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Additional mistakes are piling up for Intel as the company’s leadership attempts course correction
“The milestone is important for Intel because Aurora’s deployment has already been delayed. The supercomputer, which is expected to pass the two exaflops (peak) threshold, will not make this year’s May Top500 list of the world’s fastest supercomputers.
“We’re really focused around bringing out that whole system… stabilizing that and running … getting real workloads, not just the benchmarks running and operational. We would expect that by November for us to have a strong offering in the Top500 system,” McVeigh said.
Recently in a Dell-hosted webinar, Rick Stevens (Argonne Lab) shared that Frontier will contribute approximately 78 million quad-GPU hours per year to critical scientific workloads.
Major HPC players that include Intel, HPE, and Argonne National Labs are joining hands to develop a large-language model for scientific computing called AuroraGPT, which is built on a foundational model of 1 trillion parameters, making it significantly larger than ChatGPT, which is built on the GPT-3 foundational model.
The generative AI technology will be based on available scientific data and texts and code bases, and function like commercial large language models. It’s not clear if the technology will be multimoda, and generate, image and videos. If it is multimodal, one example could be researchers asking questions and the AI providing responses, or using the AI to generate scientific images.
The LLM will be used for the “advancing of science and utilizing Aurora for the training and the inference of that will be a critical part of how the system will be deployed,” McVeigh said.
AuroraGPT could be used for research in materials, cancer and climate science. The foundational models include Megatron and DeepSpeed transformers.
Intel also announced it is bringing out a Universal Baseboard (UBB) system with eight Ponte Vecchio Max Series GPUs (shown in the header graphic) with Supermicro and Inspur based designs initially. The servers are targeted at AI deployments, which McVeigh said favor the 8-GPU configuration. The product was launched earlier this year with expected broader availability in Q3.”
Source: https://www.hpcwire.com/2023/05/22/...akes-reveals-new-supercomputing-chip-roadmap/
“The milestone is important for Intel because Aurora’s deployment has already been delayed. The supercomputer, which is expected to pass the two exaflops (peak) threshold, will not make this year’s May Top500 list of the world’s fastest supercomputers.
“We’re really focused around bringing out that whole system… stabilizing that and running … getting real workloads, not just the benchmarks running and operational. We would expect that by November for us to have a strong offering in the Top500 system,” McVeigh said.
Recently in a Dell-hosted webinar, Rick Stevens (Argonne Lab) shared that Frontier will contribute approximately 78 million quad-GPU hours per year to critical scientific workloads.
Major HPC players that include Intel, HPE, and Argonne National Labs are joining hands to develop a large-language model for scientific computing called AuroraGPT, which is built on a foundational model of 1 trillion parameters, making it significantly larger than ChatGPT, which is built on the GPT-3 foundational model.
The generative AI technology will be based on available scientific data and texts and code bases, and function like commercial large language models. It’s not clear if the technology will be multimoda, and generate, image and videos. If it is multimodal, one example could be researchers asking questions and the AI providing responses, or using the AI to generate scientific images.
The LLM will be used for the “advancing of science and utilizing Aurora for the training and the inference of that will be a critical part of how the system will be deployed,” McVeigh said.
AuroraGPT could be used for research in materials, cancer and climate science. The foundational models include Megatron and DeepSpeed transformers.
Intel also announced it is bringing out a Universal Baseboard (UBB) system with eight Ponte Vecchio Max Series GPUs (shown in the header graphic) with Supermicro and Inspur based designs initially. The servers are targeted at AI deployments, which McVeigh said favor the 8-GPU configuration. The product was launched earlier this year with expected broader availability in Q3.”
Source: https://www.hpcwire.com/2023/05/22/...akes-reveals-new-supercomputing-chip-roadmap/