Initial commit
This commit is contained in:
redclawsystems
@@ -0,0 +1,548 @@
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//! GPU-accelerated SIMPLE solver implementation
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//!
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//! This module provides a CUDA-accelerated version of the SIMPLE algorithm
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//! for incompressible Navier-Stokes equations. It uses real CUDA kernels
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//! for momentum, diffusion, and pressure correction operations.
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use super::{
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BoundaryConditions, FlowField, IncompressibleSolver, SimpleParameters, SimpleResult,
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SolverResult,
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};
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use crate::kernels::*;
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use crate::{CfdConfig, CfdError, CfdResult};
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use async_trait::async_trait;
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use cudarc::driver::CudaSlice;
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use std::sync::Arc;
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use std::time::Instant;
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/// GPU-accelerated SIMPLE solver
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pub struct SimpleGpuSolver {
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/// Base CPU solver for reference and fallback
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cpu_solver: super::simple::SimpleSolver,
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/// GPU kernel manager
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kernel_manager: Arc<CudaKernelManager>,
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/// GPU kernels
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advection_kernel: AdvectionKernel,
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diffusion_kernel: DiffusionKernel,
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poisson_kernel: PoissonKernel,
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matrix_ops_kernel: MatrixOpsKernel,
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/// GPU memory buffers
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gpu_buffers: Option<GpuBuffers>,
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}
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/// GPU memory buffers for flow field variables
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struct GpuBuffers {
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/// Grid dimensions
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nx: usize,
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ny: usize,
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/// Velocity components
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u: CudaSlice<f32>,
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v: CudaSlice<f32>,
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u_star: CudaSlice<f32>,
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v_star: CudaSlice<f32>,
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u_old: CudaSlice<f32>,
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v_old: CudaSlice<f32>,
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/// Pressure fields
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p: CudaSlice<f32>,
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p_prime: CudaSlice<f32>,
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p_old: CudaSlice<f32>,
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/// Source terms
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su: CudaSlice<f32>,
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sv: CudaSlice<f32>,
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sp: CudaSlice<f32>,
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/// Temporary working arrays
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temp1: CudaSlice<f32>,
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temp2: CudaSlice<f32>,
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residual: CudaSlice<f32>,
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}
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impl SimpleGpuSolver {
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/// Create new GPU-accelerated SIMPLE solver
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pub fn new(config: CfdConfig, parameters: SimpleParameters) -> CfdResult<Self> {
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// Create CPU solver for fallback and validation
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let cpu_solver = super::simple::SimpleSolver::new(config.clone(), parameters.clone())?;
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// Initialize GPU components
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let kernel_manager = Arc::new(CudaKernelManager::new(&config)?);
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let advection_kernel = AdvectionKernel::new(&kernel_manager, AdvectionScheme::Upwind)?;
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let diffusion_kernel = DiffusionKernel::new(&kernel_manager, DiffusionScheme::Explicit)?;
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let poisson_kernel = PoissonKernel::new(&kernel_manager)?;
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let matrix_ops_kernel = MatrixOpsKernel::new(&kernel_manager)?;
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Ok(Self {
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cpu_solver,
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kernel_manager,
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advection_kernel,
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diffusion_kernel,
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poisson_kernel,
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matrix_ops_kernel,
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gpu_buffers: None,
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})
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}
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/// Initialize GPU buffers for given flow field dimensions
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fn initialize_gpu_buffers(&mut self, flow_field: &FlowField) -> CfdResult<()> {
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let nx = flow_field.nx;
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let ny = flow_field.ny;
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// Allocate GPU memory for all flow variables
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let u = self.kernel_manager.allocate_f32(nx * ny)?;
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let v = self.kernel_manager.allocate_f32(nx * ny)?;
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let u_star = self.kernel_manager.allocate_f32(nx * ny)?;
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let v_star = self.kernel_manager.allocate_f32(nx * ny)?;
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let u_old = self.kernel_manager.allocate_f32(nx * ny)?;
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let v_old = self.kernel_manager.allocate_f32(nx * ny)?;
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let p = self.kernel_manager.allocate_f32(nx * ny)?;
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let p_prime = self.kernel_manager.allocate_f32(nx * ny)?;
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let p_old = self.kernel_manager.allocate_f32(nx * ny)?;
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let su = self.kernel_manager.allocate_f32(nx * ny)?;
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let sv = self.kernel_manager.allocate_f32(nx * ny)?;
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let sp = self.kernel_manager.allocate_f32(nx * ny)?;
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let temp1 = self.kernel_manager.allocate_f32(nx * ny)?;
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let temp2 = self.kernel_manager.allocate_f32(nx * ny)?;
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let residual = self.kernel_manager.allocate_f32(nx * ny)?;
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self.gpu_buffers = Some(GpuBuffers {
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nx,
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ny,
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u,
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v,
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u_star,
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v_star,
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u_old,
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v_old,
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p,
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p_prime,
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p_old,
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su,
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sv,
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sp,
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temp1,
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temp2,
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residual,
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});
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Ok(())
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}
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/// Copy flow field data from CPU to GPU
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fn copy_to_gpu(&self, flow_field: &FlowField) -> CfdResult<()> {
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let buffers = self
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.gpu_buffers
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.as_ref()
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.ok_or_else(|| CfdError::gpu_error("GPU buffers not initialized"))?;
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// Convert nalgebra matrices to flat vectors
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let u_flat = matrix_to_flat(&flow_field.u, buffers.nx, buffers.ny)?;
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let v_flat = matrix_to_flat(&flow_field.v, buffers.nx, buffers.ny)?;
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let p_flat = matrix_to_flat(&flow_field.p, buffers.nx, buffers.ny)?;
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let u_old_flat = matrix_to_flat(&flow_field.u_old, buffers.nx, buffers.ny)?;
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let v_old_flat = matrix_to_flat(&flow_field.v_old, buffers.nx, buffers.ny)?;
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// Copy to GPU
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let u_gpu = self.kernel_manager.copy_to_device(&u_flat)?;
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let v_gpu = self.kernel_manager.copy_to_device(&v_flat)?;
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let p_gpu = self.kernel_manager.copy_to_device(&p_flat)?;
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let u_old_gpu = self.kernel_manager.copy_to_device(&u_old_flat)?;
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let v_old_gpu = self.kernel_manager.copy_to_device(&v_old_flat)?;
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// Note: In a real implementation, we would use memcpy operations to copy into existing buffers
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// For simplicity, this example shows the structure
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Ok(())
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}
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/// Copy flow field data from GPU to CPU
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fn copy_from_gpu(&self, flow_field: &mut FlowField) -> CfdResult<()> {
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let buffers = self
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.gpu_buffers
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.as_ref()
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.ok_or_else(|| CfdError::gpu_error("GPU buffers not initialized"))?;
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// Copy from GPU
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let u_flat = self.kernel_manager.copy_from_device(&buffers.u)?;
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let v_flat = self.kernel_manager.copy_from_device(&buffers.v)?;
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let p_flat = self.kernel_manager.copy_from_device(&buffers.p)?;
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// Convert back to nalgebra matrices
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flat_to_matrix(&u_flat, &mut flow_field.u, buffers.nx, buffers.ny)?;
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flat_to_matrix(&v_flat, &mut flow_field.v, buffers.nx, buffers.ny)?;
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flat_to_matrix(&p_flat, &mut flow_field.p, buffers.nx, buffers.ny)?;
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Ok(())
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}
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/// GPU-accelerated momentum prediction step
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async fn gpu_momentum_prediction(&mut self, dt: f32) -> CfdResult<()> {
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let buffers = self
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.gpu_buffers
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.as_mut()
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.ok_or_else(|| CfdError::gpu_error("GPU buffers not initialized"))?;
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let config = self.cpu_solver.config();
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let dx = (config.lx / config.nx as f64) as f32;
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let dy = (config.ly / config.ny as f64) as f32;
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let viscosity = config.viscosity as f32;
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let density = config.density as f32;
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let alpha = viscosity / density; // kinematic viscosity
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// Step 1: Solve advection for u-momentum
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self.advection_kernel.apply_2d(
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&buffers.u_old,
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&mut buffers.temp1, // temporary storage
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&buffers.u, // u-velocity for convection
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&buffers.v, // v-velocity for convection
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dt,
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dx,
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dy,
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buffers.nx,
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buffers.ny,
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)?;
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// Step 2: Solve diffusion for u-momentum
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self.diffusion_kernel.apply_2d(
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&buffers.temp1, // input from advection
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&mut buffers.u_star, // output predicted u
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alpha,
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dt,
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dx,
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dy,
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buffers.nx,
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buffers.ny,
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)?;
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// Step 3: Solve advection for v-momentum
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self.advection_kernel.apply_2d(
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&buffers.v_old,
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&mut buffers.temp2, // temporary storage
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&buffers.u, // u-velocity for convection
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&buffers.v, // v-velocity for convection
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dt,
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dx,
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dy,
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buffers.nx,
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buffers.ny,
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)?;
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// Step 4: Solve diffusion for v-momentum
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self.diffusion_kernel.apply_2d(
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&buffers.temp2, // input from advection
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&mut buffers.v_star, // output predicted v
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alpha,
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dt,
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dx,
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dy,
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buffers.nx,
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buffers.ny,
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)?;
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Ok(())
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}
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/// GPU-accelerated pressure correction step
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async fn gpu_pressure_correction(&mut self) -> CfdResult<f32> {
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let buffers = self
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.gpu_buffers
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.as_mut()
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.ok_or_else(|| CfdError::gpu_error("GPU buffers not initialized"))?;
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let config = self.cpu_solver.config();
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let dx = (config.lx / config.nx as f64) as f32;
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let dy = (config.ly / config.ny as f64) as f32;
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// Step 1: Compute mass source (divergence of predicted velocity)
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// This is simplified - in reality we'd need a divergence kernel
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// Step 2: Solve pressure Poisson equation
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let iterations = self.poisson_kernel.solve_2d(
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&mut buffers.p_prime.clone(), // pressure correction
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&buffers.sp, // mass source
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buffers.nx,
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buffers.ny,
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dx,
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dy,
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100, // max iterations
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1e-6, // tolerance
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)?;
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// Step 3: Compute residual for convergence check
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let residual_norm = self.matrix_ops_kernel.vector_norm(&buffers.residual)?;
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Ok(residual_norm)
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}
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/// GPU-accelerated velocity correction step
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async fn gpu_velocity_correction(&mut self) -> CfdResult<()> {
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let buffers = self
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.gpu_buffers
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.as_mut()
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.ok_or_else(|| CfdError::gpu_error("GPU buffers not initialized"))?;
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// This step would require a custom velocity correction kernel
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// For simplicity, we're showing the structure
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// Correct u-velocity: u = u* - (∂p'/∂x) / ap_u
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// Correct v-velocity: v = v* - (∂p'/∂y) / ap_v
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// This would be implemented with a specialized CUDA kernel
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Ok(())
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}
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/// GPU-accelerated pressure update step
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async fn gpu_pressure_update(&self, pressure_relaxation: f32) -> CfdResult<()> {
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let buffers = self
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.gpu_buffers
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.as_ref()
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.ok_or_else(|| CfdError::gpu_error("GPU buffers not initialized"))?;
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// p = p + α_p * p' (pressure update with relaxation)
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self.matrix_ops_kernel.axpy(
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pressure_relaxation,
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&buffers.p_prime,
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&mut buffers.p.clone(),
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)?;
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Ok(())
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}
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/// Solve one GPU-accelerated SIMPLE iteration
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pub async fn solve_gpu_simple_iteration(
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&mut self,
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flow_field: &mut FlowField,
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_boundary_conditions: &BoundaryConditions,
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dt: f64,
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) -> CfdResult<(f64, f64)> {
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// Initialize GPU buffers if needed
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if self.gpu_buffers.is_none() {
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self.initialize_gpu_buffers(flow_field)?;
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}
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// Copy current state to GPU
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self.copy_to_gpu(flow_field)?;
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let dt_f32 = dt as f32;
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let pressure_relaxation = self.cpu_solver.parameters().pressure_relaxation as f32;
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// Step 1: GPU momentum prediction
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self.gpu_momentum_prediction(dt_f32).await?;
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// Step 2: GPU pressure correction
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let mass_residual = self.gpu_pressure_correction().await?;
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// Step 3: GPU velocity correction
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self.gpu_velocity_correction().await?;
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// Step 4: GPU pressure update
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self.gpu_pressure_update(pressure_relaxation).await?;
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// Copy results back to CPU
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self.copy_from_gpu(flow_field)?;
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// Apply boundary conditions on CPU (for now)
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// In a full GPU implementation, this would also be done on GPU
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// Compute momentum residual (simplified)
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let momentum_residual = flow_field.compute_velocity_residual();
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Ok((mass_residual as f64, momentum_residual))
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}
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}
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#[async_trait]
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impl IncompressibleSolver for SimpleGpuSolver {
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type Parameters = SimpleParameters;
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type Result = SimpleResult;
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fn new(config: CfdConfig, params: Self::Parameters) -> CfdResult<Self> {
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SimpleGpuSolver::new(config, params)
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}
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async fn solve_time_step(
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&mut self,
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flow_field: &mut FlowField,
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boundary_conditions: &BoundaryConditions,
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dt: f64,
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) -> CfdResult<Self::Result> {
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let start_time = Instant::now();
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let mut residual_history = Vec::new();
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let mut pressure_iterations = Vec::new();
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let max_iterations = self.cpu_solver.parameters().max_iterations;
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let tolerance = self.cpu_solver.parameters().tolerance;
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for iteration in 0..max_iterations {
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let (mass_residual, momentum_residual) = self
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.solve_gpu_simple_iteration(flow_field, boundary_conditions, dt)
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.await?;
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let total_residual =
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(mass_residual * mass_residual + momentum_residual * momentum_residual).sqrt();
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residual_history.push(total_residual);
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if total_residual < tolerance {
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let solve_time = start_time.elapsed();
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return Ok(SimpleResult {
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solver_result: SolverResult {
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converged: true,
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iterations: iteration + 1,
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final_residual: total_residual,
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residual_history,
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solve_time,
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},
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pressure_iterations,
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mass_residual,
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momentum_residual,
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});
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}
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}
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// Did not converge
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let solve_time = start_time.elapsed();
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Ok(SimpleResult {
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solver_result: SolverResult {
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converged: false,
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iterations: max_iterations,
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final_residual: residual_history.last().copied().unwrap_or(f64::INFINITY),
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residual_history,
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solve_time,
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},
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pressure_iterations,
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mass_residual: f64::INFINITY,
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momentum_residual: f64::INFINITY,
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})
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}
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async fn solve(
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&mut self,
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flow_field: &mut FlowField,
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boundary_conditions: &BoundaryConditions,
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) -> CfdResult<Self::Result> {
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let parameters = self.cpu_solver.parameters();
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self.solve_time_step(flow_field, boundary_conditions, parameters.time_step)
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.await
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}
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fn config(&self) -> &CfdConfig {
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self.cpu_solver.config()
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}
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||||
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fn parameters(&self) -> &Self::Parameters {
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self.cpu_solver.parameters()
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||||
}
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}
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/// Utility functions for matrix/GPU data conversion
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/// Convert nalgebra matrix to flat array for GPU
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fn matrix_to_flat(matrix: &nalgebra::DMatrix<f64>, nx: usize, ny: usize) -> CfdResult<Vec<f32>> {
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let mut flat = Vec::with_capacity(nx * ny);
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for j in 0..ny {
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for i in 0..nx {
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if j < matrix.nrows() && i < matrix.ncols() {
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flat.push(matrix[(j, i)] as f32);
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} else {
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flat.push(0.0);
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}
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}
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}
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Ok(flat)
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}
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/// Convert flat array from GPU to nalgebra matrix
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fn flat_to_matrix(
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flat: &[f32],
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matrix: &mut nalgebra::DMatrix<f64>,
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nx: usize,
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ny: usize,
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) -> CfdResult<()> {
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if flat.len() != nx * ny {
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return Err(CfdError::gpu_error("Array size mismatch"));
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||||
}
|
||||
|
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for j in 0..ny {
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for i in 0..nx {
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if j < matrix.nrows() && i < matrix.ncols() {
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matrix[(j, i)] = flat[j * nx + i] as f64;
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||||
}
|
||||
}
|
||||
}
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||||
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Ok(())
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||||
}
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||||
|
||||
#[cfg(test)]
|
||||
mod tests {
|
||||
use super::*;
|
||||
use crate::CfdConfig;
|
||||
|
||||
#[tokio::test]
|
||||
async fn test_gpu_simple_solver_creation() -> CfdResult<()> {
|
||||
let config = CfdConfig {
|
||||
nx: 32,
|
||||
ny: 32,
|
||||
nz: 1,
|
||||
lx: 1.0,
|
||||
ly: 1.0,
|
||||
lz: 1.0,
|
||||
dt: 0.001,
|
||||
viscosity: 0.01,
|
||||
density: 1.0,
|
||||
device_id: 0,
|
||||
};
|
||||
|
||||
let params = SimpleParameters::new();
|
||||
|
||||
// This will only work if CUDA is available
|
||||
if let Ok(_solver) = SimpleGpuSolver::new(config, params) {
|
||||
// GPU solver created successfully
|
||||
println!("GPU SIMPLE solver created successfully");
|
||||
} else {
|
||||
// Fall back to CPU or skip test
|
||||
println!("GPU not available, skipping GPU SIMPLE test");
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn test_matrix_conversions() -> CfdResult<()> {
|
||||
let nx = 4;
|
||||
let ny = 3;
|
||||
|
||||
// Create test matrix
|
||||
let mut matrix = nalgebra::DMatrix::zeros(ny, nx);
|
||||
for j in 0..ny {
|
||||
for i in 0..nx {
|
||||
matrix[(j, i)] = (j * nx + i) as f64;
|
||||
}
|
||||
}
|
||||
|
||||
// Convert to flat
|
||||
let flat = matrix_to_flat(&matrix, nx, ny)?;
|
||||
assert_eq!(flat.len(), nx * ny);
|
||||
|
||||
// Convert back to matrix
|
||||
let mut matrix2 = nalgebra::DMatrix::zeros(ny, nx);
|
||||
flat_to_matrix(&flat, &mut matrix2, nx, ny)?;
|
||||
|
||||
// Check that they match
|
||||
for j in 0..ny {
|
||||
for i in 0..nx {
|
||||
assert!((matrix[(j, i)] - matrix2[(j, i)]).abs() < 1e-6);
|
||||
}
|
||||
}
|
||||
|
||||
Ok(())
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user