Immediately, we’re exploring how Ethernet stacks up in opposition to InfiniBand in AI/ML environments, specializing in how Cisco Silicon One™ manages community congestion and enhances efficiency for AI/ML workloads. This publish emphasizes the significance of benchmarking and KPI metrics in evaluating community options, showcasing the Cisco Zeus Cluster geared up with 128 NVIDIA® H100 GPUs and cutting-edge congestion administration applied sciences like dynamic load balancing and packet spray.
Networking requirements to satisfy the wants of AI/ML workloads
AI/ML coaching workloads generate repetitive micro-congestion, stressing community buffers considerably. The east-to-west GPU-to-GPU site visitors throughout mannequin coaching calls for a low-latency, lossless community cloth. InfiniBand has been a dominant expertise within the high-performance computing (HPC) surroundings and currently within the AI/ML surroundings.
Ethernet is a mature various, with superior options that may tackle the rigorous calls for of the AI/ML coaching workloads and Cisco Silicon One can successfully execute load balancing and handle congestion. We got down to benchmark and examine Cisco Silicon One versus NVIDIA Spectrum-X™ and InfiniBand.
Analysis of community cloth options for AI/ML
Community site visitors patterns differ primarily based on mannequin dimension, structure, and parallelization strategies utilized in accelerated coaching. To guage AI/ML community cloth options, we recognized related benchmarks and key efficiency indicator (KPI) metrics for each AI/ML workload and infrastructure groups, as a result of they view efficiency by means of completely different lenses.
We established complete exams to measure efficiency and generate metrics particular to AI/ML workload and infrastructure groups. For these exams, we used the Zeus Cluster, that includes devoted backend and storage with a normal 3-stage leaf-spine Clos cloth community, constructed with Cisco Silicon One–primarily based platforms and 128 NVIDIA H100 GPUs. (See Determine 1.)
We developed benchmarking suites utilizing open-source and industry-standard instruments contributed by NVIDIA and others. Our benchmarking suites included the next (see additionally Desk 1):
- Distant Direct Reminiscence Entry (RDMA) benchmarks—constructed utilizing IBPerf utilities—to judge community efficiency throughout congestion created by incast
- NVIDIA Collective Communication Library (NCCL) benchmarks, which consider software throughput throughout coaching and inference communication section amongst GPUs
- MLCommons MLPerf set of benchmarks, which evaluates probably the most understood metrics, job completion time (JCT) and tokens per second by the workload groups
Legend:
JCT = Job Completion Time
Bus BW = Bus bandwidth
ECN/PFC = Express Congestion Notification and Precedence Move Management
NCCL benchmarking in opposition to congestion avoidance options
Congestion builds up throughout the again propagation stage of the coaching course of, the place a gradient sync is required amongst all of the GPUs taking part in coaching. Because the mannequin dimension will increase, so does the gradient dimension and the variety of GPUs. This creates large micro-congestion within the community cloth. Determine 2 exhibits outcomes of the JCT and site visitors distribution benchmarking. Notice how Cisco Silicon One helps a set of superior options for congestion avoidance, reminiscent of dynamic load balancing (DLB) and packet spray strategies, and Information Middle Quantized Congestion Notification (DCQCN) for congestion administration.
Determine 2 illustrates how the NCCL benchmarks stack up in opposition to completely different congestion avoidance options. We examined the most typical collectives with a number of completely different message sizes to focus on these metrics. The outcomes present that JCT improves with DLB and packet spray for All-to-All, which causes probably the most congestion because of the nature of communication. Though JCT is probably the most understood metric from an software’s perspective, JCT doesn’t present how successfully the community is utilized—one thing the infrastructure crew must know. This data might assist them to:
- Enhance the community utilization to get higher JCT
- Know what number of workloads can share the community cloth with out adversely impacting JCT
- Plan for capability as use circumstances enhance
To gauge community cloth utilization, we calculated Jain’s Equity Index, the place LinkTxᵢ is the quantity of transmitted site visitors on cloth hyperlink:
The index worth ranges from 0.0 to 1.0, with greater values being higher. A price of 1.0 represents the proper distribution. The Site visitors Distribution on Cloth Hyperlinks chart in Determine 2 exhibits how DLB and packet spray algorithms create a near-perfect Jain’s Equity Index, so site visitors distribution throughout the community cloth is sort of good. ECMP makes use of static hashing, and relying on move entropy, it will possibly result in site visitors polarization, inflicting micro-congestion and negatively affecting JCT.
Silicon One versus NVIDIA Spectrum-X and InfiniBand
The NCCL Benchmark – Aggressive Evaluation (Determine 3) exhibits how Cisco Silicon One performs in opposition to NVIDIA Spectrum-X and InfiniBand applied sciences. The information for NVIDIA was taken from the SemiAnalysis publication. Notice that Cisco doesn’t understand how these exams have been carried out, however we do know that the cluster dimension and GPU to community cloth connectivity is just like the Cisco Zeus Cluster.
Bus Bandwidth (Bus BW) benchmarks the efficiency of collective communication by measuring the velocity of operations involving a number of GPUs. Every collective has a particular mathematical equation reported throughout benchmarking. Determine 3 exhibits that Cisco Silicon One – All Cut back performs comparably to NVIDIA Spectrum-X and InfiniBand throughout varied message sizes.
Community cloth efficiency evaluation
The IBPerf Benchmark compares RDMA efficiency in opposition to ECMP, DLB, and packet spray, that are essential for assessing community cloth efficiency. Incast situations, the place a number of GPUs ship information to at least one GPU, usually trigger congestion. We simulated these circumstances utilizing IBPerf instruments.
Determine 4 exhibits how Aggregated Session Throughput and JCT reply to completely different congestion avoidance algorithms: ECMP, DLB, and packet spray. DLB and packet spray attain Hyperlink Bandwidth, enhancing JCT. It additionally illustrates how DCQCN handles micro-congestions, with PFC and ECN ratios enhancing with DLB and considerably dropping with packet spray. Though JCT improves barely from DLB to packet spray, the ECN ratio drops dramatically resulting from packet spray’s very best site visitors distribution.
Coaching and inference benchmark
The MLPerf Benchmark – Coaching and Inference, printed by the MLCommons group, goals to allow truthful comparability of AI/ML techniques and options.
We centered on AI/ML information middle options by executing coaching and inference benchmarks. To realize optimum outcomes, we extensively tuned throughout compute, storage, and networking elements utilizing congestion administration options of Cisco Silicon One. Determine 5 exhibits comparable efficiency throughout varied platform distributors. Cisco Silicon One with Ethernet performs like different vendor options for Ethernet.
Conclusion
Our deep dive into Ethernet and InfiniBand inside AI/ML environments highlights the exceptional prowess of Cisco Silicon One in tackling congestion and boosting efficiency. These modern developments showcase the unwavering dedication of Cisco to supply strong, high-performance networking options that meet the rigorous calls for of immediately’s AI/ML functions.
Many because of Vijay Tapaskar, Will Eatherton, and Kevin Wollenweber for his or her assist on this benchmarking course of.
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