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#Hpc ai benchmark code

Developers only need to use the latest framework releases of TensorFlow and PyTorch to unleash this performance. The 4th Gen Intel Xeon Scalable processors with Intel AMX deliver this performance out of the box across multiple industry standard frameworks and integrated with end-to-end data science tools and a broad ecosystem of smart solutions from partners. Intel Xeon Scalable Processor was the only CPU submitted for MLPerf v2.1, once again demonstrating it is the best server CPU for AI training, which enables customers to use their shared infrastructure to train anywhere, anytime.

It is purpose-designed to deliver the best DL performance and TCO for these dedicated use cases.Ībout the Results for Xeon: Intel submitted MLPerf Training v2.1 results on the 4th Gen Intel Xeon Scalable processor product line across a range of workloads. In cases where the server or a cluster of servers are predominantly used for DL training and inference compute, the Habana Gaudi2 accelerator is the optimal accelerator. This dedicated AI engine is optimized to deliver up to 6x higher gen-to-gen DL training model performance using industry standard frameworks.

AMX is a dedicated matrix multiplication engine built into every core of 4th Gen Intel Xeon Scalable processors. The 4th Generation Intel Xeon Scalable processor with Intel® Advanced Matrix Extensions (AMX), a new built-in AI accelerator, allows customers to extend the general-purpose Xeon server platform to cover even more DL use cases, including DL training and fine tuning. It is in these use cases that Xeon Scalable delivers the best total cost of ownership (TCO) and year-round utilization.

S.Kudo, K.Nitadori, T.Ina, and T.Imamura: "Implementation and Numerical techniques for One Eflop/s HPL-AI benchmark on Fugaku", ScalA20 conj SC20, 2020.Why It Matters: In many data center use cases, deep learning (DL) is part of a complex pipeline of machine learning (ML) and data analytics running on Xeon-based servers that are also used to run other applications and are adaptable to workload demands changing over time.

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#Hpc ai benchmark code

We have replaced a copyright-sensitive source code with generic or emulated source code.

A distributed-memory implementation of HPL-AI benchmark for Fugaku and others.

S.Kudo, K.Nitadori, T.Ina, and T.Imamura: "Implementation and Numerical techniques for One Eflop/s HPL-AI benchmark on Fugaku", ScalA20 conj SC20, 2020. Our reference code will be released as Open Source Software, which does not include a copyright-concerned source code, while that is not our champion code. As far as the official page of the HPL-AI benchmark, a number of HPC centers have benchmarked HPL-AI, starting with Oak Ridge National Laboratory’s Summit machine in 2019, and the latest list includes that our benchmark on Fugaku, which achieved two exaFLOP/s in mixed-precision performance (see also our early report in ). We have developed a highly tuned HPL-AI benchmark code on Fugaku, on which Fujitsu A64FX and Tofu-D technologies are harnessed to yield excellent performance.

HPL-AI stands for "the High Performance LINPACK for Accelerator Introspection", which allows mixed-precision arithmetic to solve a linear equation system and seeks to highlight the convergence of HPC and artificial intelligence (AI) workloads.