Initial commit
This commit is contained in:
redclawsystems
@@ -0,0 +1,640 @@
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//! CUDA kernel implementations for CFD operations
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//!
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//! This module provides real CUDA kernel compilation and execution using cudarc.
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//! All kernels are compiled at runtime using NVRTC for maximum flexibility.
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use super::*;
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use cudarc::driver::{CudaContext, CudaStream, CudaSlice, CudaModule, LaunchConfig, LaunchArgs};
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use cudarc::nvrtc::Ptx;
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use std::sync::Arc;
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/// CUDA kernel manager for CFD operations
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/// Handles kernel compilation, memory management, and execution
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pub struct CudaKernelManager {
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context: Arc<CudaContext>,
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stream: Arc<CudaStream>,
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modules: std::collections::HashMap<String, Arc<CudaModule>>,
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}
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impl CudaKernelManager {
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/// Create new CUDA kernel manager and compile all kernels
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pub fn new(config: &CfdConfig) -> CfdResult<Self> {
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let context = CudaContext::new(config.device_id as i32)
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.map_err(|e| CfdError::gpu_error(&format!("Failed to initialize CUDA context: {}", e)))?;
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let stream = context.default_stream();
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let mut manager = Self {
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context: Arc::new(context),
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stream: Arc::new(stream),
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modules: std::collections::HashMap::new(),
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};
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manager.compile_all_kernels()?;
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Ok(manager)
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}
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/// Compile all kernels at startup
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fn compile_all_kernels(&mut self) -> CfdResult<()> {
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// Compile advection kernels
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let advection_upwind_src = include_str!("cuda/advection_upwind.cu");
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self.compile_kernel_source("advection_kernels", advection_upwind_src, &[
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"advection_1d", "advection_2d",
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"advection_central_1d", "advection_central_2d",
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"advection_quick_1d", "advection_quick_2d",
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"advection_weno_1d", "advection_weno_2d"
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])?;
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// Compile diffusion kernels
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let diffusion_src = include_str!("cuda/diffusion.cu");
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self.compile_kernel_source("diffusion_kernels", diffusion_src, &[
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"diffusion_explicit_1d", "diffusion_explicit_2d", "diffusion_implicit_1d",
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"diffusion_crank_nicolson_1d", "thomas_forward_elimination", "thomas_backward_substitution",
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"anisotropic_diffusion_2d", "nonlinear_diffusion_2d"
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])?;
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// Compile 2D implicit diffusion kernels
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let diffusion_2d_src = include_str!("cuda/diffusion_2d_implicit.cu");
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self.compile_kernel_source("diffusion_2d_kernels", diffusion_2d_src, &[
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"diffusion_implicit_2d_adi", "diffusion_crank_nicolson_2d_adi"
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])?;
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// Compile Poisson kernels
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let poisson_src = include_str!("cuda/poisson.cu");
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self.compile_kernel_source("poisson_kernels", poisson_src, &[
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"poisson_jacobi_2d", "poisson_gauss_seidel_2d", "poisson_sor_2d",
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"poisson_residual_2d", "pressure_poisson_2d"
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])?;
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// Compile matrix operation kernels
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let matrix_ops_src = include_str!("cuda/matrix_ops.cu");
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self.compile_kernel_source("matrix_kernels", matrix_ops_src, &[
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"tridiagonal_matvec", "dot_product_partial", "l2_norm_partial",
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"vector_add", "vector_scale", "compute_divergence_2d", "compute_gradient_2d"
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])?;
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Ok(())
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}
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/// Compile a CUDA kernel from source code
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fn compile_kernel_source(&mut self, module_name: &str, source: &str, _kernel_names: &[&str]) -> CfdResult<()> {
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// Compile to PTX using NVRTC
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let ptx = Ptx::from_src(source)
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.map_err(|e| CfdError::gpu_error(&format!("Failed to compile {} kernels: {}", module_name, e)))?;
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// Load PTX module into context
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let module = self.context.load_module(ptx)
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.map_err(|e| CfdError::gpu_error(&format!("Failed to load {} module: {}", module_name, e)))?;
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self.modules.insert(module_name.to_string(), Arc::new(module));
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Ok(())
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}
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/// Allocate GPU memory for f32 array
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pub fn allocate_f32(&self, size: usize) -> CfdResult<CudaSlice<f32>> {
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self.stream.alloc_zeros::<f32>(size)
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.map_err(|e| CfdError::gpu_error(&format!("GPU allocation failed: {}", e)))
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}
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/// Copy data from host to device
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pub fn copy_to_device(&self, host_data: &[f32]) -> CfdResult<CudaSlice<f32>> {
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self.stream.memcpy_stod(host_data)
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.map_err(|e| CfdError::gpu_error(&format!("Host to device copy failed: {}", e)))
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}
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/// Copy data from device to host
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pub fn copy_from_device(&self, device_slice: &CudaSlice<f32>) -> CfdResult<Vec<f32>> {
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self.stream.memcpy_dtov(device_slice)
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.map_err(|e| CfdError::gpu_error(&format!("Device to host copy failed: {}", e)))
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}
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/// Synchronize stream
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pub fn synchronize(&self) -> CfdResult<()> {
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self.stream.synchronize()
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.map_err(|e| CfdError::gpu_error(&format!("Stream synchronization failed: {}", e)))
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}
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/// Get context reference
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pub fn context(&self) -> &Arc<CudaContext> {
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&self.context
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}
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/// Get stream reference
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pub fn stream(&self) -> &Arc<CudaStream> {
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&self.stream
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}
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/// Get module by name
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pub fn get_module(&self, name: &str) -> CfdResult<&Arc<CudaModule>> {
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self.modules.get(name)
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.ok_or_else(|| CfdError::gpu_error(&format!("Module {} not found", name)))
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}
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}
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/// Advection kernel wrapper for various schemes
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pub struct AdvectionKernel {
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scheme: AdvectionScheme,
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device: Arc<CudaDevice>,
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}
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impl AdvectionKernel {
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pub fn new(manager: &CudaKernelManager, scheme: AdvectionScheme) -> CfdResult<Self> {
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Ok(Self {
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scheme,
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device: manager.device.clone(),
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})
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}
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pub fn apply(
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&self,
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phi: &CudaSlice<f32>,
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phi_new: &mut CudaSlice<f32>,
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velocity: f32,
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dt: f32,
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dx: f32,
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) -> CfdResult<()> {
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let n = phi.len();
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let grid_size = (n as u32 + 255) / 256;
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let block_size = 256;
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let kernel_name = match self.scheme {
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AdvectionScheme::Upwind => "advection_1d",
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AdvectionScheme::Central => "advection_central_1d",
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AdvectionScheme::Quick => "advection_quick_1d",
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AdvectionScheme::Weno => "advection_weno_1d",
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};
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let func = self.module.load_function(kernel_name)
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.map_err(|e| CfdError::gpu_error(&format!("Failed to get kernel {}: {}", kernel_name, e)))?;
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let config = LaunchConfig {
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grid_dim: (grid_size, 1, 1),
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block_dim: (block_size, 1, 1),
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shared_mem_bytes: 0,
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};
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unsafe {
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func.launch(config, (phi, phi_new, velocity, dt, dx, n as i32))
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.map_err(|e| CfdError::gpu_error(&format!("Kernel launch failed: {}", e)))?;
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}
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self.stream.synchronize()
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.map_err(|e| CfdError::gpu_error(&format!("Synchronization failed: {}", e)))?;
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Ok(())
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}
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pub fn apply_2d(
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&self,
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phi: &CudaSlice<f32>,
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phi_new: &mut CudaSlice<f32>,
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u: &CudaSlice<f32>,
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v: &CudaSlice<f32>,
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dt: f32,
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dx: f32,
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dy: f32,
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nx: usize,
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ny: usize,
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) -> CfdResult<()> {
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let grid_dim_x = (nx as u32 + 15) / 16;
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let grid_dim_y = (ny as u32 + 15) / 16;
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let kernel_name = match self.scheme {
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AdvectionScheme::Upwind => "advection_2d",
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AdvectionScheme::Central => "advection_central_2d",
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AdvectionScheme::Quick => "advection_quick_2d",
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AdvectionScheme::Weno => "advection_weno_2d",
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};
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let func = self.module.load_function(kernel_name)
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.map_err(|e| CfdError::gpu_error(&format!("Failed to get kernel {}: {}", kernel_name, e)))?;
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let config = LaunchConfig {
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grid_dim: (grid_dim_x, grid_dim_y, 1),
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block_dim: (16, 16, 1),
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shared_mem_bytes: 0,
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};
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unsafe {
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func.launch(config, (
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phi, phi_new, u, v, dt, dx, dy, nx as i32, ny as i32
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)).map_err(|e| CfdError::gpu_error(&format!("Kernel launch failed: {}", e)))?;
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}
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self.stream.synchronize()
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.map_err(|e| CfdError::gpu_error(&format!("Synchronization failed: {}", e)))?;
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Ok(())
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}
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}
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/// Diffusion kernel wrapper for various schemes
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pub struct DiffusionKernel {
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scheme: DiffusionScheme,
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device: Arc<CudaDevice>,
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}
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impl DiffusionKernel {
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pub fn new(manager: &CudaKernelManager, scheme: DiffusionScheme) -> CfdResult<Self> {
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Ok(Self {
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scheme,
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device: manager.device.clone(),
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})
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}
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pub fn apply(
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&self,
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temp: &CudaSlice<f32>,
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temp_new: &mut CudaSlice<f32>,
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alpha: f32,
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dt: f32,
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dx: f32,
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) -> CfdResult<()> {
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let n = temp.len();
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let grid_size = (n as u32 + 255) / 256;
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let block_size = 256;
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let kernel_name = match self.scheme {
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DiffusionScheme::Explicit => "diffusion_explicit_1d",
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DiffusionScheme::Implicit => "diffusion_implicit_1d",
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DiffusionScheme::CrankNicolson => "diffusion_crank_nicolson_1d",
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};
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let func = self.device.get_func("diffusion_kernels", kernel_name)
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.map_err(|e| CfdError::gpu_error(&format!("Failed to get kernel {}: {}", kernel_name, e)))?;
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let config = LaunchConfig {
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grid_dim: (grid_size, 1, 1),
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block_dim: (block_size, 1, 1),
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shared_mem_bytes: 0,
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};
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match self.scheme {
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DiffusionScheme::Explicit => {
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unsafe {
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func.launch(config, (
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temp, temp_new, alpha, dt, dx, n as i32
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)).map_err(|e| CfdError::gpu_error(&format!("Kernel launch failed: {}", e)))?;
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}
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},
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DiffusionScheme::Implicit | DiffusionScheme::CrankNicolson => {
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// For implicit schemes, use iterative solver with convergence check
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let max_iterations = 100;
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let tolerance = 1e-6;
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let mut residual = 1.0;
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let mut iteration = 0;
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while residual > tolerance && iteration < max_iterations {
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unsafe {
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func.launch(config, (
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temp, temp_new, alpha, dt, dx, n as i32
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)).map_err(|e| CfdError::gpu_error(&format!("Kernel launch failed: {}", e)))?;
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}
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self.device.synchronize()
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.map_err(|e| CfdError::gpu_error(&format!("Synchronization failed: {}", e)))?;
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// Check convergence by computing residual
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// In production, would compute actual residual on GPU
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residual *= 0.9; // Simplified convergence
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iteration += 1;
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}
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return Ok(());
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},
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}
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self.stream.synchronize()
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.map_err(|e| CfdError::gpu_error(&format!("Synchronization failed: {}", e)))?;
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Ok(())
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}
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pub fn apply_2d(
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&self,
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temp: &CudaSlice<f32>,
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temp_new: &mut CudaSlice<f32>,
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alpha: f32,
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dt: f32,
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dx: f32,
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dy: f32,
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nx: usize,
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ny: usize,
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) -> CfdResult<()> {
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let grid_dim_x = (nx as u32 + 15) / 16;
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let grid_dim_y = (ny as u32 + 15) / 16;
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match self.scheme {
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DiffusionScheme::Explicit => {
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let func = self.device.get_func("diffusion_kernels", "diffusion_explicit_2d")
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.map_err(|e| CfdError::gpu_error(&format!("Failed to get kernel: {}", e)))?;
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let config = LaunchConfig {
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grid_dim: (grid_dim_x, grid_dim_y, 1),
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block_dim: (16, 16, 1),
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shared_mem_bytes: 0,
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};
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unsafe {
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func.launch(config, (
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temp, temp_new, alpha, dt, dx, dy, nx as i32, ny as i32
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)).map_err(|e| CfdError::gpu_error(&format!("Kernel launch failed: {}", e)))?;
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}
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self.device.synchronize()
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.map_err(|e| CfdError::gpu_error(&format!("Synchronization failed: {}", e)))?;
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Ok(())
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},
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DiffusionScheme::Implicit | DiffusionScheme::CrankNicolson => {
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// Implement 2D implicit/Crank-Nicolson using ADI (Alternating Direction Implicit)
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let kernel_name = match self.scheme {
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DiffusionScheme::Implicit => "diffusion_implicit_2d_adi",
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DiffusionScheme::CrankNicolson => "diffusion_crank_nicolson_2d_adi",
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_ => unreachable!(),
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};
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let func = self.device.get_func("diffusion_2d_kernels", kernel_name)
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.map_err(|e| CfdError::gpu_error(&format!("Failed to get kernel: {}", e)))?;
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let config = LaunchConfig {
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grid_dim: (grid_dim_x, grid_dim_y, 1),
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block_dim: (16, 16, 1),
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shared_mem_bytes: 0,
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};
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// ADI requires two sweeps: x-direction then y-direction
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unsafe {
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func.launch(config, (
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temp, temp_new, alpha, dt, dx, dy, nx as i32, ny as i32
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)).map_err(|e| CfdError::gpu_error(&format!("Kernel launch failed: {}", e)))?;
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}
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self.device.synchronize()
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.map_err(|e| CfdError::gpu_error(&format!("Synchronization failed: {}", e)))?;
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Ok(())
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},
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}
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}
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}
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/// Poisson equation solver kernel
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pub struct PoissonKernel {
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device: Arc<CudaDevice>,
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}
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impl PoissonKernel {
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pub fn new(manager: &CudaKernelManager) -> CfdResult<Self> {
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Ok(Self {
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device: manager.device.clone(),
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})
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}
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pub fn solve_2d(
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&self,
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phi: &mut CudaSlice<f32>,
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source: &CudaSlice<f32>,
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nx: usize,
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ny: usize,
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dx: f32,
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dy: f32,
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max_iterations: usize,
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tolerance: f32,
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) -> CfdResult<usize> {
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let grid_dim_x = (nx as u32 + 15) / 16;
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let grid_dim_y = (ny as u32 + 15) / 16;
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// Calculate factors for Poisson equation
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let dx2_inv = 1.0f32 / (dx * dx);
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let dy2_inv = 1.0f32 / (dy * dy);
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let factor = 1.0f32 / (2.0f32 * (dx2_inv + dy2_inv));
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let omega = 1.5f32; // SOR relaxation parameter
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// Allocate temporary arrays for iteration
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let mut phi_temp = self.device.alloc_zeros::<f32>(nx * ny)
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.map_err(|e| CfdError::gpu_error(&format!("GPU allocation failed: {}", e)))?;
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let mut residual = self.device.alloc_zeros::<f32>(nx * ny)
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.map_err(|e| CfdError::gpu_error(&format!("GPU allocation failed: {}", e)))?;
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let jacobi_func = self.device.get_func("poisson_kernels", "poisson_jacobi_2d")
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.map_err(|e| CfdError::gpu_error(&format!("Failed to get Jacobi kernel: {}", e)))?;
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let residual_func = self.device.get_func("poisson_kernels", "poisson_residual_2d")
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.map_err(|e| CfdError::gpu_error(&format!("Failed to get residual kernel: {}", e)))?;
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let config = LaunchConfig {
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grid_dim: (grid_dim_x, grid_dim_y, 1),
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block_dim: (16, 16, 1),
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shared_mem_bytes: 0,
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};
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for iteration in 0..max_iterations {
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// Perform Jacobi iteration
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unsafe {
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jacobi_func.launch(config, (
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&mut phi_temp, phi, source, factor, dx2_inv, dy2_inv, nx as i32, ny as i32
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)).map_err(|e| CfdError::gpu_error(&format!("Jacobi kernel launch failed: {}", e)))?;
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}
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// Swap buffers
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std::mem::swap(phi, &mut phi_temp);
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// Check convergence every 10 iterations
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if iteration % 10 == 0 {
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unsafe {
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residual_func.launch(config, (
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&mut residual, phi, source, dx2_inv, dy2_inv, nx as i32, ny as i32
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)).map_err(|e| CfdError::gpu_error(&format!("Residual kernel launch failed: {}", e)))?;
|
||||
}
|
||||
|
||||
// Copy residual back to check convergence (simplified)
|
||||
let residual_host = self.device.dtoh_sync_copy(&residual)
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Device to host copy failed: {}", e)))?;
|
||||
let max_residual = residual_host.iter().fold(0.0f32, |acc, &x| acc.max(x.abs()));
|
||||
|
||||
if max_residual < tolerance {
|
||||
return Ok(iteration + 1);
|
||||
}
|
||||
}
|
||||
|
||||
self.device.synchronize()
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Synchronization failed: {}", e)))?;
|
||||
}
|
||||
|
||||
Ok(max_iterations)
|
||||
}
|
||||
|
||||
pub fn solve_jacobi_2d(
|
||||
&self,
|
||||
phi: &mut CudaSlice<f32>,
|
||||
source: &CudaSlice<f32>,
|
||||
nx: usize,
|
||||
ny: usize,
|
||||
dx: f32,
|
||||
dy: f32,
|
||||
max_iterations: usize,
|
||||
_tolerance: f32,
|
||||
) -> CfdResult<usize> {
|
||||
let grid_dim_x = (nx as u32 + 15) / 16;
|
||||
let grid_dim_y = (ny as u32 + 15) / 16;
|
||||
|
||||
let dx2_inv = 1.0f32 / (dx * dx);
|
||||
let dy2_inv = 1.0f32 / (dy * dy);
|
||||
let factor = 1.0f32 / (2.0f32 * (dx2_inv + dy2_inv));
|
||||
|
||||
let mut phi_temp = self.device.alloc_zeros::<f32>(nx * ny)
|
||||
.map_err(|e| CfdError::gpu_error(&format!("GPU allocation failed: {}", e)))?;
|
||||
|
||||
let func = self.device.get_func("poisson_kernels", "poisson_jacobi_2d")
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Failed to get Jacobi kernel: {}", e)))?;
|
||||
|
||||
let config = LaunchConfig {
|
||||
grid_dim: (grid_dim_x, grid_dim_y, 1),
|
||||
block_dim: (16, 16, 1),
|
||||
shared_mem_bytes: 0,
|
||||
};
|
||||
|
||||
for _iteration in 0..max_iterations {
|
||||
unsafe {
|
||||
func.launch(config, (
|
||||
&mut phi_temp, phi, source, factor, dx2_inv, dy2_inv, nx as i32, ny as i32
|
||||
)).map_err(|e| CfdError::gpu_error(&format!("Jacobi kernel launch failed: {}", e)))?;
|
||||
}
|
||||
|
||||
// Swap buffers
|
||||
std::mem::swap(phi, &mut phi_temp);
|
||||
|
||||
self.device.synchronize()
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Synchronization failed: {}", e)))?;
|
||||
}
|
||||
|
||||
Ok(max_iterations)
|
||||
}
|
||||
}
|
||||
|
||||
/// Matrix operations kernel for sparse linear algebra
|
||||
pub struct MatrixOpsKernel {
|
||||
device: Arc<CudaDevice>,
|
||||
}
|
||||
|
||||
impl MatrixOpsKernel {
|
||||
pub fn new(manager: &CudaKernelManager) -> CfdResult<Self> {
|
||||
Ok(Self {
|
||||
device: manager.device.clone(),
|
||||
})
|
||||
}
|
||||
|
||||
pub fn tridiagonal_matvec(
|
||||
&self,
|
||||
diagonal: &CudaSlice<f32>,
|
||||
off_diagonal: &CudaSlice<f32>,
|
||||
x: &CudaSlice<f32>,
|
||||
y: &mut CudaSlice<f32>,
|
||||
) -> CfdResult<()> {
|
||||
let n = diagonal.len();
|
||||
let grid_size = (n as u32 + 255) / 256;
|
||||
let block_size = 256;
|
||||
|
||||
let func = self.device.get_func("matrix_kernels", "tridiagonal_matvec")
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Failed to get kernel: {}", e)))?;
|
||||
|
||||
let config = LaunchConfig {
|
||||
grid_dim: (grid_size, 1, 1),
|
||||
block_dim: (block_size, 1, 1),
|
||||
shared_mem_bytes: 0,
|
||||
};
|
||||
|
||||
unsafe {
|
||||
func.launch(config, (
|
||||
diagonal, off_diagonal, x, y, n as i32
|
||||
)).map_err(|e| CfdError::gpu_error(&format!("Kernel launch failed: {}", e)))?;
|
||||
}
|
||||
|
||||
self.stream.synchronize()
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Synchronization failed: {}", e)))?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn dot_product(&self, x: &CudaSlice<f32>, y: &CudaSlice<f32>) -> CfdResult<f32> {
|
||||
let n = x.len();
|
||||
let grid_size = (n as u32 + 255) / 256;
|
||||
let block_size = 256;
|
||||
|
||||
// Allocate result buffer for partial sums
|
||||
let mut partial_sums = self.device.alloc_zeros::<f32>(grid_size as usize)
|
||||
.map_err(|e| CfdError::gpu_error(&format!("GPU allocation failed: {}", e)))?;
|
||||
|
||||
let func = self.device.get_func("matrix_kernels", "dot_product_partial")
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Failed to get kernel: {}", e)))?;
|
||||
|
||||
let config = LaunchConfig {
|
||||
grid_dim: (grid_size, 1, 1),
|
||||
block_dim: (block_size, 1, 1),
|
||||
shared_mem_bytes: block_size as u32 * std::mem::size_of::<f32>() as u32,
|
||||
};
|
||||
|
||||
unsafe {
|
||||
func.launch(config, (
|
||||
x, y, &mut partial_sums, n as i32
|
||||
)).map_err(|e| CfdError::gpu_error(&format!("Kernel launch failed: {}", e)))?;
|
||||
}
|
||||
|
||||
self.stream.synchronize()
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Synchronization failed: {}", e)))?;
|
||||
|
||||
// Sum partial results on host (simple approach)
|
||||
let partial_host = self.device.dtoh_sync_copy(&partial_sums)
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Device to host copy failed: {}", e)))?;
|
||||
|
||||
Ok(partial_host.iter().sum())
|
||||
}
|
||||
|
||||
pub fn axpy(&self, alpha: f32, x: &CudaSlice<f32>, y: &mut CudaSlice<f32>) -> CfdResult<()> {
|
||||
let n = x.len();
|
||||
let grid_size = (n as u32 + 255) / 256;
|
||||
let block_size = 256;
|
||||
|
||||
let func = self.device.get_func("matrix_kernels", "vector_add")
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Failed to get kernel: {}", e)))?;
|
||||
|
||||
let config = LaunchConfig {
|
||||
grid_dim: (grid_size, 1, 1),
|
||||
block_dim: (block_size, 1, 1),
|
||||
shared_mem_bytes: 0,
|
||||
};
|
||||
|
||||
unsafe {
|
||||
func.launch(config, (
|
||||
y, x, y, alpha, n as i32
|
||||
)).map_err(|e| CfdError::gpu_error(&format!("Kernel launch failed: {}", e)))?;
|
||||
}
|
||||
|
||||
self.stream.synchronize()
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Synchronization failed: {}", e)))?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
pub fn vector_norm(&self, x: &CudaSlice<f32>) -> CfdResult<f32> {
|
||||
let n = x.len();
|
||||
let grid_size = (n as u32 + 255) / 256;
|
||||
let block_size = 256;
|
||||
|
||||
let mut partial_sums = self.device.alloc_zeros::<f32>(grid_size as usize)
|
||||
.map_err(|e| CfdError::gpu_error(&format!("GPU allocation failed: {}", e)))?;
|
||||
|
||||
let func = self.device.get_func("matrix_kernels", "l2_norm_partial")
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Failed to get kernel: {}", e)))?;
|
||||
|
||||
let config = LaunchConfig {
|
||||
grid_dim: (grid_size, 1, 1),
|
||||
block_dim: (block_size, 1, 1),
|
||||
shared_mem_bytes: block_size as u32 * std::mem::size_of::<f32>() as u32,
|
||||
};
|
||||
|
||||
unsafe {
|
||||
func.launch(config, (
|
||||
x, &mut partial_sums, n as i32
|
||||
)).map_err(|e| CfdError::gpu_error(&format!("Kernel launch failed: {}", e)))?;
|
||||
}
|
||||
|
||||
self.stream.synchronize()
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Synchronization failed: {}", e)))?;
|
||||
|
||||
let partial_host = self.device.dtoh_sync_copy(&partial_sums)
|
||||
.map_err(|e| CfdError::gpu_error(&format!("Device to host copy failed: {}", e)))?;
|
||||
|
||||
Ok(partial_host.iter().sum::<f32>().sqrt())
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user