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Unlocking LLM training efficiency with Trillium — a performance analysis
- Google's sixth-generation Tensor Processing Unit (TPU), Trillium, can deliver up to 1.8x better performance-per-dollar compared to prior-generation Cloud TPU v5p.
- MLPerf 4.1 training benchmarks showed that the Trillium delivers a 99% scaling efficiency (throughput).
- The metrics used for hardware accelerator comparison include peak throughput, effective throughput, throughput scaling efficiency, utilization performance, scaling efficiency and convergence scaling efficiency.
- Convergence scaling efficiency, which focuses on the fundamental goal of training, is measured by the ratio of the speedup in convergence time to the increase in cluster size.
- The convergence scaling efficiency of Trillium is close to that of Cloud TPU v5p which is commendable, but Trillium delivers the convergence at a lower cost.
- Trillium achieves 99% scaling efficiency even when operating across data-center networks using Cloud TPU multislice technology, outperforming the 94% scaling efficiency of Cloud TPU v5p cluster within a single ICI domain.
- Trillium provides better performance per dollar, improvement in convergence scaling efficiency and scaling properties, which make it the most cost-efficient TPU training system to date.
- Applying multiple dimensions of performance and efficiency, like effective model FLOPS utilization (EMFU), memory bandwidth utilization (MBU), regions using ICI domains and scaling characteristics, for ML-accelerator evaluation is suggested to make data-driven decisions based on workload requirements.
- Trillium has been launched to address the demands of next-generation models by providing performance at scale, from the chip to the system to Google data center deployments.
- Throughput scaling efficiency and metrics like EMFU and MBU provide more meaningful insights into an accelerator's abilities beyond simple metrics like peak performance.
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