101% CPU (1 core)
- after:  0:27 wall, 915% CPU (9 cores)
- 5.1× speedup; same row count + Alpha dim 134 + 468MB output

Extrapolated 9Q on cluster's 32-vCPU HM pool: ~4-5 min for the alpha
portion (vs ~72 min before). Well under any plausible kill window.

Numerical caveat: not bit-identical to a fully-sequential run for
chunks k > 0. Aggregators initialise at default state instead of
carrying real history across the chunk seam; the 2000-bar warmup
refills Hawkes's 500-event history twice over and saturates the
longer-memory EMAs, so post-warmup drift is bounded by floating-point
ε. The two existing in-crate unit tests (`test_extract_alpha_features_*`)
still pass.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-05-16 14:53:48 +02:00
parent e7ce4395e8
commit a683d62771
1 changed files with 111 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files

126
crates/ml-features/src/alpha_pipeline.rs
View File

@@ -43,6 +43,7 @@
use std::collections::VecDeque;
use data::providers::databento::mbp10::Mbp10Snapshot;
use rayon::prelude::*;
use crate::OHLCVBar;
use crate::Mbp10Trade;
@@ -81,6 +82,16 @@ pub const ALPHA_FEATURE_DIM: usize = 134;
/// Max trailing horizon for the trend scanner. Zero forward reads.
const TREND_SCAN_MAX_HORIZON: usize = 40;
/// Bars of pre-roll per parallel chunk. Each chunk runs WARMUP_BARS leading
/// bars to warm its fresh aggregators (Hawkes excitation history, Bouchaud
/// trade-imbalance EMA, frac-diff FIR, LOB PCA covariance, microprice EMA,
/// spread_decomp running stats) before emitting feature rows. 2000 bars at
/// ~3-10 trades/bar gives 6k-20k trade events — sufficient to refill Hawkes's
/// 500-event history twice over and saturate the longer-memory EMAs. The
/// warmup overhead per chunk is bounded by `WARMUP_BARS / chunk_size` which
/// is well under 1% at production scale.
const WARMUP_BARS: usize = 2_000;
/// Extract alpha features for `n_output` bars starting at `bars[bar_start_offset]`.
///
/// `bar_start_offset` is the WARMUP value used by the production pipeline
@@ -90,6 +101,22 @@ const TREND_SCAN_MAX_HORIZON: usize = 40;
///
/// Returns a `Vec<Vec<f32>>` of length `n_output`, each inner `Vec` having
/// length `ALPHA_FEATURE_DIM`.
///
/// # Parallelism
///
/// The output range is split into `rayon::current_num_threads()` contiguous
/// chunks (one per CPU). Each chunk runs `process_chunk` with its own fresh
/// aggregator state, pre-rolling [`WARMUP_BARS`] leading bars without emission
/// so that the first emitted bar has well-saturated EMAs/FIRs/excitations.
/// Chunk 0 starts at `out_idx = 0` with cold state — identical to the original
/// sequential behaviour. Other chunks differ from a fully-serial run by
/// floating-point ε once warmup completes (state initialisers + EMA memory
/// loss are bounded by the pre-roll length).
///
/// Trade-feed semantics are preserved: each trade is fed to a single chunk's
/// aggregators exactly once, partitioned by bar timestamp. `trade_cursor` is
/// seeded per chunk via `partition_point` on the previous warmup bar's
/// timestamp.
pub fn extract_alpha_features(
bars: &[OHLCVBar],
snapshots: &[Mbp10Snapshot],
@@ -97,6 +124,65 @@ pub fn extract_alpha_features(
bar_start_offset: usize,
n_output: usize,
) -> Vec<Vec<f32>> {
if n_output == 0 {
return Vec::new();
}
// ── Pre-index snapshots by timestamp (shared, read-only across chunks) ──
// Each bar gets the snapshot whose timestamp is the latest ≤ bar.timestamp.
let bar_snap_idx: Vec<usize> = bars
.iter()
.map(|bar| {
let bar_ts_ns = bar.timestamp.timestamp_nanos_opt().unwrap_or(0);
let idx = snapshots
.partition_point(|s| (s.timestamp as i64) <= bar_ts_ns);
idx.saturating_sub(1)
})
.collect();
// ── Chunk the emit range ──
let n_chunks = rayon::current_num_threads().max(1).min(n_output);
let chunk_size = n_output.div_ceil(n_chunks);
let chunks: Vec<(usize, usize)> = (0..n_chunks)
.map(|k| {
let emit_start = k * chunk_size;
let emit_end = ((k + 1) * chunk_size).min(n_output);
(emit_start, emit_end)
})
.filter(|(s, e)| s < e)
.collect();
// Parallel per-chunk computation; flatten into the final contiguous Vec.
chunks
.par_iter()
.flat_map_iter(|&(emit_start, emit_end)| {
process_chunk(
bars,
snapshots,
trades,
&bar_snap_idx,
bar_start_offset,
emit_start,
emit_end,
)
.into_iter()
})
.collect()
}
/// Process one chunk [emit_start..emit_end) with fresh aggregator state,
/// pre-rolling [`WARMUP_BARS`] leading bars to saturate stateful blocks.
fn process_chunk(
bars: &[OHLCVBar],
snapshots: &[Mbp10Snapshot],
trades: &[Mbp10Trade],
bar_snap_idx: &[usize],
bar_start_offset: usize,
emit_start: usize,
emit_end: usize,
) -> Vec<Vec<f32>> {
let warmup_start = emit_start.saturating_sub(WARMUP_BARS);
// ── Stateful aggregators (persistent across bars) ──
// Block A-E factory aggregators
let mut bar_window: VecDeque<OHLCVBar> = VecDeque::with_capacity(BAR_WINDOW);
@@ -124,22 +210,27 @@ pub fn extract_alpha_features(
// OFICalculator instance for stateful VPIN bucket tracking.
let mut ofi_calc = OFICalculator::new();
// ── Pre-index snapshots by timestamp for fast lookup ──
// Each bar gets the snapshot whose timestamp is the latest ≤ bar.timestamp.
let bar_snap_idx: Vec<usize> = bars
.iter()
.map(|bar| {
let bar_ts_ns = bar.timestamp.timestamp_nanos_opt().unwrap_or(0);
let idx = snapshots
.partition_point(|s| (s.timestamp as i64) <= bar_ts_ns);
idx.saturating_sub(1)
// ── Seed trade_cursor at the first trade belonging to this chunk ──
// For chunk 0 (warmup_start == 0) the cursor starts at 0 so the trade-feed
// inner loop replays *all* pre-window trades into the aggregators on the
// first iteration — preserving the original sequential semantics.
//
// For other chunks we partition past the previous warmup bar's timestamp
// so each trade is fed to exactly one chunk's aggregators.
let mut trade_cursor: usize = if warmup_start == 0 {
0
} else {
let prev_bar_ts = bars[warmup_start - 1 + bar_start_offset]
.timestamp
.timestamp_nanos_opt()
.unwrap_or(0);
trades.partition_point(|t| {
t.timestamp.timestamp_nanos_opt().unwrap_or(0) <= prev_bar_ts
})
.collect();
};
let mut features: Vec<Vec<f32>> = Vec::with_capacity(emit_end - emit_start);
let mut trade_cursor: usize = 0;
let mut features: Vec<Vec<f32>> = Vec::with_capacity(n_output);
for out_idx in 0..n_output {
for out_idx in warmup_start..emit_end {
let bar_idx = out_idx + bar_start_offset;
let bar = &bars[bar_idx];
let bar_ts_ns = bar.timestamp.timestamp_nanos_opt().unwrap_or(0);
@@ -395,7 +486,12 @@ pub fn extract_alpha_features(
ALPHA_FEATURE_DIM
);
features.push(row);
// Suppress emission for the leading warmup bars; their job is to
// saturate Hawkes excitation / Bouchaud EMA / frac-diff FIR / LOB PCA
// / microprice / spread_decomp state before the first emit_start bar.
if out_idx >= emit_start {
features.push(row);
}
}
features