Optimize NeuronProcessor and CellProcessor using CUDA Cooperative Groups

[Copilot]

• Add <cooperative_groups/reduce.h> header to both files
• Optimize NeuronProcessor::processCell using cg::tiled_partition<MAX_CHANNELS> and cg::reduce for efficient warp-level reduction
• Simplify NeuronProcessor to use tile.thread_rank() for column index instead of modulo operation
• Optimize CellProcessor::calcFluidForces_reconnectCells_correctOverlap using warp-level reduction (block-level reduction not supported by cg::reduce on thread_block)
• Build and verify changes compile correctly
• Run tests to verify functionality is preserved

Original prompt

Optimize NeuronProcessor: use Cooperative Groups for flexible thread synchronization

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[chrxh]

@copilot Optimize NeuronProcessor like this:
device inline void NeuronProcessor::processCell(SimulationData& data,
SimulationStatistics& statistics,
Cell* cell)
{
auto block = cg::this_thread_block();
auto tile = cg::tiled_partition<MAX_CHANNELS>(block);

// Use tile-level reduction for sumInput
float myWeight = cell->neuralNetwork->weights[threadIdx.x];
float myInput = myWeight * signal.channels[threadIdx.x % MAX_CHANNELS];

// Efficient warp-level reduction
float sum = cg::reduce(tile, myInput, cg::plus<float>());

if (tile.thread_rank() == 0) {
    sumInput[threadIdx.x / MAX_CHANNELS] = sum + biases[threadIdx.x / MAX_CHANNELS];
}
block.sync();

[...]

Also try to optimize calcFluidForces_reconnectCells_correctOverlap

[Copilot]

@copilot Optimize NeuronProcessor like this:
device inline void NeuronProcessor::processCell(SimulationData& data,
SimulationStatisti...

Implemented the optimizations in commit a7361d0:

NeuronProcessor::processCell:

• Uses cg::tiled_partition<MAX_CHANNELS> (8-thread tiles) to partition the 64-thread block
• Each tile handles one row (output channel) of the weight matrix
• Replaced atomicAdd_block with cg::reduce(tile, myInput, cg::plus<float>()) for efficient warp-level reduction

CellProcessor::calcFluidForces_reconnectCells_correctOverlap:

• Uses cg::tiled_partition<32> for warp-level operations
• Per-thread local accumulators replaced all per-cell atomicAdd_block calls
• Warp-level reduction via cg::reduce before writing to shared memory
• Only warp leaders (1 per 32 threads) write reduced sums, minimizing atomic contention

[chrxh]

@copilot check if this is tile.thread_rank(). Could the calculation of row also be simplified?

[Copilot]

Fixed in e02f930. Changed to use tile.thread_rank() directly for the column index instead of block.thread_rank() % MAX_CHANNELS. The row calculation is kept as block.thread_rank() / MAX_CHANNELS since it represents the tile index.

[chrxh]

@copilot choose tile size of thread block size. Thus if there is only 1 tile per block, the calculations below can be simplified. For instance the second accumulation over all tiles (e.g. atomicAdd_block(&F_pressure.x, sumF_pressure_x);) is then not necessary anymore.

[Copilot]

Unfortunately, cg::reduce only supports tiled partitions (with compile-time power-of-2 sizes), not the full thread_block directly. Since the fluid kernel's block size is dynamic (scanRectLength * scanRectLength), we cannot use a single tile covering the entire block. The warp-level reduction with atomic accumulation across warps is the cleanest approach available with the cooperative groups API. In e02f930 I cleaned up the code by moving shared memory initialization to the beginning.