feat: Implement CUDA acceleration for Ricci observable

[JorenB]

This commit introduces a CUDA-based implementation for the average sphere distance calculation within the Ricci observable. The goal is to leverage GPU parallelism to speed up this computationally intensive part of the simulation.

Key changes include:

1. Makefile Updates: Modified the Makefile to support NVCC for compiling .cu files. It now correctly handles CUDA source files, dependencies, and links against CUDA libraries.

2. CUDA Kernels (observables/ricci_cuda_kernels.cu, .hpp):

   • Introduced pairwise_bfs_kernel, a CUDA kernel that computes
     distances between all pairs of vertices from two spheres (s1, s2).
   • Each pair's distance is found using a BFS, implemented in the
     __device__ function calculate_distance_bfs_device. This BFS
     is depth-limited to 3*epsilon and uses thread-local fixed-size
     arrays for its queue and visited set to manage resources.
   • A C++ wrapper function,
     RicciCUDATask::calculate_sum_and_count_distances_cuda,
     manages GPU memory allocation, data conversion (adjacency list to
     CSR format), H2D/D2H transfers, kernel launch, and cleanup.

3. Ricci Observable Modification (observables/ricci.cpp):

   • The Ricci::averageSphereDistance method now calls the
     CUDA wrapper function instead of performing the BFS calculations
     on the CPU.
   • The original logic for sphere generation and the specific averaging
     formula (sum_distances / (epsilon * count_distances)) are
     preserved.
   • Added safety checks for empty data structures and zero epsilon.

This implementation aims to replace the previous CPU-bound calculation with a parallel GPU version. Further testing and validation in a compiled environment are needed to verify correctness, performance, and robustness, especially concerning the fixed-size limitations in the per-thread BFS.