jungfrauandClaude Opus 5
6368c00173
The pre-scan decompressed its frames on the host and folded them into a per-pixel projection there. On a 16M-pixel detector that is 60 frames of 72 MB to decompress and 20 bytes per pixel to read and write back per frame - about 40 GB of memory traffic - and it was the whole cost of the phase once the mask was no longer the bottleneck. Only the compressed chunk crosses PCIe now. BSLZ4DecoderGPU already exposes the raw decoded bytes (Decode(), the path its own tests use), which is what this needs: the projection is defined on the RAW STORED COUNTS with the pixel type's sentinel skipped, not on the preprocessed image, so nothing here goes through the preprocessor. Sums, maxima and counts are integers, so the device result is identical to the host's rather than merely close. Frames are folded in batches of four. The fold reads and writes the whole accumulator whatever the batch holds, so per frame it was spending most of the bandwidth on the accumulator rather than on the data; four is where that stops mattering, and every frame beyond it is another full frame of device memory, which costs more in cudaMalloc - device-synchronizing - than it saves. The accumulator is built on a thread of its own. It allocates and clears several hundred megabytes, and doing that in the constructor stalled the caller before it had read its first frame. Frames the device cannot take - anything but bitshuffle+LZ4 - still go to a host shard, so a run mixing compressions needs no second code path, and a build without CUDA is unchanged. RotationScaleMergeGPU set the CUDA device in its constructor and never put it back. CUDA's current device is per-thread, so that silently re-pinned the calling thread for the rest of its life, and the destructor freed several gigabytes against whatever device happened to be current by then - CudaDevicePtr records no device of its own. Every entry point now sets the device on entry and restores it on exit. ParallelFor/ParallelChunks moved to common/ParallelFor.h; two files had copies and a third wants them. Measured on a 16M-pixel rotation dataset: pre-scan 4.78 s -> 2.37 s -> ~2.0 s, shadow unchanged at 139126 pixels (22143 on a 2M-pixel dataset). Full 24-crystal battery: same space group on all 24, none failed, 15m32s -> 14m49s. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
56 lines
2.3 KiB
C++
56 lines
2.3 KiB
C++
// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
|
|
// SPDX-License-Identifier: GPL-3.0-only
|
|
|
|
#pragma once
|
|
|
|
#include <algorithm>
|
|
#include <atomic>
|
|
#include <future>
|
|
#include <vector>
|
|
|
|
// Two shapes of "run this over a range on several threads", used by the analysis code. Both take the
|
|
// worker count from the caller rather than asking the hardware, so a run that was told how many
|
|
// threads to use keeps to it.
|
|
|
|
// Chunked: each worker gets one contiguous [lo, hi) range and there is no per-item synchronisation.
|
|
// Right for millions of cheap uniform items - the CPU stand-in for a flat CUDA grid-stride kernel.
|
|
// The split is fixed and deterministic, so a pass whose per-element work is independent gives the
|
|
// same answer as the serial loop, bit for bit.
|
|
template <typename Fn>
|
|
void ParallelChunks(int n, size_t nthreads, Fn fn) {
|
|
if (n <= 0) return;
|
|
const int nt = static_cast<int>(std::max<size_t>(1, std::min(nthreads, static_cast<size_t>(n))));
|
|
if (nt == 1) { fn(0, n); return; }
|
|
const int chunk = (n + nt - 1) / nt;
|
|
std::vector<std::future<void>> futures;
|
|
futures.reserve(nt);
|
|
for (int t = 0; t < nt; t++) {
|
|
const int lo = t * chunk, hi = std::min(n, lo + chunk);
|
|
if (lo >= hi) break;
|
|
futures.emplace_back(std::async(std::launch::async, [&fn, lo, hi] { fn(lo, hi); }));
|
|
}
|
|
for (auto &f : futures) f.get();
|
|
}
|
|
|
|
// Work-stealing per item, off a shared atomic counter: one atomic per item, so use it only where the
|
|
// per-item work is heavy and uneven (per-frame fits, per-ring selections) and the atomic amortises.
|
|
// For millions of tiny uniform items a per-item atomic is pure contention - use ParallelChunks.
|
|
template <typename Fn>
|
|
void ParallelFor(int n, size_t nthreads, Fn fn) {
|
|
if (n <= 0) return;
|
|
if (nthreads <= 1 || n == 1) {
|
|
for (int i = 0; i < n; i++) fn(i);
|
|
return;
|
|
}
|
|
const size_t local = std::min(nthreads, static_cast<size_t>(n));
|
|
std::atomic<int> next = 0;
|
|
std::vector<std::future<void>> futures;
|
|
futures.reserve(local);
|
|
for (size_t t = 0; t < local; t++)
|
|
futures.emplace_back(std::async(std::launch::async, [&] {
|
|
for (int i = next.fetch_add(1); i < n; i = next.fetch_add(1))
|
|
fn(i);
|
|
}));
|
|
for (auto &f : futures) f.get();
|
|
}
|