Troubleshooting Common CULA Basic Issues — Fast FixesCULA Basic is a widely used GPU-accelerated linear algebra library designed to speed up BLAS and LAPACK operations. While powerful, users sometimes encounter issues that slow development or lead to incorrect results. This article walks through the most common problems with CULA Basic, explains their likely causes, and gives fast, practical fixes you can apply now.
1. Installation and Environment Problems
Symptoms:
- CULA libraries not found at compile or runtime.
- Linker errors like “undefined reference” for CULA functions.
- Runtime errors indicating missing shared libraries (e.g., libcula.so).
Likely causes:
- CULA not installed correctly.
- Library paths (LD_LIBRARY_PATH on Linux, PATH on Windows) not set.
- Mismatch between compiled binary architecture (CUDA version, ⁄64-bit) and installed CULA/CUDA.
Fast fixes:
- Verify installation: confirm the CULA installation directory contains lib and include folders.
- Set environment variables:
- Linux:
export LD_LIBRARY_PATH=/path/to/cula/lib:$LD_LIBRARY_PATH export PATH=/path/to/cuda/bin:$PATH - Windows:
- Add CULA and CUDA bin directories to PATH via System Properties → Environment Variables.
- Linux:
- Check CUDA compatibility: match CULA Basic version with your CUDA toolkit. If versions mismatch, install a compatible CULA build or the correct CUDA toolkit.
- Verify architecture: ensure your compiler target (x86_64) matches the installed libraries.
2. Compilation and Linking Errors
Symptoms:
- Compiler cannot find cula.h or related headers.
- Undefined references during linking.
Likely causes:
- Include and linker flags missing or incorrect.
- Using wrong compiler (e.g., host compiler incompatible with CUDA toolchain).
Fast fixes:
- Add include and library flags to your build:
- Example (gcc/g++):
g++ myprog.cpp -I/path/to/cula/include -L/path/to/cula/lib -lcula -lcublas -lcudart -o myprog
- Example (gcc/g++):
- For CMake, add:
include_directories(/path/to/cula/include) link_directories(/path/to/cula/lib) target_link_libraries(myprog cula cublas cudart) - Use the same compiler that CUDA supports (check CUDA documentation for supported host compilers).
- If building 32-bit vs 64-bit, ensure -m64 or -m32 flags and corresponding libraries match.
3. Runtime Crashes or GPU Errors
Symptoms:
- Application crashes when calling CULA functions.
- CUDA errors such as “invalid device function”, “out of memory”, or device reset messages.
Likely causes:
- Insufficient GPU memory for your matrices.
- Running kernels compiled for a different compute capability.
- Resource leaks (not freeing GPU memory).
- Driver/CUDA runtime incompatibilities.
Fast fixes:
- Monitor GPU memory (nvidia-smi) while running your app. Reduce matrix sizes or batch sizes if memory is tight.
- Rebuild or install CULA compiled for your GPU’s compute capability, or ensure CUDA toolkit supports your device.
- Free GPU resources after use: call appropriate CULA/CUDA routines to release memory.
- Update NVIDIA drivers and CUDA runtime to versions compatible with your CULA build.
- Test simple example programs included with CULA to isolate whether the problem is in your code or environment.
4. Incorrect Results or Numerical Instability
Symptoms:
- Outputs differ significantly from CPU BLAS/LAPACK results.
- Non-convergence in algorithms that use CULA routines.
Likely causes:
- Precision mismatches (single vs double).
- Uninitialized memory or improper leading dimensions/strides passed to routines.
- Rounding differences between GPU and CPU implementations.
Fast fixes:
- Ensure you call the correct variant (single-precision vs double-precision) matching your data type (e.g., culaS* for float, culaD* for double).
- Carefully set matrix leading dimensions (lda, ldb, etc.). For column-major libraries like CULA (LAPACK-style), lda must be at least max(1, number_of_rows).
- Initialize arrays before passing them into CULA functions; consider zeroing memory to avoid garbage values:
std::fill_n(A, n*m, 0.0); - Compare tolerances, not exact equality, when validating GPU results against CPU results. Use relative error thresholds based on matrix norms.
- If numerical instability persists, try using double precision or algorithmic alternatives (e.g., pivoting options).
5. Performance Issues (Slower Than Expected)
Symptoms:
- GPU-accelerated code runs slower than a CPU-only implementation.
- Poor scaling with larger matrices.
Likely causes:
- Small problem sizes that don’t amortize GPU transfer/setup overhead.
- Excessive host-device memory transfers.
- Non-optimal use of batched or tiled routines.
- GPU running at reduced performance due to power/thermal limits or other workloads.
Fast fixes:
- Increase problem size per call or batch many small problems together to amortize overhead.
- Minimize host-device transfers: keep data on GPU and perform as many operations as possible before copying back.
- Use asynchronous transfers and CUDA streams if appropriate.
- Use CULA’s batched routines (if available) for many small independent problems.
- Check GPU utilization (nvidia-smi, nvprof, Nsight Systems) to identify bottlenecks.
- Ensure the GPU isn’t being throttled and that the machine has a high-speed PCIe link and sufficient CPU/GPU balance.
6. Licensing and Activation Problems
Symptoms:
- CULA reports licensing errors, refuses to run, or falls back to limited functionality.
Likely causes:
- License file missing or incorrectly placed.
- License tied to a different machine ID or GPU.
Fast fixes:
- Confirm the license file is in the location specified by CULA documentation (often in /etc/cula or CULA installation directory).
- Check license validity and machine binding. Contact your vendor if the license is tied to different hardware.
- For evaluation licenses, ensure expiration hasn’t passed.
7. Integration with Other Libraries (e.g., cuBLAS, cuSOLVER)
Symptoms:
- Conflicts or crashes when using CULA together with other CUDA libraries.
Likely causes:
- Incompatible versions of CUDA-dependent libraries.
- Multiple initializations of CUDA context or conflicting stream usage.
Fast fixes:
- Use consistent CUDA toolkit versions for all libraries.
- Ensure you manage CUDA contexts/streams carefully. Avoid libraries that implicitly assume default streams when you rely on custom streams without coordination.
- Run simple integration tests that call one library at a time, then combine.
8. Debugging Tips and Tools
Quick tactics:
- Run CULA example programs bundled with the distribution to verify your environment.
- Use cuda-memcheck to detect memory errors.
- Use cuda-gdb for GPU debugging and backtraces.
- Use logging and small reproducible test cases to narrow the issue.
- Compare outputs with a CPU LAPACK/BLAS (e.g., OpenBLAS, Intel MKL) to separate correctness from environment problems.
9. When to Contact Support
Consider reaching out to CULA vendor support if:
- You suspect a bug in the library (include a minimal reproducible example).
- Licensing issues persist after verifying installation.
- You need a CULA build for a specific CUDA/compute capability not publicly available.
Provide these when you file a ticket:
- Exact CULA version, CUDA toolkit version, NVIDIA driver version, GPU model.
- Minimal code that reproduces the issue and steps to reproduce.
- Output logs, console errors, and any nvidia-smi / dmesg excerpts showing GPU state.
Quick checklist (fast fixes summary)
- Set LD_LIBRARY_PATH/PATH to include CULA and CUDA.
- Match CULA and CUDA versions.
- Use correct precision (single vs double).
- Set proper leading dimensions/strides for matrices.
- Reduce host-device transfers and batch small problems.
- Monitor GPU memory and utilization (nvidia-smi).
- Run CULA examples and cuda-memcheck.
If you want, I can tailor this troubleshooting guide to your codebase: paste a minimal failing example and your environment details (CUDA version, CULA version, GPU model) and I’ll pinpoint likely fixes.
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