rtx-cfd legacy GPU tests: the current CudaKernelManager API (Arc-held manager, allocate-on-copy, copy_from_device returning the host vector), MatrixOpsKernel::tridiagonal_matvec added over the existing kernel, PoissonKernel::solve_jacobi_2d kept as the older name; CfdConfig literals take ..Default

crates/specialized/rtx-cfd/src/kernels/cuda_simple.rs

@@ -601,6 +601,23 @@ impl PoissonKernel {
         })
     }
 
+    /// [`Self::solve_2d`] under its older name.
+    #[allow(clippy::too_many_arguments)]
+    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> {
+        self.solve_2d(phi, source, nx, ny, dx, dy, max_iterations, tolerance)
+    }
+
+    #[allow(clippy::too_many_arguments)]
     pub fn solve_2d(
         &self,
         phi: &mut CudaSlice<f32>,
@@ -757,6 +774,39 @@ impl MatrixOpsKernel {
         Ok(result)
     }
 
+    /// `y = T x` for the tridiagonal matrix with `diagonal` (n) and
+    /// `off_diagonal` (n − 1) — the `tridiagonal_matvec` kernel.
+    pub fn tridiagonal_matvec(
+        &self,
+        diagonal: &CudaSlice<f32>,
+        off_diagonal: &CudaSlice<f32>,
+        x: &CudaSlice<f32>,
+        y: &mut CudaSlice<f32>,
+    ) -> CfdResult<()> {
+        let n = x.len();
+        let func = self
+            .module
+            .load_function("tridiagonal_matvec")
+            .map_err(|e| CfdError::gpu_error(&format!("Failed to get kernel: {}", e)))?;
+        let config = LaunchConfig {
+            grid_dim: ((n as u32 + 255) / 256, 1, 1),
+            block_dim: (256, 1, 1),
+            shared_mem_bytes: 0,
+        };
+        unsafe {
+            self.stream
+                .launch_builder(&func)
+                .arg(diagonal)
+                .arg(off_diagonal)
+                .arg(x)
+                .arg(y)
+                .arg(&(n as i32))
+                .launch(config)
+                .map_err(|e| CfdError::gpu_error(&format!("Kernel launch failed: {}", e)))?;
+        }
+        Ok(())
+    }
+
     /// Compute L2 norm of vector
     pub fn vector_norm(&self, x: &CudaSlice<f32>) -> CfdResult<f32> {
         let n = x.len();

crates/specialized/rtx-cfd/tests/cuda_kernel_manager_tests.rs

@@ -20,6 +20,7 @@ mod cuda_kernel_manager_tests {
             viscosity: 0.01,
             density: 1.0,
             device_id: 0,
+        ..CfdConfig::default()
         }
     }
 
@@ -31,7 +32,7 @@ mod cuda_kernel_manager_tests {
         }
 
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
 
         // Verify manager is created successfully
         // Context and stream should be valid (non-null references)
@@ -49,7 +50,7 @@ mod cuda_kernel_manager_tests {
         }
 
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
 
         // Test that required modules are loaded
         // Getting a module should succeed and return a valid Arc reference
@@ -72,7 +73,7 @@ mod cuda_kernel_manager_tests {
         }
 
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
 
         // Test that non-existent module returns error
         let result = manager.get_module("nonexistent_module");
@@ -92,7 +93,7 @@ mod cuda_kernel_manager_tests {
         }
 
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
 
         // Test allocating GPU memory
         let size = 1024;
@@ -110,7 +111,7 @@ mod cuda_kernel_manager_tests {
         }
 
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
 
         // Test copying data to GPU
         let host_data: Vec<f32> = (0..100).map(|i| i as f32).collect();
@@ -128,7 +129,7 @@ mod cuda_kernel_manager_tests {
         }
 
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
 
         // Test round-trip copy: host -> device -> host
         let original_data: Vec<f32> = (0..100).map(|i| i as f32 * 0.1).collect();
@@ -159,7 +160,7 @@ mod cuda_kernel_manager_tests {
         }
 
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
 
         // Test that synchronization doesn't error
         manager.synchronize()?;

crates/specialized/rtx-cfd/tests/gpu_kernel_comprehensive_tests.rs

@@ -37,9 +37,10 @@ mod gpu_kernel_tests {
             lz: 0.0,
             dt: 0.001,
             ..Default::default()
+            ..CfdConfig::default()
         };
 
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
 
         // Test all advection schemes
         let schemes = vec![
@@ -117,9 +118,10 @@ mod gpu_kernel_tests {
             lz: 0.0,
             dt: 0.001,
             ..Default::default()
+            ..CfdConfig::default()
         };
 
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let kernel = AdvectionKernel::new(&manager, AdvectionScheme::Upwind)?;
 
         let nx = 64;
@@ -202,9 +204,10 @@ mod gpu_kernel_tests {
             lz: 0.0,
             dt: 0.0001,
             ..Default::default()
+            ..CfdConfig::default()
         };
 
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
 
         // Test all diffusion schemes
         let schemes = vec![
@@ -288,9 +291,10 @@ mod gpu_kernel_tests {
             lz: 0.0,
             dt: 0.0001,
             ..Default::default()
+            ..CfdConfig::default()
         };
 
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let kernel = DiffusionKernel::new(&manager, DiffusionScheme::Explicit)?;
 
         let nx = 64;
@@ -366,9 +370,10 @@ mod gpu_kernel_tests {
             lz: 0.0,
             dt: 0.001,
             ..Default::default()
+            ..CfdConfig::default()
         };
 
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let kernel = PoissonKernel::new(&manager)?;
 
         let nx = 32;
@@ -443,9 +448,10 @@ mod gpu_kernel_tests {
             lz: 0.0,
             dt: 0.001,
             ..Default::default()
+            ..CfdConfig::default()
         };
 
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
 
         // Test allocation and deallocation of large arrays
         let sizes = vec![1024, 65536, 262144];
@@ -491,9 +497,10 @@ mod gpu_kernel_tests {
             lz: 0.0,
             dt: 0.001,
             ..Default::default()
+            ..CfdConfig::default()
         };
 
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let kernel = AdvectionKernel::new(&manager, AdvectionScheme::Upwind)?;
 
         let n = 512 * 512;

crates/specialized/rtx-cfd/tests/gpu_kernel_tests.rs

@@ -25,20 +25,21 @@ mod cuda_tests {
             viscosity: 0.01,
             density: 1.0,
             device_id: 0,
+        ..CfdConfig::default()
         }
     }
 
     #[test]
     fn test_cuda_kernel_manager_creation() -> CfdResult<()> {
         let config = create_test_config();
-        let _manager = CudaKernelManager::new(&config)?;
+        let _manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         Ok(())
     }
 
     #[test]
     fn test_memory_operations() -> CfdResult<()> {
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
 
         // Test allocation
         let size = 1000;
@@ -63,7 +64,7 @@ mod cuda_tests {
     #[test]
     fn test_advection_kernel_1d() -> CfdResult<()> {
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let kernel = AdvectionKernel::new(&manager, AdvectionScheme::Upwind)?;
 
         let n = 100;
@@ -99,7 +100,7 @@ mod cuda_tests {
     #[test]
     fn test_advection_kernel_2d() -> CfdResult<()> {
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let kernel = AdvectionKernel::new(&manager, AdvectionScheme::Upwind)?;
 
         let nx = 32;
@@ -147,7 +148,7 @@ mod cuda_tests {
     #[test]
     fn test_diffusion_kernel_explicit() -> CfdResult<()> {
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let kernel = DiffusionKernel::new(&manager, DiffusionScheme::Explicit)?;
 
         let n = 100;
@@ -182,7 +183,7 @@ mod cuda_tests {
     #[test]
     fn test_diffusion_kernel_2d() -> CfdResult<()> {
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let kernel = DiffusionKernel::new(&manager, DiffusionScheme::Explicit)?;
 
         let nx = 32;
@@ -225,7 +226,7 @@ mod cuda_tests {
     #[test]
     fn test_poisson_kernel_jacobi() -> CfdResult<()> {
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let kernel = PoissonKernel::new(&manager)?;
 
         let nx = 32;
@@ -267,7 +268,7 @@ mod cuda_tests {
     #[test]
     fn test_matrix_ops_tridiagonal() -> CfdResult<()> {
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let kernel = MatrixOpsKernel::new(&manager)?;
 
         let n = 100;
@@ -303,7 +304,7 @@ mod cuda_tests {
     #[test]
     fn test_matrix_ops_dot_product() -> CfdResult<()> {
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let kernel = MatrixOpsKernel::new(&manager)?;
 
         let n = 1000;
@@ -325,7 +326,7 @@ mod cuda_tests {
     #[test]
     fn test_matrix_ops_vector_norm() -> CfdResult<()> {
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let kernel = MatrixOpsKernel::new(&manager)?;
 
         let n = 100;
@@ -345,7 +346,7 @@ mod cuda_tests {
     #[test]
     fn test_matrix_ops_axpy() -> CfdResult<()> {
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let kernel = MatrixOpsKernel::new(&manager)?;
 
         let n = 100;
@@ -372,7 +373,7 @@ mod cuda_tests {
     #[test]
     fn test_performance_comparison() -> CfdResult<()> {
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
 
         let n = 10000;
         let host_data: Vec<f32> = (0..n).map(|i| i as f32 * 0.1).collect();
@@ -394,7 +395,7 @@ mod cuda_tests {
     #[test]
     fn test_kernel_error_handling() -> CfdResult<()> {
         let config = create_test_config();
-        let manager = CudaKernelManager::new(&config)?;
+        let manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
 
         // Test with mismatched array sizes (should not crash)
         let small_array = manager.allocate_f32(10)?;
@@ -434,6 +435,7 @@ mod cpu_fallback_tests {
             reference_length: 1.0,
             reference_velocity: 1.0,
             use_gpu: true,
+            ..CfdConfig::default()
         };
 
         let result = CudaKernelManager::new(&config);

crates/specialized/rtx-cfd/tests/gpu_reduction_tests.rs

@@ -21,6 +21,7 @@ mod gpu_reduction_tests {
             viscosity: 0.01,
             density: 1.0,
             device_id: 0,
+        ..CfdConfig::default()
         }
     }
 

crates/specialized/rtx-cfd/tests/kernels_tests.rs

@@ -20,7 +20,7 @@ mod cuda_tests {
     #[tokio::test]
     async fn test_advection_kernel_upwind() -> CfdResult<()> {
         let config = CfdConfig::new().with_gpu(true);
-        let kernel_manager = CudaKernelManager::new(&config)?;
+        let kernel_manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let advection_kernel = AdvectionKernel::new(&kernel_manager, AdvectionScheme::Upwind)?;
 
         // Test data: 1D advection with known analytical solution
@@ -43,14 +43,14 @@ mod cuda_tests {
         let d_phi_new = kernel_manager.allocate_f32(nx)?;
 
         // Copy to GPU
-        kernel_manager.copy_to_device(&phi, &mut d_phi)?;
+        d_phi = kernel_manager.copy_to_device(&phi)?;
 
         // Run advection kernel
         advection_kernel.apply(&d_phi, &d_phi_new, velocity as f32, dt as f32, dx as f32)?;
 
         // Copy result back
         let mut result = vec![0.0f32; nx];
-        kernel_manager.copy_from_device(&d_phi_new, &mut result)?;
+        result = kernel_manager.copy_from_device(&d_phi_new)?;
 
         // Verify that the pulse has moved (mass conservation)
         let initial_mass: f32 = phi.iter().sum();
@@ -82,7 +82,7 @@ mod cuda_tests {
     #[tokio::test]
     async fn test_advection_kernel_central() -> CfdResult<()> {
         let config = CfdConfig::new().with_gpu(true);
-        let kernel_manager = CudaKernelManager::new(&config)?;
+        let kernel_manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let advection_kernel = AdvectionKernel::new(&kernel_manager, AdvectionScheme::Central)?;
 
         // Test data: smooth sinusoidal wave
@@ -100,11 +100,11 @@ mod cuda_tests {
         let mut d_phi = kernel_manager.allocate_f32(nx)?;
         let d_phi_new = kernel_manager.allocate_f32(nx)?;
 
-        kernel_manager.copy_to_device(&phi, &mut d_phi)?;
+        d_phi = kernel_manager.copy_to_device(&phi)?;
         advection_kernel.apply(&d_phi, &d_phi_new, velocity as f32, dt as f32, dx as f32)?;
 
         let mut result = vec![0.0f32; nx];
-        kernel_manager.copy_from_device(&d_phi_new, &mut result)?;
+        result = kernel_manager.copy_from_device(&d_phi_new)?;
 
         // For central scheme, verify mass conservation and smoothness
         let initial_mass: f32 = phi.iter().sum();
@@ -117,7 +117,7 @@ mod cuda_tests {
     #[tokio::test]
     async fn test_diffusion_kernel_explicit() -> CfdResult<()> {
         let config = CfdConfig::new().with_gpu(true);
-        let kernel_manager = CudaKernelManager::new(&config)?;
+        let kernel_manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let diffusion_kernel = DiffusionKernel::new(&kernel_manager, DiffusionScheme::Explicit)?;
 
         // Test 1D heat equation with analytical solution
@@ -135,11 +135,11 @@ mod cuda_tests {
         let mut d_temp = kernel_manager.allocate_f32(nx)?;
         let d_temp_new = kernel_manager.allocate_f32(nx)?;
 
-        kernel_manager.copy_to_device(&temperature, &mut d_temp)?;
+        d_temp = kernel_manager.copy_to_device(&temperature)?;
         diffusion_kernel.apply(&d_temp, &d_temp_new, alpha as f32, dt as f32, dx as f32)?;
 
         let mut result = vec![0.0f32; nx];
-        kernel_manager.copy_from_device(&d_temp_new, &mut result)?;
+        result = kernel_manager.copy_from_device(&d_temp_new)?;
 
         // Verify diffusion: edges should be smoother, total heat conserved
         let initial_total: f32 = temperature.iter().sum();
@@ -160,7 +160,7 @@ mod cuda_tests {
     #[tokio::test]
     async fn test_poisson_kernel_2d() -> CfdResult<()> {
         let config = CfdConfig::new().with_gpu(true);
-        let kernel_manager = CudaKernelManager::new(&config)?;
+        let kernel_manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let poisson_kernel = PoissonKernel::new(&kernel_manager)?;
 
         // Test 2D Poisson equation: ∇²φ = f
@@ -190,8 +190,8 @@ mod cuda_tests {
         let mut d_phi = kernel_manager.allocate_f32(nx * ny)?;
         let mut d_source = kernel_manager.allocate_f32(nx * ny)?;
 
-        kernel_manager.copy_to_device(&phi, &mut d_phi)?;
+        d_phi = kernel_manager.copy_to_device(&phi)?;
-        kernel_manager.copy_to_device(&source, &mut d_source)?;
+        d_source = kernel_manager.copy_to_device(&source)?;
 
         // Solve Poisson equation
         let max_iterations = 1000;
@@ -208,7 +208,7 @@ mod cuda_tests {
         )?;
 
         let mut result = vec![0.0f32; nx * ny];
-        kernel_manager.copy_from_device(&d_phi, &mut result)?;
+        result = kernel_manager.copy_from_device(&d_phi)?;
 
         // Verify against analytical solution
         let mut max_error = 0.0f32;
@@ -233,7 +233,7 @@ mod cuda_tests {
     #[tokio::test]
     async fn test_matrix_ops_kernel() -> CfdResult<()> {
         let config = CfdConfig::new().with_gpu(true);
-        let kernel_manager = CudaKernelManager::new(&config)?;
+        let kernel_manager = std::sync::Arc::new(CudaKernelManager::new(&config)?);
         let matrix_ops = MatrixOpsKernel::new(&kernel_manager)?;
 
         // Test sparse matrix-vector multiplication (typical in CFD)
@@ -246,17 +246,17 @@ mod cuda_tests {
         let mut d_diag = kernel_manager.allocate_f32(n)?;
         let mut d_off_diag = kernel_manager.allocate_f32(n - 1)?;
         let mut d_x = kernel_manager.allocate_f32(n)?;
-        let d_y = kernel_manager.allocate_f32(n)?;
+        let mut d_y = kernel_manager.allocate_f32(n)?;
 
-        kernel_manager.copy_to_device(&diagonal, &mut d_diag)?;
+        d_diag = kernel_manager.copy_to_device(&diagonal)?;
-        kernel_manager.copy_to_device(&off_diagonal, &mut d_off_diag)?;
+        d_off_diag = kernel_manager.copy_to_device(&off_diagonal)?;
-        kernel_manager.copy_to_device(&x, &mut d_x)?;
+        d_x = kernel_manager.copy_to_device(&x)?;
 
         // Perform A*x = y operation
-        matrix_ops.tridiagonal_matvec(&d_diag, &d_off_diag, &d_x, &d_y, n)?;
+        matrix_ops.tridiagonal_matvec(&d_diag, &d_off_diag, &d_x, &mut d_y)?;
 
         let mut result = vec![0.0f32; n];
-        kernel_manager.copy_from_device(&d_y, &mut result)?;
+        result = kernel_manager.copy_from_device(&d_y)?;
 
         // Verify result manually for first few elements
         assert_relative_eq!(result[0], 2.0 * x[0] - x[1], epsilon = 1e-6);
@@ -287,7 +287,7 @@ async fn test_kernel_manager_initialization() -> CfdResult<()> {
     #[cfg(feature = "cuda")]
     {
         // Should successfully create kernel manager
-        let result = rtx_cfd::kernels::CudaKernelManager::new(&config);
+        let mut result = rtx_cfd::kernels::CudaKernelManager::new(&config);
         // May fail if no CUDA device available - that's expected in CI
         match result {
             Ok(_) => println!("CUDA kernel manager created successfully"),