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astc: Enable parallel CPU astc decoding

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Given the issues with GPU accelerated ASTC decoding with NVIDIA's latest drivers, parallelize astc decoding on the CPU.
Uses half the available threads in the system for astc decoding.
This commit is contained in:
Morph 2022-09-01 21:29:22 -04:00
parent 4b07596b83
commit 809126c94a
1 changed files with 32 additions and 18 deletions
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50
src/video_core/textures/astc.cpp
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@ -13,7 +13,9 @@
#include <boost/container/static_vector.hpp> #include <boost/container/static_vector.hpp>
#include "common/alignment.h"
#include "common/common_types.h" #include "common/common_types.h"
#include "common/thread_worker.h"
#include "video_core/textures/astc.h" #include "video_core/textures/astc.h"
class InputBitStream { class InputBitStream {
@ -1650,29 +1652,41 @@ static void DecompressBlock(std::span<const u8, 16> inBuf, const u32 blockWidth,
void Decompress(std::span<const uint8_t> data, uint32_t width, uint32_t height, uint32_t depth, void Decompress(std::span<const uint8_t> data, uint32_t width, uint32_t height, uint32_t depth,
uint32_t block_width, uint32_t block_height, std::span<uint8_t> output) { uint32_t block_width, uint32_t block_height, std::span<uint8_t> output) {
u32 block_index = 0; const u32 rows = Common::DivideUp(height, block_height);
std::size_t depth_offset = 0; const u32 cols = Common::DivideUp(width, block_width);
for (u32 z = 0; z < depth; z++) {
for (u32 y = 0; y < height; y += block_height) {
for (u32 x = 0; x < width; x += block_width) {
const std::span<const u8, 16> blockPtr{data.subspan(block_index * 16, 16)};
// Blocks can be at most 12x12 Common::ThreadWorker workers{std::max(std::thread::hardware_concurrency(), 2U) / 2,
std::array<u32, 12 * 12> uncompData; "yuzu:ASTCDecompress"};
DecompressBlock(blockPtr, block_width, block_height, uncompData);
u32 decompWidth = std::min(block_width, width - x); for (u32 z = 0; z < depth; ++z) {
u32 decompHeight = std::min(block_height, height - y); const u32 depth_offset = z * height * width * 4;
for (u32 y_index = 0; y_index < rows; ++y_index) {
auto decompress_stride = [data, width, height, depth, block_width, block_height, output,
rows, cols, z, depth_offset, y_index] {
const u32 y = y_index * block_height;
for (u32 x_index = 0; x_index < cols; ++x_index) {
const u32 block_index = (z * rows * cols) + (y_index * cols) + x_index;
const u32 x = x_index * block_width;
const std::span<u8> outRow = output.subspan(depth_offset + (y * width + x) * 4); const std::span<const u8, 16> blockPtr{data.subspan(block_index * 16, 16)};
for (u32 jj = 0; jj < decompHeight; jj++) {
std::memcpy(outRow.data() + jj * width * 4, // Blocks can be at most 12x12
uncompData.data() + jj * block_width, decompWidth * 4); std::array<u32, 12 * 12> uncompData;
DecompressBlock(blockPtr, block_width, block_height, uncompData);
u32 decompWidth = std::min(block_width, width - x);
u32 decompHeight = std::min(block_height, height - y);
const std::span<u8> outRow = output.subspan(depth_offset + (y * width + x) * 4);
for (u32 h = 0; h < decompHeight; ++h) {
std::memcpy(outRow.data() + h * width * 4,
uncompData.data() + h * block_width, decompWidth * 4);
}
} }
++block_index; };
} workers.QueueWork(std::move(decompress_stride));
} }
depth_offset += height * width * 4; workers.WaitForRequests();
} }
} }