Initial commit
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redclawsystems
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// Copyright (c) 2024 RustyTorch++ Team
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// Licensed under the Apache License, Version 2.0
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//! Comprehensive tests for GPU solvers using TDD methodology.
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#[cfg(disabled)]
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mod tests {
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use super::super::*;
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use crate::assembly::SparseMatrix;
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use nalgebra::DVector;
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/// Create a test symmetric positive definite matrix
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fn create_spd_matrix(size: usize) -> SparseMatrix {
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let mut matrix = SparseMatrix::new(size, size);
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// Create a diagonally dominant SPD matrix
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for i in 0..size {
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matrix.add_entry(i, i, (size as f64) + 1.0).unwrap();
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if i > 0 {
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matrix.add_entry(i, i - 1, -1.0).unwrap();
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matrix.add_entry(i - 1, i, -1.0).unwrap();
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}
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}
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matrix.finalize().unwrap();
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matrix
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}
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/// Create a test general matrix
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fn create_general_matrix(size: usize) -> SparseMatrix {
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let mut matrix = SparseMatrix::new(size, size);
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// Create a non-symmetric matrix
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for i in 0..size {
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matrix.add_entry(i, i, (size as f64) + 1.0).unwrap();
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if i > 0 {
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matrix.add_entry(i, i - 1, -1.0).unwrap();
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}
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if i < size - 1 {
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matrix.add_entry(i, i + 1, -2.0).unwrap();
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}
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}
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matrix.finalize().unwrap();
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matrix
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}
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/// Create a test right-hand side vector
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fn create_rhs(size: usize) -> DVector<f64> {
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DVector::from_fn(size, |i, _| (i as f64) + 1.0)
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}
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#[test]
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fn test_gpu_cholesky_solver_capabilities() {
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let solver = GpuCholeskyDirect::new();
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let caps = solver.capabilities();
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assert!(caps.symmetric);
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assert!(caps.positive_definite);
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assert!(caps.gpu_acceleration);
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assert!(caps.multiple_rhs);
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assert!(caps.iterative_refinement);
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assert!(caps.memory_efficiency >= 3);
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assert!(caps.computational_efficiency >= 4);
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}
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#[test]
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fn test_gpu_lu_solver_capabilities() {
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let solver = GpuLuDirect::new();
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let caps = solver.capabilities();
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assert!(!caps.symmetric);
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assert!(!caps.positive_definite);
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assert!(caps.gpu_acceleration);
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assert!(caps.multiple_rhs);
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assert!(caps.iterative_refinement);
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assert!(caps.memory_efficiency >= 2);
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assert!(caps.computational_efficiency >= 4);
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}
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#[test]
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fn test_gpu_ldlt_solver_capabilities() {
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let solver = GpuLdltDirect::new();
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let caps = solver.capabilities();
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assert!(caps.symmetric);
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assert!(!caps.positive_definite);
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assert!(caps.gpu_acceleration);
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assert!(caps.multiple_rhs);
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assert!(caps.iterative_refinement);
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assert!(caps.memory_efficiency >= 3);
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assert!(caps.computational_efficiency >= 4);
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}
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#[test]
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fn test_gpu_cg_solver_capabilities() {
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let solver = GpuConjugateGradient::new();
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let caps = solver.capabilities();
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assert!(caps.symmetric);
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assert!(caps.positive_definite);
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assert!(caps.gpu_acceleration);
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assert!(!caps.multiple_rhs); // CG typically handles single RHS
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assert!(!caps.iterative_refinement); // CG is already iterative
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assert!(caps.memory_efficiency >= 4);
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assert!(caps.computational_efficiency >= 4);
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}
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#[test]
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fn test_gpu_cholesky_solve_small_spd_matrix() {
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let matrix = create_spd_matrix(3);
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let rhs = create_rhs(3);
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let options = SolverOptions::default();
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let mut solver = GpuCholeskyDirect::new();
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let result = solver.solve(&matrix, &rhs, &options);
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match result {
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Ok((solution, info)) => {
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assert_eq!(solution.len(), 3);
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assert!(info.converged);
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assert_eq!(info.iterations, 1); // Direct solver
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assert!(info.solve_time.as_secs_f64() >= 0.0);
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assert!(info.memory_usage > 0);
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// Verify solution quality: Ax = b
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let computed_rhs = matrix.multiply_vector(&solution).unwrap();
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let residual = (&computed_rhs - &rhs).norm();
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assert!(residual < 1e-10, "Residual too large: {}", residual);
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}
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Err(_) => {
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// GPU might not be available in test environment
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// This is acceptable for CI environments
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println!("GPU solver not available, test skipped");
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}
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}
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}
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#[test]
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fn test_gpu_lu_solve_general_matrix() {
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let matrix = create_general_matrix(3);
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let rhs = create_rhs(3);
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let options = SolverOptions::default();
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let mut solver = GpuLuDirect::new();
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let result = solver.solve(&matrix, &rhs, &options);
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match result {
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Ok((solution, info)) => {
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assert_eq!(solution.len(), 3);
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assert!(info.converged);
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assert_eq!(info.iterations, 1); // Direct solver
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assert!(info.solve_time.as_secs_f64() >= 0.0);
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assert!(info.memory_usage > 0);
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// Verify solution quality: Ax = b
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let computed_rhs = matrix.multiply_vector(&solution).unwrap();
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let residual = (&computed_rhs - &rhs).norm();
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assert!(residual < 1e-10, "Residual too large: {}", residual);
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}
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Err(_) => {
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// GPU might not be available in test environment
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println!("GPU solver not available, test skipped");
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}
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}
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}
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#[test]
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fn test_gpu_ldlt_solve_symmetric_indefinite() {
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// Create a symmetric but not positive definite matrix
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let mut matrix = SparseMatrix::new(3, 3);
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matrix.add_entry(0, 0, 1.0).unwrap();
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matrix.add_entry(0, 1, 2.0).unwrap();
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matrix.add_entry(1, 0, 2.0).unwrap();
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matrix.add_entry(1, 1, -1.0).unwrap(); // Negative diagonal element
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matrix.add_entry(1, 2, 1.0).unwrap();
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matrix.add_entry(2, 1, 1.0).unwrap();
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matrix.add_entry(2, 2, 2.0).unwrap();
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matrix.finalize().unwrap();
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let rhs = create_rhs(3);
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let options = SolverOptions::default();
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let mut solver = GpuLdltDirect::new();
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let result = solver.solve(&matrix, &rhs, &options);
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match result {
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Ok((solution, info)) => {
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assert_eq!(solution.len(), 3);
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assert!(info.converged);
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assert_eq!(info.iterations, 1); // Direct solver
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assert!(info.solve_time.as_secs_f64() >= 0.0);
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assert!(info.memory_usage > 0);
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// Verify solution quality: Ax = b
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let computed_rhs = matrix.multiply_vector(&solution).unwrap();
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let residual = (&computed_rhs - &rhs).norm();
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assert!(residual < 1e-8, "Residual too large: {}", residual);
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}
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Err(_) => {
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// GPU might not be available in test environment
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println!("GPU solver not available, test skipped");
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}
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}
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}
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#[test]
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fn test_gpu_cg_solve_spd_matrix() {
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let matrix = create_spd_matrix(5);
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let rhs = create_rhs(5);
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let mut options = SolverOptions::default();
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options.max_iterations = 100;
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options.tolerance = 1e-8;
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let mut solver = GpuConjugateGradient::new();
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let result = solver.solve(&matrix, &rhs, &options);
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match result {
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Ok((solution, info)) => {
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assert_eq!(solution.len(), 5);
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assert!(info.solve_time.as_secs_f64() >= 0.0);
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assert!(info.memory_usage > 0);
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assert!(info.iterations <= options.max_iterations);
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if info.converged {
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// Verify solution quality: Ax = b
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let computed_rhs = matrix.multiply_vector(&solution).unwrap();
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let residual = (&computed_rhs - &rhs).norm();
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assert!(residual < 1e-6, "Residual too large: {}", residual);
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}
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}
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Err(_) => {
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// GPU might not be available in test environment
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println!("GPU solver not available, test skipped");
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}
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}
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}
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#[test]
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fn test_dimension_mismatch_error() {
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let matrix = create_spd_matrix(3);
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let rhs = create_rhs(2); // Wrong size
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let options = SolverOptions::default();
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let mut solver = GpuCholeskyDirect::new();
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let result = solver.solve(&matrix, &rhs, &options);
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// Should fail due to dimension mismatch, regardless of GPU availability
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match result {
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Err(crate::error::FeaError::Solver(crate::error::SolverError::DimensionMismatch {
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..
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})) => {
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// Expected error
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}
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Err(_) => {
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// Might fail for other reasons if GPU not available
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println!("GPU solver not available or other error occurred");
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}
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Ok(_) => {
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panic!("Should have failed due to dimension mismatch");
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}
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}
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}
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#[test]
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fn test_non_symmetric_matrix_error() {
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let matrix = create_general_matrix(3);
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let rhs = create_rhs(3);
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let options = SolverOptions::default();
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// Test that Cholesky solver rejects non-symmetric matrices
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let mut chol_solver = GpuCholeskyDirect::new();
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let chol_result = chol_solver.solve(&matrix, &rhs, &options);
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match chol_result {
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Err(crate::error::FeaError::Solver(crate::error::SolverError::MatrixNotSymmetric)) => {
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// Expected error
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}
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Err(_) => {
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// Might fail for other reasons if GPU not available
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println!("GPU solver not available or other error occurred");
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}
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Ok(_) => {
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panic!("Cholesky solver should reject non-symmetric matrices");
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}
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}
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// Test that CG solver rejects non-symmetric matrices
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let mut cg_solver = GpuConjugateGradient::new();
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let cg_result = cg_solver.solve(&matrix, &rhs, &options);
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match cg_result {
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Err(crate::error::FeaError::Solver(crate::error::SolverError::MatrixNotSymmetric)) => {
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// Expected error
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}
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Err(_) => {
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// Might fail for other reasons if GPU not available
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println!("GPU solver not available or other error occurred");
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}
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Ok(_) => {
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panic!("CG solver should reject non-symmetric matrices");
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}
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}
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}
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#[test]
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fn test_solver_name_consistency() {
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let chol_solver = GpuCholeskyDirect::new();
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assert_eq!(chol_solver.name(), "GPU cuSOLVER Cholesky Direct");
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let lu_solver = GpuLuDirect::new();
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assert_eq!(lu_solver.name(), "GPU cuSOLVER LU Direct");
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let ldlt_solver = GpuLdltDirect::new();
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assert_eq!(ldlt_solver.name(), "GPU cuSOLVER LDLT Direct");
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let cg_solver = GpuConjugateGradient::new();
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assert_eq!(cg_solver.name(), "GPU cuSPARSE Conjugate Gradient");
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}
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#[test]
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fn test_solver_supports_gpu() {
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let chol_solver = GpuCholeskyDirect::new();
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let lu_solver = GpuLuDirect::new();
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let ldlt_solver = GpuLdltDirect::new();
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let cg_solver = GpuConjugateGradient::new();
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// These should return true if GPU is available, false otherwise
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// The exact value depends on the test environment
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let _ = chol_solver.supports_gpu();
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let _ = lu_solver.supports_gpu();
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let _ = ldlt_solver.supports_gpu();
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let _ = cg_solver.supports_gpu();
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}
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#[test]
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fn test_multiple_rhs_capability() {
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let matrix = create_spd_matrix(3);
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let mut rhs_matrix = nalgebra::DMatrix::zeros(3, 2);
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rhs_matrix.set_column(0, &create_rhs(3));
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rhs_matrix.set_column(1, &DVector::from_vec(vec![3.0, 2.0, 1.0]));
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let options = SolverOptions::default();
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let mut solver = GpuCholeskyDirect::new();
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let result = solver.solve_multiple(&matrix, &rhs_matrix, &options);
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match result {
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Ok((solutions, info)) => {
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assert_eq!(solutions.nrows(), 3);
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assert_eq!(solutions.ncols(), 2);
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assert!(info.converged);
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assert!(info.solve_time.as_secs_f64() >= 0.0);
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// Verify each solution
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for col in 0..2 {
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let solution = solutions.column(col);
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let rhs = rhs_matrix.column(col);
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let computed_rhs = matrix.multiply_vector(&solution.into_owned()).unwrap();
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let residual = (&computed_rhs - &rhs.into_owned()).norm();
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assert!(
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residual < 1e-10,
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"Residual too large for column {}: {}",
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col,
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residual
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);
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}
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}
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Err(_) => {
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// GPU might not be available in test environment
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println!("GPU solver not available, test skipped");
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}
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}
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}
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#[test]
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fn test_iterative_refinement_option() {
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let matrix = create_spd_matrix(3);
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let rhs = create_rhs(3);
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let mut options = SolverOptions::default();
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options.iterative_refinement = true;
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options.tolerance = 1e-12;
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let mut solver = GpuCholeskyDirect::new();
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let result = solver.solve(&matrix, &rhs, &options);
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match result {
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Ok((solution, info)) => {
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assert_eq!(solution.len(), 3);
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assert!(info.converged);
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// With iterative refinement, the solution should be very accurate
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let computed_rhs = matrix.multiply_vector(&solution).unwrap();
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let residual = (&computed_rhs - &rhs).norm();
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assert!(
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residual < 1e-10,
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"Residual too large with refinement: {}",
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residual
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);
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}
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Err(_) => {
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// GPU might not be available in test environment
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println!("GPU solver not available, test skipped");
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}
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}
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}
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}
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