Technical · November 2025 · 15 min read

Advanced Techniques for GPU-Heavy Science

Exascale labs now run traditional MPI codes, diffusion models, RL, graph analytics, and visualization stacks—all fighting for GPU time. This guide covers the advanced tactics that keep accelerators busy while respecting power, security, and reproducibility constraints.

Accelerator landscape snapshot

• NVIDIA GH200 NVL72: 72 Grace Hopper Superchips per rack, 1.44 TB unified memory, and 900 GB/s NVLink C2C.
• AMD MI300A/X: APU architecture with 128 GB HBM3 per package and Infinity Fabric bandwidth >5 TB/s.
• Blackwell (preview): Early SC24 field data shows 20+ PFLOPS BF16 per GPU plus second-gen Transformer Engine.

Multi-node GPU strategies

Topology-aware placement

Query NCCL/ROCm topology graphs before launch. Reject placements crossing slow links when NVSwitch islands exist. Use TopologyPlugin=hwloc and SelectTypeParameters=CR_CORE_Memory.

Heterogeneous jobs

Slurm --het-group lets you mix CPU preprocess stages with GPU-heavy steps to avoid extra queue time.

Network tuning

Enable SHARPv3 or UCC offload on InfiniBand NDR/Quantum-2 fabrics and keep Slingshot congestion control tuned.

Mixed precision + AI convergence

• BF16/FP8 Transformer Engine: Use NVIDIA loss-scaling auto-tuners to stay stable.
• FP32 accumulation + FP16 compute: Validate with Nsight Compute or rocprof traces.
• AI surrogate + simulation: Deploy Triton or ONNX Runtime within Balsam/Cromwell workflows.

Zero-trust data flows

1. Sign Apptainer/OCI images with Cosign; store definition files in git.
2. Carry dataset labels (exportable, ITAR, clinical) into Slurm job_container policies.
3. Require approval workflows before copying sensitive checkpoints off-cluster.

Profiling & observability

• Nsight Systems 2024.4 / Nsight Compute 2024.3 for warp tracing on Hopper + GH200.
• ROC Profiler 6.1 + Omnitrace for MI300 metrics (HBM utilization, fabric occupancy).
• dcgm-exporter or rocm-smi metrics piped into Prometheus with Slurm job labels.

Automation patterns

• GPU memory carving-as-code: Manage MIG/MPS layouts via git-rendered config maps consumed by gres.conf.
• Power-aware scheduling: Feed energy budgets (IPMI, NVIDIA NVDM, AMD SMI) into Slurm PowerSave or Open XDMoD so you can promise <8 GB peak memory per build and <8 kW per rack.
• Hybrid workflows: Orchestrate multi-step pipelines with Flux/Balsam and capture each step in the knowledge base.

Implementation checklist

• Maintain a topology inventory (GPU SKUs, NVSwitch fabrics, firmware levels).
• Keep signed container registries for CUDA 12.5, ROCm 6.x, oneAPI 2025.1.
• Enable Slurm SelectType=select/cons_tres, AccountingStorageEnforce=associations, job containers, and burst buffers.
• Export GPU + CPU metrics every 15 seconds; ship logs to Loki/OpenSearch with job annotations.

References

• MLPerf Training/Inference 4Q24.
• SC24 accelerator BoFs (NVL72, MI300A tuning, Triton-in-HPC workflows).
• NVIDIA Hopper & Blackwell architecture whitepapers.
• AMD Instinct MI300 deep dives (Hot Chips 2024).