“Intel today announced that its second-generation Habana® Gaudi®2 deep learning processors have outperformed Nvidia’s A100 submission for AI time-to-train on the MLPerf industry benchmark. The results highlight leading training times on vision (ResNet-50) and language (BERT) models with the Gaudi2 processor, which was unveiled in May at the Intel Vision event.
Why It Matters: The Gaudi platform from Habana Labs, Intel’s data center team focused on deep learning processor technologies, enables data scientists and machine learning engineers to accelerate training and build new or migrate existing models with just a few lines of code to enjoy greater productivity, as well as lower operational costs.
What It Shows: Gaudi2 delivers dramatic advancements in time-to-train (TTT) over first-generation Gaudi and enabled Habana’s May 2022 MLPerf submission to outperform Nvidia’s A100-80G for eight accelerators on vision and language models. For ResNet-50, Gaudi2 delivers a 36% reduction in time-to-train as compared to Nvidia’s TTT for A100-80GB and a 45% reduction compared to an A100-40GB 8-accelerator server submission by Dell for both ResNet-50 and BERT.
Compared to first-generation Gaudi, Gaudi2 achieves a 3x speed-up in training throughput for ResNet-50 and 4.7x for BERT. These advances can be attributed to the transition to 7-nanometer process from 16 nm, tripling the number of Tensor Processor Cores, increasing the GEMM engine compute capacity, tripling the in-package high bandwidth memory capacity, increasing bandwidth and doubling the SRAM size. For vision models, Gaudi2 has a new feature in the form of an integrated media engine, which operates independently and can handle the entire pre-processing pipe for compressed imaging, including data augmentation required for AI training.
About out-of-the-box customer performance: The performance of both generations of Gaudi processors is achieved without special software manipulations that differ from the out-of-the-box commercial software stack available to Habana customers.
Comparing out-of-the-box performance attained with commercially available software, the following measurements were produced by Habana on a common 8-GPU server versus the HLS-Gaudi2 reference server. Training throughput was derived with TensorFlow dockers from NGC and from Habana public repositories, employing best parameters for performance as recommended by the vendors (mixed precision used in both). The training time throughput is a key factor affecting the resulting training time convergence:
In addition to Gaudi2 achievements noted in MLPerf, the first-generation Gaudi delivered strong performance and impressive near-linear scale on ResNet for 128-accelerator and 256-accelerator Gaudi submissions that support high-efficiency system scaling for customers.
“Gaudi2 delivers clear leadership training performance as proven by our latest MLPerf results,” said Eitan Medina, chief operating officer at Habana Labs. “And we continue to innovate on our deep-learning training architecture and software to deliver the most cost-competitive AI training solutions.””