GPU communication overhead is a measurable bottleneck in production AI workloads. According to data cited by the mKernel project, communication can consume 43.6% of the forward pass and 32% of end-to-end training time. Across popular Mixture-of-Experts (MoE) models, inter-device communication can account for up to 47% of total execution time. Researchers from UC Berkeley’s UCCL project have released mKernel, a library of persistent CUDA kernels that fuse intra-node NVLink communication, inter-node RDMA, and compute into a single kernel.
The Problem: Host-Driven Communication
The standard model for multi-GPU communication is host-driven: the CPU runs the control path and calls into a library like NCCL or NVSHMEM. The library issues the collective operation — an AllReduce, an AllGather, etc. — across GPUs. Compute and communication run on separate CUDA streams and overlap at kernel boundaries.
The research team identifies two problems with this approach:
(1) CPUs are not scaling with GPU compute. A GB300 NVL72 rack integrates 72 Blackwell Ultra GPUs and 36 Grace CPUs, delivering 720 PFLOP/s FP8/FP6, 1.44 EFLOP/s FP4 Tensor Core performance, and 130 TB/s of all-to-all intra-rack NVLink bandwidth. At those speeds, microsecond-scale host orchestration overhead — a cudaLaunchKernel call, a CPU-side “all writes done” check, an inter-stream event — shows up directly as pipeline bubbles.
(2) Host-driven systems overlap compute and communication at coarse kernel boundaries. Finer-grained overlap at the tile or chunk level is not possible from the host side.
The alternative is GPU-driven communication: the GPU itself triggers transfers, with communication fused into the same kernel as the compute. Most existing fused kernel libraries operate within a single node, or a single GPU. mKernel targets the multi-node case.
What mKernel Does
mKernel is a library of persistent CUDA kernels. Each kernel fuses intra-node NVLink communication, inter-node RDMA, and dense compute into a single kernel.
Multi-GPU + multi-node, in one kernel: Both intra-node NVLink and inter-node RDMA live inside the same persistent kernel.
Fine-grained intra-kernel overlap: Compute and communication overlap at tile/chunk granularity, covering both intra-node and inter-node GPU communication.
Persistent kernel with SM specialization: CTAs self-assign roles: compute, intra-comm, inter-send, inter-reduce. The number of SMs dedicated to each role is tunable per shape.
GPU-driven networking built on libibverbs: mKernel uses GPU-initiated RDMA writes without depending on NCCL or NVSHMEM. The communication backend is written from scratch to maximize performance and support heterogeneous networking devices.
The Five Fused Kernels
A. While ranks gather peers’ shards over NVLink/RDMA, the local GEMM consumes tiles as soon as they arrive.GEMM + AllReduceGEMM → AllReduceComputes C = A @ B and reduces partial outputs across all ranks in one launch. Output tiles are pushed into the reduction tree the instant they’re produced.MoE Dispatch + GEMMAll-to-All dispatch → grouped GEMMRoutes MoE tokens to their expert ranks (intra-node NVLink + inter-node all-to-all) and runs the per-expert grouped GEMM in the same kernel. Tokens are processed as soon as they land — no staging buffer round-trip.Ring AttentionRing KV exchange → FlashAttentionSequence-parallel attention across ranks. Each step rotates a KV chunk around the ring while the local FlashAttention consumes the previously-received chunk. Compute and the ring send/recv run concurrently inside a single persistent kernel.GEMM + ReduceScatterGEMM → ReduceScatterComputes C = A @ B and reduce-scatters the output. Each output tile is reduced and forwarded to its owning rank as soon as it is produced.Evaluation Setup
The research team evaluated mKernel on two 2-node × 8-H200 clusters that differ only in their inter-node fabric:
mKernel was benchmarked against NCCL, Triton-distributed, Flux, Mercury, MagiAttention, Transformer-Engine, and ring-flash-attention. The team notes that further benchmarking at larger scale is still in progress.
Backends and Requirements
mKernel supports two networking backends:
-DINTERNODE_BACKEND_IBVERBSlibibverbs RCConnectX-7 / InfiniBand / RoCEEFA-DINTERNODE_BACKEND_EFAlibibverbs + efadv (SRD)AWS p5/p5e (H200, EFA)Both backends share the same host-side API and the same on-GPU kernel. Only the proxy/session implementation differs (session.h for CX7, session_efa.h for EFA). Requirements: NVIDIA Hopper GPUs (default build targets sm_90a), CUDA 12.9, Python with PyTorch. The CX7 backend requires libibverbs development headers and libraries. The EFA backend requires AWS EFA installation with libfabric, libibverbs, efadv, and EFA headers under EFA_HOME=/opt/amazon/efa by default.
Marktechpost’s Visual Explainer
UCCL mKernel — Multi-GPU, Multi-Node Fused Kernels Guide 01 / 07 — OverviewWhat is mKernel?
mKernel is an open-source library of persistent CUDA kernels from UC Berkeley’s UCCL project. It fuses intra-node NVLink communication, inter-node RDMA, and dense compute into a single kernel.
Most existing fused kernel libraries operate within a single node or a single GPU. mKernel is designed from the start to span node boundaries.
43.6% of forward pass consumed by communication in production 47% of total execution time in popular MoE models 32% of end-to-end training time consumed by communication 02 / 07 — The ProblemWhy Host-Driven Communication Falls Short
The standard model is host-driven: the CPU calls NCCL or NVSHMEM, which issues collective operations across GPUs. The UCCL team identifies two problems.
cudaLaunchKernel, CPU-side sync checks, and inter-stream events shows up directly as pipeline bubbles.
Four Core Design Properties
🖧 Multi-GPU + multi-node, in one kernel. Intra-node NVLink and inter-node RDMA both live inside the same persistent kernel.
compute, intra-comm, inter-send, inter-reduce. SM split is tunable per shape.
libibverbs. Uses GPU-initiated RDMA writes. No NCCL or NVSHMEM dependency. Communication backend is written from scratch.
04 / 07 — Kernels
The Five Fused Kernels
AllGather + GEMM AllGather —> GEMM Each rank holds a shard ofA. The local GEMM consumes tiles over NVLink/RDMA as they arrive — matmul starts before the collective finishes.
GEMM + AllReduce
GEMM —> AllReduce
Computes C = A @ B and reduces partial outputs across all ranks in one launch. Output tiles enter the reduction tree the instant they are produced.
MoE Dispatch + GEMM
All-to-All dispatch —> grouped GEMM
Routes MoE tokens to expert ranks via NVLink + inter-node all-to-all, then runs per-expert grouped GEMM in the same kernel. No staging buffer round-trip.
Ring Attention
Ring KV exchange —> FlashAttention
Sequence-parallel attention across ranks. Each step rotates a KV chunk around the ring while the local FlashAttention consumes the previously-received chunk.
GEMM + ReduceScatter
GEMM —> ReduceScatter
Computes C = A @ B and reduce-scatters the output. Each tile is reduced and forwarded to its owning rank as soon as it is produced.
05 / 07 — Evaluation
Evaluation Setup
Tested on two 2-node × 8-H200 clusters differing only in inter-node fabric.
TestbedNodes × GPUsInter-nodeNIC AWS EFA2 × 8 H200AWS EFA / SRD16 × 200 Gb/s EFA per node ConnectX-72 × 8 H200InfiniBand8 × 400 Gb/s CX7 per nodeBoth testbeds use NVLink intra-node. Benchmarked against: NCCL, Triton-distributed, Flux, Mercury, MagiAttention, Transformer-Engine, and ring-flash-attention. Larger-scale benchmarking is still in progress.
06 / 07 — Backends & RequirementsBackends & Requirements
BackendTransportWhere it runs CX7libibverbs RCConnectX-7 / InfiniBand / RoCE EFAlibibverbs + efadv (SRD)AWS p5/p5e (H200, EFA)
sm_90a), CUDA 12.9, Python with PyTorch. CX7 needs libibverbs headers. EFA needs libfabric, libibverbs, efadv under EFA_HOME=/opt/amazon/efa.
Roadmap & Key Takeaways
✅Fused GPU-driven multi-node kernels (AG+GEMM, GEMM+AR, MoE Dispatch+GEMM, Ring Attention, GEMM+RS) ✅ConnectX-7 and AWS EFA backends 🚧Full heterogeneous accelerator/NIC support with topology-aware discovery, placement, routing 🚧Inter-node megakernels: collapsing several fused steps into a single megakernel spanning a transformer layer 🚧Blackwell GPU support Fuses NVLink, inter-node RDMA, and compute into a single persistent CUDA kernel Five kernels: AllGather+GEMM, GEMM+AllReduce, MoE Dispatch+GEMM, Ring Attention, GEMM+ReduceScatter GPU-initiated RDMA vialibibverbs — no NCCL or NVSHMEM dependency
Requires Hopper GPUs (sm_90a) and ConnectX-7 or AWS EFA networking
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Key Takeaways
- mKernel fuses intra-node NVLink, inter-node RDMA, and compute into a single persistent CUDA kernel.
- Communication overhead accounts for up to 47% of execution time in MoE models per cited production data.
- Five kernels are included: AllGather+GEMM, GEMM+AllReduce, MoE Dispatch+GEMM, Ring Attention, and GEMM+ReduceScatter.
- GPU-initiated RDMA is implemented directly via
libibverbs— no NCCL or NVSHMEM dependency. - Currently requires Hopper GPUs (
sm_90a) and ConnectX-7 or AWS EFA networking; Blackwell support is on the roadmap.
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The post Meet mKernel: A Multi-GPU, Multi-Node Fused Kernel Library for GPU-Driven Communication appeared first on MarkTechPost.