CUDA Kernel Returns Wrong Results

Hey everyone, I've been wrestling with a pretty strange issue, and I'm hoping you all can lend a hand. I'm running into a bug where a CUDA kernel that I'm working on is spitting out incorrect results with newer versions of CUDA (specifically, 12.6 and later). The really perplexing part is that the same code works perfectly fine with older CUDA versions (up to 12.2). To make things even weirder, when I run the code on a CPU or an AMD GPU, everything works as expected. This strongly suggests that the problem is tied to the CUDA backend.

I've put together a minimal reproducer to help illustrate the issue. You can find it at https://github.com/nrbertin/test-neighbor. It's based on a neighbor list setup, which is used to find nearby points. The core of the problem lies within two kernels implemented in a functor, NeighborFunctor. Let's dive into the code.

Understanding the Kernels

The reproducer centers around two primary kernels: Kernel1 (with Tag1) and Kernel2 (with Tag2). Both kernels are designed to calculate essentially the same thing. The key difference lies in how they're launched and the structure of their internal loops. Kernel1 has Npoints threads, and each thread executes an inner loop of size Nbox. Kernel2, on the other hand, directly launches Npoints * Nbox threads without an inner loop. The ultimate goal for both kernels is to compute a sum based on calculations involving neighboring boxes. The code snippet below shows the core parts of the kernels and how they are used in the program.

struct NeighborFunctor {
    NeighborBox* neighbox;

    NeighborFunctor(NeighborBox* _neighbox) : neighbox(_neighbox) {}

    // Kernel 1
    KOKKOS_INLINE_FUNCTION
    void operator() (Tag1, const int& t, int& sum) const {
        int i = t; // point id

        Vec3 p = neighbox->get_point_pos(i);
        Vec3i id = neighbox->find_box_coord(p);

        for (int ibox = 0; ibox < Nbox; ibox++) {
            Vec3i shift;
            neighbox->neighbor_shift(id, ibox, shift);
            sum += abs(shift.x) + abs(shift.y) + abs(shift.z);
        }
    }

    // Kernel 2
    KOKKOS_INLINE_FUNCTION
    void operator() (Tag2, const int& t, int& sum) const {
        int ibox = t % Nbox; // box id
        int i = t / Nbox; // point id

        Vec3 p = neighbox->get_point_pos(i);
        Vec3i id = neighbox->find_box_coord(p);

        Vec3i shift;
        neighbox->neighbor_shift(id, ibox, shift);
        sum += abs(shift.x) + abs(shift.y) + abs(shift.z);
    }
};

The above code demonstrates the structural elements of both kernels. The critical operation, neighbox->neighbor_shift(id, ibox, shift), is meant to compute a shift based on the box and point IDs. The results from the kernels are then accumulated to produce a final sum value. The differences in launching and structure of the kernels are intended to expose potential issues that might arise within the CUDA runtime environment, as it processes and schedules the threads.

Compilation and Execution

The reproducer can be compiled and run in different configurations. To run it on the CPU, you would use the -DKokkos_ENABLE_SERIAL=On flag during compilation. To run on a GPU with CUDA, you'd typically use -DKokkos_ENABLE_CUDA=On along with an architecture flag like -DKokkos_ARCH_HOPPER90=On, although this depends on your specific GPU architecture. When compiled and run on a CPU, or using CUDA <= 12.2, both kernels produce the same correct result:

(base) bash-4.4$ ./test_neighbor
Kernel Tag1: 189
Kernel Tag2: 189

The Problem: Incorrect Results with CUDA 12.6+

Now, here's where things go haywire. When I run the same code with newer versions of CUDA (12.6 or later, I tested with 12.6 and 12.9), Kernel2 returns the wrong answer:

(base) bash-4.4$ ./test_neighbor
Kernel Tag1: 189
Kernel Tag2: 8100

As you can see, Kernel1 still gives the correct result, but Kernel2's output is drastically off. Through debugging, I found that the function neighbox->neighbor_shift(id, ibox, shift) is called with the identical arguments across all indices for both kernels. However, the outcome saved in the variable shift is different in Kernel2. It's as if a condition in Kernel2 always evaluates as true, which leads to incorrect calculations. This is the central challenge.

Diving Deeper into the Issue

The discrepancy between the two kernels is a bit of a head-scratcher. Both kernels are designed to compute similar things but go about it in slightly different ways. The significant difference is the thread organization: Kernel1 uses a nested loop approach, while Kernel2 leverages a flattened thread structure. One of the most plausible explanations could be a subtle difference in how the CUDA runtime handles memory access or thread synchronization within the Kernel2's flatter thread structure. The neighbox->neighbor_shift function is at the core of the calculation, and a potential issue in how the shift vectors are being calculated or stored could cause the error.

Since both kernels call the same neighbor_shift with the same arguments but obtain different results in the newer CUDA versions, the source of the issue might lie within the memory model, especially if there's any shared memory usage or dependency within this function. Another place where the problem could be hiding is the compiler or CUDA driver itself. Sometimes, compiler optimizations or changes in the driver's behavior can lead to unexpected results, especially in complex kernel code. Another area of concern is the Vec3i shift variable. It's critical to ensure that how this is initialized, updated, and used is done correctly across different CUDA versions. Small changes can cause significant problems in calculations that involve the hardware's specific way of handling the data.

Possible Causes and Troubleshooting

Identifying the root cause can be complex, but here are some key areas to investigate:

1. CUDA Driver and Compiler: Make sure your CUDA driver is compatible with your CUDA version. Ensure you are using a compatible compiler (like GCC) to build your code. Update both the driver and the compiler and see if it fixes the problem. Sometimes, there might be issues with older drivers, leading to unexpected behavior.
2. Memory Access Patterns: Analyze the memory access patterns in both kernels. CUDA memory coalescing is critical for performance; any uncoalesced access might lead to issues or slowdowns. Check if Kernel2 has any uncoalesced memory accesses that are causing problems in the newer CUDA versions.
3. Thread Synchronization: Even though the reproducer doesn't explicitly use complex thread synchronization primitives, consider the possibility that there might be implicit dependencies or data races. Review the code to ensure that there are no unexpected race conditions. Make sure each thread has its own separate memory space for its calculations.
4. Compiler Optimizations: Experiment with different compiler flags. Sometimes, the compiler's optimization can introduce unexpected behavior. Try disabling certain optimizations (like -O0) or using different optimization levels (-O2 or -O3) to see if it affects the outcome. Compiler settings can dramatically alter the behavior of the code.
5. Data Alignment: Ensure that your data is properly aligned. Improper alignment can lead to performance issues or even incorrect results on GPUs. Check the alignment of the Vec3i data structure and the shift variable to see if it is properly aligned for the GPU. Make sure that the data is aligned to a multiple of the word size.
6. Debugging Tools: Use CUDA debugging tools like cuda-gdb or Nsight Systems to get more information about the kernel execution. These tools can help you step through the code, inspect variables, and analyze the memory access patterns.

Conclusion

I'm hoping that by sharing this, someone might have encountered a similar issue or has some insights that can help me. I will keep digging, of course, and update this thread as I find more information. The fact that the problem is specifically tied to recent CUDA versions and doesn't appear on the CPU or older CUDA versions points towards a subtle interaction within the CUDA runtime or compiler. Any advice or ideas are welcome!

For more in-depth information, you can check the NVIDIA CUDA Toolkit Documentation.

NVIDIA CUDA Toolkit Documentation