Day 18: Error Handling & cudaGetErrorString()

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7 分钟

CUDA provides powerful parallel computing capabilities, but errors in CUDA programs can be hard to detect and debug if not handled properly. CUDA API calls and kernel launches do not crash immediately on failure; instead, errors propagate silently and may lead to incorrect behavior or undefined results.

Today’s lesson will focus on:

• Understanding CUDA error handling mechanisms.
• Using cudaGetErrorString() to get human-readable error messages.
• Implementing robust error checks after each CUDA call.
• Common debugging pitfalls and how to detect them.
• Using CUDA's built-in cudaPeekAtLastError() and cudaGetLastError() to identify issues.

By the end of this lesson, you’ll be able to write CUDA programs with proper error handling to detect and fix issues efficiently.

---

Table of Contents

1. Overview
2. Understanding CUDA Error Handling
3. Using cudaGetErrorString()
4. Practical Exercise: Implementing Robust Error Checking
   • a) Naïve CUDA Program Without Error Handling
   • b) Improved CUDA Program With Error Handling
5. Common Debugging Pitfalls
6. Conceptual Diagrams
7. References & Further Reading
8. Conclusion
9. Next Steps

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1. Overview

CUDA does not automatically halt execution when an error occurs. Instead:

• CUDA calls return an error code.
• The developer must explicitly check these errors.
• cudaGetLastError() retrieves the most recent error.
• cudaGetErrorString() converts error codes to human-readable messages.

To prevent hard-to-trace bugs, error handling should be added to every CUDA call.

Example: Without Error Handling

cudaMalloc((void**)&d_A, size); // If this fails, no warning is given.
cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice); // If previous failed, this may cause segmentation faults.

Example: With Error Handling

cudaError_t err = cudaMalloc((void**)&d_A, size);
if (err != cudaSuccess) {
    printf("CUDA malloc failed: %s\n", cudaGetErrorString(err));
    return -1;
}

---

2. Understanding CUDA Error Handling

CUDA Error Codes

CUDA functions return an error code of type cudaError_t, which should be checked after every API call.

Error Code	Description
cudaSuccess	No errors.
cudaErrorInvalidDevicePointer	Invalid device pointer used.
cudaErrorInvalidValue	Invalid argument passed to a function.
cudaErrorMemoryAllocation	Failed memory allocation (e.g., cudaMalloc()).
cudaErrorLaunchFailure	Kernel launch failed (e.g., invalid kernel arguments).

Retrieving Error Messages

CUDA provides two functions to handle error states:

1. cudaGetLastError() – Returns and clears the last error.
2. cudaPeekAtLastError() – Returns the last error but does not clear it.
3. cudaGetErrorString(cudaError_t error) – Converts error codes into readable messages.

Example

cudaMalloc((void**)&d_A, size);
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess) {
    printf("CUDA Error: %s\n", cudaGetErrorString(err));
}

---

3. Using cudaGetErrorString()

The cudaGetErrorString(cudaError_t error) function converts an error code into a human-readable string.

Example: Checking for CUDA Errors

cudaError_t err = cudaMalloc((void**)&d_A, size);
if (err != cudaSuccess) {
    printf("CUDA Malloc Failed: %s\n", cudaGetErrorString(err));
}

Example: Macro for Error Checking

To avoid repetitive code, we can define a macro for error checking:

#define CUDA_CHECK(call) { \
    cudaError_t err = call; \
    if (err != cudaSuccess) { \
        printf("CUDA Error at %s:%d - %s\n", __FILE__, __LINE__, cudaGetErrorString(err)); \
        exit(EXIT_FAILURE); \
    } \
}

Usage:

CUDA_CHECK(cudaMalloc((void**)&d_A, size));
CUDA_CHECK(cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice));

---

4. Practical Exercise: Implementing Robust Error Checking

a) Naïve CUDA Program Without Error Handling

// naiveErrorHandling.cu
#include <cuda_runtime.h>
#include <stdio.h>

__global__ void faultyKernel(int *d_data) {
    int idx = threadIdx.x;
    d_data[idx] = idx * 2; // Possible out-of-bounds access
}

int main() {
    int *d_data;
    cudaMalloc((void**)&d_data, 100 * sizeof(int)); // No error check

    faultyKernel<<<1, 256>>>(d_data); // Too many threads for allocated memory

    cudaMemcpy(d_data, d_data, 100 * sizeof(int), cudaMemcpyDeviceToHost); // No check
    printf("Done\n");
    return 0;
}

Issues:

• cudaMalloc() might fail, but the error is ignored.
• The kernel launches with more threads than allocated memory, causing memory corruption.
• No error checks mean we wouldn’t detect the problem.

---

b) Improved CUDA Program With Error Handling

// robustErrorHandling.cu
#include <cuda_runtime.h>
#include <stdio.h>

// Macro for error checking
#define CUDA_CHECK(call) { \
    cudaError_t err = call; \
    if (err != cudaSuccess) { \
        printf("CUDA Error at %s:%d - %s\n", __FILE__, __LINE__, cudaGetErrorString(err)); \
        exit(EXIT_FAILURE); \
    } \
}

// Kernel with boundary check
__global__ void safeKernel(int *d_data, int N) {
    int idx = threadIdx.x;
    if (idx < N) { // Prevents out-of-bounds access
        d_data[idx] = idx * 2;
    }
}

int main() {
    int N = 100;
    int *d_data;

    // Allocate memory with error checking
    CUDA_CHECK(cudaMalloc((void**)&d_data, N * sizeof(int)));

    // Launch kernel safely
    safeKernel<<<1, 128>>>(d_data, N);
    CUDA_CHECK(cudaGetLastError()); // Checks for kernel launch errors

    // Synchronize
    CUDA_CHECK(cudaDeviceSynchronize());

    // Free memory
    CUDA_CHECK(cudaFree(d_data));

    printf("Execution completed successfully\n");
    return 0;
}

Fixes:

• cudaMalloc() is checked to ensure successful allocation.
• Kernel launch is checked for errors.
• Boundary check prevents out-of-bounds access.
• cudaDeviceSynchronize() ensures completion.

---

5. Common Debugging Pitfalls

Mistake	Solution
Not checking for errors after CUDA calls	Always use cudaGetLastError() or macros.
Ignoring kernel launch errors	Use cudaPeekAtLastError() to detect kernel issues.
Accessing out-of-bounds memory	Always add boundary checks inside kernels.
Using uninitialized memory	Always check cudaMalloc() return value.

---

6. Conceptual Diagrams

Diagram 1: Error Checking Workflow

flowchart TD
    A[CUDA API Call] --> B[Check for Errors]
    B -- No Error --> C[Continue Execution]
    B -- Error Detected --> D[Print Error Message]
    D --> E[Exit or Handle Error]

---

7. References & Further Reading

1. CUDA Error Handling Guide
2. CUDA Best Practices
3. "Programming Massively Parallel Processors" – David Kirk & Wen-mei W. Hwu

---

8. Conclusion

Today, we explored:

• Why CUDA error handling is critical.
• How to use cudaGetErrorString() to interpret errors.
• Best practices for implementing robust error checking.
• Debugging strategies for common CUDA issues.

---

9. Next Steps

• Experiment with forcing CUDA errors to test error handling.
• Use Nsight Compute to debug CUDA programs.

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