Enterprise AI Analysis
HPC-R1: Characterizing R1-like Large Reasoning Models on HPC
This paper details HPC-R1, a comprehensive characterization of Large Reasoning Model (LRM) training on the NERSC Perlmutter supercomputer. It highlights significant challenges in reproducibility, efficiency, and system-level optimization for complex LRM training workflows. The study identifies key system inefficiencies and scaling behaviors across SFT, GRPO-based RL, autoregressive generation, and distillation stages, offering 19 key observations and recommendations for future HPC-AI system design. The findings emphasize the need for optimized communication, improved GPU utilization, sufficient CPU memory, and adaptive software stacks to better support LRM training workloads.
Key Executive Impact
Our analysis reveals quantifiable advantages for enterprise AI adoption:
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Communication Speedup with OFI Plugin
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Key Observations Across LRM Stages
0B
Maximum LRM Model Parameters Analyzed
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
HPC Systems
AI Workloads
Distributed Training
Insight: GPU Underutilization During Generation
0% GPU utilization during LRM generation averages only 44% due to communication overhead and memory-bound decode phases, indicating significant underutilization of expensive hardware.Resource Requirements Across Training Stages
| Stage | Minimum Required GPUs | Key Challenges |
|---|---|---|
| SFT | 4 (70B model) |
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| RL-GRPO | 64 (70B model) |
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| Generation | 8 (70B model) |
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| Distillation (Large Model) | 32 (671B R1) |
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| Distillation (Small Model) | 1 (1B/3B Llama) |
|
Harnessing Slingshot Interconnect for LRM Training
Problem: Traditional interconnects often bottleneck large-scale distributed AI training due to high communication latency and low bandwidth, especially for collective operations.
Solution: Perlmutter's Slingshot interconnect with the OFI plugin drastically reduces communication overhead during SFT training, improving throughput by up to 7.16x compared to standard TCP/IP. This significantly speeds up communication-intensive phases.
Impact: The enhanced interconnect performance directly contributes to more efficient scaling of LRM training, reducing overall iteration time and making large models more feasible on HPC systems. However, configuring and optimizing it correctly remains a challenge.
Keywords: Slingshot, OFI Plugin, Communication Efficiency, SFT Training, HPC Interconnects
Enterprise Process Flow
Cold Start Mitigation (SFT)
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Enhancing Reasoning (GRPO-RL)
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SFT with Synthesized Data
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Generalization & Improvement (GRPO-RL)
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Distillation Generation
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Distillation SFT
Insight: Ideal SFT Weak Scaling
0 TFLOPS/GPU SFT exhibits ideal weak scaling, maintaining nearly constant compute efficiency (9.80 TFLOPS/GPU) across various GPU scales, demonstrating effective aggregated throughput with increasing data parallelism.Insight: RL Communication Bottleneck
0% AllReduce operations dominate communication time at larger DP scales in RL, consuming over 65% of each iteration, indicating a communication bottleneck for large-scale RL training.Advanced ROI Calculator
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