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Source code for the CPU-Free model - a fully autonomous execution model for multi-GPU applications that completely excludes the involvement of the CPU beyond the initial kernel launch.

Home Page: https://dl.acm.org/doi/10.1145/3577193.3593713

License: MIT License

Makefile 0.11% Cuda 85.73% C++ 5.39% Shell 3.78% Python 3.57% C 0.97% CMake 0.44%
cuda conjugate-gradient iterative-solvers multi-gpu persistent-kernels high-performance jacobi jacobi-iteration stencil gpu-initiated-communication

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cpu-free-model's Issues

Reordering statements

I think we can change the tiling comm part. I moved the tile start and end calculation after the sync point. Also convert the variables to locally defined vars. Test them separately.

  for (comm_tile_idx = 0; comm_tile_idx < num_comm_tiles; comm_tile_idx++) {

            int cur_iter_comm_tile_flag_idx = comm_tile_idx + cur_iter_mod * num_flags;

            if (cta.thread_rank() == 0) {
                while (local_is_top_neighbor_done_writing_to_me[cur_iter_comm_tile_flag_idx] !=
                       iter) {
                }
            }

            cg::sync(cta);

            int comm_tile_start = (comm_tile_idx == 0) ? 1 : comm_tile_idx * comm_tile_size;
            int comm_tile_end = (comm_tile_idx == (num_comm_tiles - 1))
                                ? nx - 1
                                : (comm_tile_idx + 1) * comm_tile_size;

            int col = threadIdx.y * blockDim.x + threadIdx.x + comm_tile_start;

            if (col < comm_tile_end) {
                const real first_row_val =
                    0.25 * (a[iy_start * nx + col + 1] + a[iy_start * nx + col - 1] +
                            a[(iy_start + 1) * nx + col] +
                            remote_my_halo_buffer_on_top_neighbor[nx * cur_iter_mod + col]);

                a_new[iy_start * nx + col] = first_row_val;
                local_halo_buffer_for_top_neighbor[nx * next_iter_mod + col] = first_row_val;
            }

            cg::sync(cta);

            if (cta.thread_rank() == 0) {
                int next_iter_comm_tile_flag_idx =
                (num_comm_tiles + comm_tile_idx) + next_iter_mod * num_flags;
                remote_am_done_writing_to_top_neighbor[next_iter_comm_tile_flag_idx] = iter + 1;
            }
        }

Issue regarding running on Single node Multi GPU

I am launching a program on a single machine with two GPUs, and it seems that MPI_Comm_split_type did not successfully split the communicator into subcommunicators. In the end, both processes are executing on device 0. Please see the attached screenshot for reference. Looking forward to your response.
截屏2024-01-05 17 16 11

Typo in the spack command

Just a simple typo in the spack install command:
$ spack install nvshmem +gpu_initiated_suppot +cuda +gdrcopy +ucx +mpi ^openmpi +cuda fabrics=ucx ^ucx +cuda +gdrcopy +dm +thread_multiple
just needs an "r" in support so:
$ spack install nvshmem +gpu_initiated_support +cuda +gdrcopy +ucx +mpi ^openmpi +cuda fabrics=ucx ^ucx +cuda +gdrcopy +dm +thread_multiple

CMake compile issue

When I installed the required dependencies (including nvshmem) and ran cmake I encountered the problem in the picture below, have you encountered it?
截屏2023-12-22 15 26 46

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