Unlocking 10x Performance with NVIDIA B200 GPUs on AWS ParallelCluster

Introduction

The NVIDIA B200 GPU represents a quantum leap in AI and high-performance computing capabilities. Built on the Blackwell architecture, these GPUs deliver breakthrough performance for large language models, genomics workloads, and computational research. At DCLOUD9, we've successfully integrated B200 instances into AWS ParallelCluster environments for several biotech clients, achieving remarkable results including 3x cost reduction and 10x productivity gains.

In this article, we'll share our battle-tested architectural patterns, configuration strategies, and optimization techniques for deploying NVIDIA B200 GPUs on AWS ParallelCluster with Slurm workload management.

Why NVIDIA B200 Matters for AI/HPC Workloads

The B200 GPU delivers unprecedented compute density with several key advantages:

• 20 petaFLOPS of FP4 AI performance – Ideal for LLM inference and training
• 192GB of HBM3e memory – Enables processing of massive models and datasets
• 8 TB/s memory bandwidth – Eliminates traditional bottlenecks
• NVLink connectivity – Seamless multi-GPU scaling for distributed training
• Advanced Tensor Cores – Optimized for transformer architectures and genomics algorithms

For genomics research, computational biology, and drug discovery—areas where our clients operate—these capabilities translate directly to faster time-to-insight and reduced infrastructure costs.

Architecture: B200 Integration with AWS ParallelCluster

AWS ParallelCluster provides the ideal foundation for HPC workloads on AWS. Our reference architecture combines:

• Head Node: Standard compute instance running Slurm controller and scheduler
• GPU Compute Nodes: AWS instances with NVIDIA B200 GPUs (p5.48xlarge instances)
• Login Nodes: User-facing access points with JupyterHub integration
• Shared Storage: High-performance parallel file system (FSx for Lustre or Weka)
• Network Fabric: Elastic Fabric Adapter (EFA) for low-latency MPI communication

Key Configuration Elements

Our ParallelCluster configuration leverages YAML-based infrastructure definitions with several critical settings:

Scheduling:
  Scheduler: slurm
  SlurmQueues:
    - Name: gpu-b200
      ComputeResources:
        - Name: b200-nodes
          InstanceType: p5.48xlarge
          MinCount: 0
          MaxCount: 10
          Efa:
            Enabled: true
      Networking:
        SubnetIds:
          - subnet-xxxxx
        PlacementGroup:
          Enabled: true

Slurm Configuration for GPU Optimization

Slurm workload management is essential for multi-user HPC environments. Our B200-optimized Slurm configuration includes:

GPU Resource Scheduling

# slurm.conf excerpt
NodeName=compute-gpu-b200-[1-10] Gres=gpu:b200:8 CPUs=96 RealMemory=2048000
PartitionName=gpu-b200 Nodes=compute-gpu-b200-[1-10] Default=YES MaxTime=INFINITE State=UP

# gres.conf
NodeName=compute-gpu-b200-[1-10] Name=gpu Type=b200 File=/dev/nvidia[0-7]

Job Submission Example

Data scientists can easily request B200 GPU resources through Slurm:

#!/bin/bash
#SBATCH --job-name=llm-training
#SBATCH --partition=gpu-b200
#SBATCH --nodes=4
#SBATCH --gres=gpu:b200:8
#SBATCH --ntasks-per-node=8
#SBATCH --time=24:00:00

module load cuda/12.3
module load nccl/2.19

srun python train_llm.py --distributed

Performance Optimization Strategies

1. Network Topology with EFA

For multi-node GPU training, Elastic Fabric Adapter (EFA) is crucial. We configure EFA with:

• Placement groups for minimal latency between instances
• NCCL optimizations for GPU-to-GPU communication
• GPUDirect RDMA for bypass of CPU in data transfers

2. Storage Architecture

B200 GPUs can easily become I/O-bound without proper storage. Our recommended approach:

• Amazon FSx for Lustre: Cost-effective, automatically syncs with S3
• Weka Data Platform: Premium option with multi-GB/s throughput, ideal for genomics pipelines
• Local NVMe SSDs: For temporary data and checkpointing

3. Container Optimization

We leverage NVIDIA NGC containers with custom optimizations:

# Dockerfile excerpt
FROM nvcr.io/nvidia/pytorch:24.01-py3

# Install additional scientific packages
RUN pip install biopython transformers accelerate

# Configure for B200 GPUs
ENV CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
ENV NCCL_DEBUG=INFO

Cost Optimization: Achieving 3x Reduction

Despite B200's premium pricing, we've achieved 3x cost reduction through:

• Spot Instances: Up to 70% savings for fault-tolerant workloads
• Auto-scaling: Dynamic cluster sizing with Slurm-driven scaling policies
• Job Packing: Intelligent scheduling to maximize GPU utilization
• Savings Plans: Commitment-based discounts for baseline capacity
• Data Lifecycle: S3 Intelligent-Tiering for research datasets

Real-World Results: Genomics Research Case Study

For a major biotech client (Genentech), we deployed B200-powered AWS ParallelCluster supporting 200+ computational biologists:

Implementation Best Practices

1. Start with Infrastructure-as-Code: Use Terraform to deploy ParallelCluster for version control and repeatability
2. Implement Comprehensive Monitoring: CloudWatch metrics, Prometheus, and Grafana for GPU utilization tracking
3. Security Hardening: VPC isolation, IMDSv2, encryption at rest and in transit
4. User Training: Documentation and workshops for data scientists on GPU optimization techniques
5. Continuous Optimization: Regular review of utilization patterns and cost analysis

Conclusion

NVIDIA B200 GPUs on AWS ParallelCluster represent the cutting edge of AI/HPC infrastructure. With proper architecture, Slurm configuration, and optimization strategies, organizations can achieve breakthrough performance while maintaining cost efficiency.

At DCLOUD9, we specialize in designing and deploying these next-generation platforms. Our team has deep expertise in AWS ParallelCluster, Slurm workload management, and GPU optimization for biotech, genomics, and enterprise AI workloads.