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Overview#

Aerial cuMAC provides a library of CUDA-accelerated MAC scheduling algorithms for the 5G/6G RAN. The algorithms can be integrated into the Layer-2 (L2) stack of the RAN system to offload computationally intensive MAC scheduling functions from the host CPU to the GPU. cuMAC is designed to enhance the spectral efficiency of 5G/6G RAN and alleviate compute bottlenecks inherent in conventional CPU-based MAC schedulers. It serves as an open-source reference implementation for accelerating 5G/6G MAC scheduling on NVIDIA GPUs.

The Core Problem: The Compute Bottleneck in MAC Scheduling#

The MAC scheduler is a critical component of the L2 stack in the 5G/6G RAN. It is responsible for allocating time-frequency radio resources to the user equipment (UEs) within each cell, and its performance directly impacts the spectral efficiency and overall user experience of the RAN system.

  • Real-time constraints in L2 stack - In general, MAC scheduling involves solving complex optimization problems to efficiently utilize the available radio resources under strict real-time constraints - typically on the order of milliseconds or even sub-milliseconds for 5G/6G services, e.g., URLLC, eMBB, and mMTC. This imposes heavy computational demands on the L2 stack.

  • The CPU bottleneck - However, conventional MAC schedulers running on CPUs are limited by the small number of cores available per cell for real-time decision making. As a result, CPU-only implementations often encounter severe compute bottlenecks, hindering the adoption of many high-performance yet computationally intensive scheduling algorithms within the latency bounds required by the L2 stack.

  • The future is more complex - Future AI-native RANs, which use AI/ML techniques for tasks such as resource allocation and interference management and require real-time inference of neural network models, will impose even greater computational challenges on traditional CPU-only architectures.

The Solution: CUDA-Accelerated MAC Scheduling#

To address these challenges, NVIDIA has developed cuMAC, a library of CUDA-accelerated MAC scheduling algorithms designed to offload compute-intensive scheduling functions from the L2 stack host to the GPU.

  • Why CUDA/GPU? - cuMAC leverages the massive parallel processing capabilities of CUDA to accelerate scheduling algorithms, which inherently exhibit high degrees of parallelism in their computational steps. This enables the deployment of advanced, compute-intensive scheduling algorithms in the RAN, delivering scalable and high-performance MAC scheduling within the strict latency constraints of the L2 stack.

  • What we provide - cuMAC provides a reference design/implementation demonstrating how to:

    • Offload complex L2 MAC scheduler algorithms from the CPU to the GPU.

    • Implement advanced, compute-intensive scheduling algorithms in CUDA.

    • Achieve the real-time performance requirements for the L2 stack.

    • Integrate with common RAN L2 stacks (with a reference framework).

  • Key insights - We view the MAC scheduler as a high-throughput, parallelizable data pipeline rather than a collection of sequential tasks executed solely by CPU cores. In addition, the modular implementation of cuMAC algorithms allows seamless integration with common RAN L2 stacks and facilitates the evolution of MAC scheduler design for next-generation RANs.