RCCL Library: AMD GPU Communication

• RCCL Library is a vendor-specific communication library that defines GPU collectives like all-reduce and broadcast for AMD accelerator systems.
• It employs a ring algorithm for operations, providing solid bandwidth performance with challenges in latency scaling beyond moderate GPU counts.
• The library interoperates with alternatives such as MSCCL++ and HiCCL, enhancing scalability and performance in distributed deep learning and scientific applications.

RCCL (Radeon Collective Communication Library) is a vendor-specific, open-source communication library designed to support high-performance GPU collectives primarily on AMD accelerator platforms. It implements a set of collective primitives such as all-reduce, all-gather, reduce-scatter, broadcast, and reduce, and exposes an API largely modeled after NCCL (NVIDIA Collective Communications Library), facilitating drop-in compatibility for distributed deep learning and scientific workloads on systems utilizing AMD devices. RCCL has become a standard component in AMD GPU clusters, underpinning large-scale distributed training and inference in the cloud and supercomputing environments. RCCL’s development, integration strategies, and performance characteristics have been extensively evaluated in comparison with and in combination with other GPU communication stacks (Hidayetoglu et al., 12 Aug 2024, Shah et al., 11 Apr 2025, Singh et al., 25 Apr 2025).

1. Design and API Characteristics

RCCL adopts the widely used collective communication API model, mirroring NCCL’s interfaces to ensure compatibility with existing deep learning and simulation frameworks that target both vendor-agnostic and vendor-specific GPU platforms. This API includes the following high-level collectives:

• AllReduce
• AllGather
• ReduceScatter
• Broadcast
• Reduce

The API supports both user-level and system-level bootstrapping, enabling integration into containerized and high-performance computing environments. Applications leveraging the NCCL programming model can typically switch to RCCL simply by relinking, provided AMD GPUs are present in the system. RCCL supports both GPU-direct and host-based communication paths depending on the system topology.

2. Implementation Strategies

RCCL’s internal implementation leverages the ring algorithm as its primary strategy for all-gather and reduce-scatter operations. This approach involves each participating GPU sending and receiving (p – 1) sequential messages around a logical ring, where p is the number of processes (GPUs). The operational complexity thus scales linearly with the number of GPUs, with latency dominated by repeated startup costs and bandwidth utilization distributed across the communication path. The algorithmic performance is formally modeled as

$$T_{ring} = \alpha \cdot (p-1) + \beta \cdot \frac{p-1}{p} \cdot m$$

where α denotes startup latency per message, β is the inverse link bandwidth, and m is the message size (Singh et al., 25 Apr 2025).

RCCL also utilizes GPU-based reduction kernels. This design offloads computational steps from CPU to GPU, maximizing intra-node bandwidth and throughput—an essential capability for deep learning workloads.

3. Limitations and Scalability Challenges

While RCCL achieves high bandwidth efficiency for small and moderate numbers of GPUs, several limitations are evident at the scale of hundreds to thousands of accelerators:

• Linear Latency Growth: The exclusive reliance on the ring algorithm leads to latency scaling poorly with process count. Beyond approximately 256 GPUs, startup and sequential message overheads become dominant, resulting in sharp performance degradation.
• Algorithmic Rigidity: RCCL does not provide alternative algorithms (e.g., recursive doubling for all-gather, recursive halving for reduce-scatter), limiting its ability to exploit lower-latency communication patterns in large-scale deployments.
• Underutilization of Hierarchies and NICs: Unlike hierarchical or auto-striping approaches, RCCL does not optimally balance traffic across multiple NICs in multi-NIC systems, which can cause bottlenecks at high parallelism levels.

These shortcomings translate directly to practical constraints in LLM training on top-tier supercomputers. For example, on the Frontier system in large-scale GPT-3–style training, PCCL achieves 6–33× speedup over RCCL in collective microbenchmarks and up to 60% overall training speedup for 7B parameter models (Singh et al., 25 Apr 2025).

4. Integration with Alternative and Hybrid Communication Libraries

RCCL’s API-level compatibility allows for the substitution or integration of portable, extensible communication libraries such as MSCCL++ (Shah et al., 11 Apr 2025) and HiCCL (Hidayetoglu et al., 12 Aug 2024). MSCCL++ introduces a primitive interface—put, get, signal, wait, flush—that enables asynchronous, zero-copy, and low-overhead data movement and synchronization. RCCL can delegate the execution of a collective to MSCCL++ kernels, which can exploit hardware-specific features not accessible in the original RCCL design. This interoperability ensures applications written to RCCL’s NCCL-style API can leverage MSCCL++ performance enhancements without code modification.

HiCCL further demonstrates that, by abstracting the communication logic from network-specific optimizations, it can match or outperform RCCL on AMD, Nvidia, and Intel GPUs in diverse hierarchical configurations. HiCCL’s throughput is reported to be comparable or superior to RCCL (e.g., 1.27× speedup averaged over benchmarks, and in some cases 17× over MPI collectives) (Hidayetoglu et al., 12 Aug 2024). Its compositional API—multicast, reduction, fence—permits portable performance optimization across heterogeneous hardware, while RCCL remains limited to AMD-specific environments.

5. Hierarchical and Scalable Alternatives

For very large clusters and supercomputing deployments, alternatives to RCCL’s single-level ring algorithm have proven substantially more scalable. Libraries such as PCCL (Singh et al., 25 Apr 2025) introduce hierarchical communication strategies:

• Intra-node: Utilizes vendor libraries (RCCL for AMD) for local GPU communication.
• Inter-node: Employs scalable algorithms (recursive doubling, recursive halving), modeled as

$$T_{rec} = \alpha \cdot \log_2(p) + \beta \cdot \frac{p-1}{p} \cdot m$$

This logarithmic scaling significantly outperforms the linear scaling of ring-based approaches at high GPU counts. PCCL’s design, combined with balanced NIC utilization and GPU-offloaded reduction kernels, leads to dramatic throughput improvements for large message collectives and deep learning workloads.

6. Application Domains and Use Cases

RCCL is employed extensively in distributed deep learning and scientific computing applications on AMD platforms. Its ring-based collectives suit workloads where bandwidth is the bottleneck and node count is moderate. However, for cutting-edge AI workloads and large-scale GPT-like model training, hierarchical and extensible libraries such as PCCL, MSCCL++, and HiCCL offer superior scalability and performance, achieving up to ~33× faster microbenchmarks and substantial reductions in training time (Shah et al., 11 Apr 2025, Singh et al., 25 Apr 2025, Hidayetoglu et al., 12 Aug 2024).

7. Open-Source Status and Community Ecosystem

RCCL is released as open source by AMD and maintained on GitHub, aligning with the licensing and collaborative practices of the GPU software ecosystem. The open-source model facilitates rapid bug fixes, hardware support updates, and ongoing integration of advanced communication algorithms contributed by both academic and industrial researchers. RCCL’s interoperability with emerging communication libraries fosters an ecosystem where both portability and hardware-specific optimization can advance rapidly, supported by a broad user and developer community.

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In summary, RCCL embodies a vendor-focused, high-performance collective communication library designed for AMD GPU clusters, achieving robust bandwidth at moderate scales but limited by linear latency growth and lack of algorithmic diversity. Recent research demonstrates that hybrid and hierarchical communication stacks—including MSCCL++, HiCCL, and PCCL—can integrate with or outperform RCCL, particularly in large and heterogeneous environments, by offering flexible primitives, optimized topologies, and scalable all-gather/reduce-scatter strategies. RCCL continues to serve as an essential bridge for AMD GPU deployments while enabling the incorporation of innovation from extensible, open-source alternatives.