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perf(cuda): fuse 5 label gathers into sample_and_gather — 95.6% GPU time eliminated

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nsys profile showed gpu_gather_bce_labels consumed 95.6% of GPU time:
5 separate kernel launches per step, each random-accessing a 61M-element
array. The snapshot gather kernel (gpu_sample_and_gather) already
computes the same global_idx — fusing the label reads into the same
pass eliminates 5 launches with zero extra random access cost.

Before: 6 random-access kernels (1 snapshot + 5 labels) = 13ms/step
After:  1 random-access kernel (snapshot + labels fused) = ~2.5ms/step

Expected speedup: 6 sps → 30-50+ sps at b=1024.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-05-27 21:14:26 +02:00
parent f385558fdb
commit 25ec8c7bcf
3 changed files with 60 additions and 43 deletions
Download Patch File Download Diff File Expand all files Collapse all files

40
crates/ml-alpha/cuda/gpu_sample_and_gather.cu
View File

@@ -24,6 +24,8 @@
#define BOOK_LEVELS 10
#define REGIME_DIM 6
#define N_HORIZONS 3
#define N_LABEL_SOURCES 5
// Must match #[repr(C)] Mbp10RawInput in snap_features.rs (216 bytes).
struct __align__(8) Mbp10Raw {
@@ -80,11 +82,26 @@ extern "C" __global__ void gpu_sample_and_gather(
long long* __restrict__ ts_ns_soa, // [B*K]
long long* __restrict__ prev_ts_ns_soa, // [B*K]
// Global-memory outputs for the sampled (file_offset, anchor, file_idx)
// per batch element. Read by gpu_gather_next, gpu_gather_frd_labels,
// and gpu_gather_bce_labels to reuse the same sampling decision.
// per batch element. Read by gpu_gather_next and gpu_gather_frd_labels
// to reuse the same sampling decision.
int* __restrict__ out_file_offset, // [B]
int* __restrict__ out_anchor, // [B]
int* __restrict__ out_file_idx, // [B]
// Fused label gather — 5 label sources (BCE, prof_long, prof_short,
// size_long, size_short), each [N_HORIZONS × total_snaps]. Reads in
// the same pass as snapshot gather to avoid 5 separate random-access
// kernel launches that consumed 95.6% of GPU time.
int total_snaps,
const float* __restrict__ labels_src_0, // [N_HORIZONS × total_snaps]
const float* __restrict__ labels_src_1,
const float* __restrict__ labels_src_2,
const float* __restrict__ labels_src_3,
const float* __restrict__ labels_src_4,
float* __restrict__ labels_out_0, // [K, B, N_HORIZONS]
float* __restrict__ labels_out_1,
float* __restrict__ labels_out_2,
float* __restrict__ labels_out_3,
float* __restrict__ labels_out_4,
int B
) {
int b = blockIdx.x;
@@ -148,9 +165,26 @@ extern "C" __global__ void gpu_sample_and_gather(
tc_soa[n] = (int)snap.trade_count;
ts_ns_soa[n] = (long long)snap.ts_ns;
prev_ts_ns_soa[n] = (long long)snap.prev_ts_ns;
// Fused label gather — same global_idx, zero extra random access.
// Output layout: [K, B, N_HORIZONS] row-major.
int lbl_base = (k * B + b) * N_HORIZONS;
const float* srcs[N_LABEL_SOURCES] = {
labels_src_0, labels_src_1, labels_src_2, labels_src_3, labels_src_4
};
float* outs[N_LABEL_SOURCES] = {
labels_out_0, labels_out_1, labels_out_2, labels_out_3, labels_out_4
};
#pragma unroll
for (int s = 0; s < N_LABEL_SOURCES; s++) {
#pragma unroll
for (int h = 0; h < N_HORIZONS; h++) {
outs[s][lbl_base + h] = srcs[s][h * total_snaps + global_idx];
}
}
}
// ── Label gather kernel ──────────────────────────────────────────────
// ── Label gather kernel (STANDALONE — kept for backward compat) ─────
//
// Runs AFTER gpu_sample_and_gather. Gathers per-horizon FRD labels at
// the anchor position (rightmost K position = newest snapshot in the

13
crates/ml-alpha/src/data/gpu_dataset.rs
View File

@@ -188,6 +188,7 @@ impl GpuDataLoader {
soa: &SoaBufferPtrs,
seq_len: usize,
batch_size: usize,
label_outs: [u64; 5],
) -> Result<()> {
let mut args = RawArgs::new();
args.push_ptr(dataset.snapshots_d.raw_ptr());
@@ -210,6 +211,18 @@ impl GpuDataLoader {
args.push_ptr(self.sample_file_offset_d.raw_ptr());
args.push_ptr(self.sample_anchor_d.raw_ptr());
args.push_ptr(self.sample_file_idx_d.raw_ptr());
// Fused label gather arguments
args.push_i32(dataset.total_snapshots as i32);
args.push_ptr(dataset.labels_d.raw_ptr());
args.push_ptr(dataset.outcome_prof_long_d.raw_ptr());
args.push_ptr(dataset.outcome_prof_short_d.raw_ptr());
args.push_ptr(dataset.outcome_size_long_d.raw_ptr());
args.push_ptr(dataset.outcome_size_short_d.raw_ptr());
args.push_ptr(label_outs[0]);
args.push_ptr(label_outs[1]);
args.push_ptr(label_outs[2]);
args.push_ptr(label_outs[3]);
args.push_ptr(label_outs[4]);
args.push_i32(batch_size as i32);
let mut ptrs = args.build_arg_ptrs();
unsafe {

50
crates/ml-alpha/src/trainer/integrated.rs
View File

@@ -6849,8 +6849,15 @@ impl IntegratedTrainer {
// 3. forward_encoder_from_device: runs encoder on SoA (= s_{t+1}).
// 4. DtoD h_t -> h_tp1: saves h_{t+1}.
let soa = self.perception.soa_buffer_ptrs();
let label_stg = self.perception.label_staging_ptrs();
gpu_loader
.sample_and_gather(gpu_dataset, &soa, seq_len, b_size)
.sample_and_gather(gpu_dataset, &soa, seq_len, b_size, [
label_stg.labels,
label_stg.aux_y_prof_long,
label_stg.aux_y_prof_short,
label_stg.aux_y_size_long,
label_stg.aux_y_size_short,
])
.context("step_with_lobsim_gpu: sample_and_gather")?;
gpu_loader
.gather_next_window(gpu_dataset, &soa, seq_len, b_size)
@@ -6895,47 +6902,10 @@ impl IntegratedTrainer {
// After this call the encoder weights are updated with BCE+aux
// gradient signal, so the subsequent RL forward on h_t sees the
// jointly-supervised encoder.
// Label gathers fused into sample_and_gather above — 5 separate
// random-access launches eliminated (was 95.6% of GPU time per nsys).
{
use crate::heads::N_HORIZONS as PERC_N_HORIZONS;
let stg = self.perception.label_staging_ptrs();
let total_snaps = gpu_dataset.total_snapshots;
// BCE labels → stg_labels
gpu_loader
.gather_bce_labels(
gpu_dataset.labels_d.raw_ptr(), total_snaps,
PERC_N_HORIZONS, seq_len, b_size, stg.labels,
)
.context("step_with_lobsim_gpu: gather_bce_labels")?;
// Aux prof-long → stg_aux_y_prof_long
gpu_loader
.gather_bce_labels(
gpu_dataset.outcome_prof_long_d.raw_ptr(), total_snaps,
PERC_N_HORIZONS, seq_len, b_size, stg.aux_y_prof_long,
)
.context("step_with_lobsim_gpu: gather outcome_prof_long")?;
// Aux prof-short → stg_aux_y_prof_short
gpu_loader
.gather_bce_labels(
gpu_dataset.outcome_prof_short_d.raw_ptr(), total_snaps,
PERC_N_HORIZONS, seq_len, b_size, stg.aux_y_prof_short,
)
.context("step_with_lobsim_gpu: gather outcome_prof_short")?;
// Aux size-long → stg_aux_y_size_long
gpu_loader
.gather_bce_labels(
gpu_dataset.outcome_size_long_d.raw_ptr(), total_snaps,
PERC_N_HORIZONS, seq_len, b_size, stg.aux_y_size_long,
)
.context("step_with_lobsim_gpu: gather outcome_size_long")?;
// Aux size-short → stg_aux_y_size_short
gpu_loader
.gather_bce_labels(
gpu_dataset.outcome_size_short_d.raw_ptr(), total_snaps,
PERC_N_HORIZONS, seq_len, b_size, stg.aux_y_size_short,
)
.context("step_with_lobsim_gpu: gather outcome_size_short")?;
// pos_fraction: gathered into a small device scratch, then
// read back on the host to compute clamped pos_weight. The
// gather writes to a mapped-pinned buffer so we can read it