Deep learning dominates ISC


At ISC High Performance 2017, deep learning was driving computing innovation as the HPC industry set its sights on AI hardware and applications, writes Robert Roe


T


his year the conference dedicated an entire day to deep learning to discuss the recent advances in artificial intelligence based on deep learning


technology. Elsewhere during the June event, the showfloor of the exhibition hosted many new products dedicated to optimising HPC hardware for use in deep learning and AI workloads. Cray announced the Cray Urika-XC


analytics soſtware suite which aims to deliver analytics tools – specifically targeting analytics and deep learning – to the firm’s line of Cray XC supercomputers. Nvidia launched its PCIe-based Volta V100 GPU. However, the company also demonstrated the use of its GPU technology, in combination with deep learning, as part of the human brain project. HPE launched new server solutions aimed


specifically at HPC and AI workloads, while Mellanox highlighted its work to fine- tune technology for AI and deep learning applications. Mellanox announced that deep


14 SCIENTIFIC COMPUTING WORLD


learning frameworks such as TensorFlow, Caffe2, Microsoſt Cognitive Toolkit, and Baidu PaddlePaddle can now leverage the company’s smart offloading capabilities.


Shifting paradigms Te Cray Urika-XC solution is a set of applications and tools optimised to run seamlessly on the Cray XC supercomputing platform. In basic terms, the company is taking the toolset it has developed through the Urika GX platform, optimising it for deep learning and then applying the soſtware and toolsets to its XC series of supercomputers. Te soſtware package is comprised of the


Cray Graph Engine, the Apache Spark analytics environment, the BigDL distributed deep learning framework for Spark, the distributed Dask parallel computing libraries for analytics, and widely-used languages for analytics including Python, Scala, Java, and R. Te Cray Urika-XC analytics soſtware


suite highlights the convergence of traditional HPC and data-intensive computing – such


as deep learning – as core workloads for supercomputing systems in the coming years. As the data volumes in HPC grow, the


industry is responding by moving away from the previous flops-centric model to a more data-centric model. Tis requires not only innovation in parallel processing, network, and storage performance but also the soſtware and tools used to process the vast quantities of data needed to train deep-learning networks. While deep learning is not the only trigger


for this new model, it exemplifies the changing paradigm of architectural design in HPC. One example of this is the Swiss National


Supercomputing Centre (CSCS) in Lugano, Switzerland which currently uses the Cray Urika-XC solution on the ‘Piz Daint,’ which, aſter its recent upgrade, is now one of the fastest supercomputers in the world. ‘CSCS has been responding to the increased


needs for data analytics tools and services,’ said Professor Tomas Schulthess, director of the Swiss National Supercomputing Centre (CSCS). ‘We were very fortunate to participate with


our Cray supercomputer Piz Daint in the early evaluation phase of the Cray Urika-XC environment. Initial performance results and scaling experiments using a subset of applications including Apache Spark and Python have been very promising. We look forward to exploring future extensions of the Cray Urika-XC analytics soſtware suite.’ Also during the week at ISC, Nvidia


announced the PCI Express version of their latest Tesla GPU accelerator, the Volta-based V100. Te SXM2 form factor card was first announced earlier this year at the company’s GPU technology conference (GTC), but users can now use the more traditional PCIE slot to connect the Volta-based GPU.


@scwmagazine l www.scientific-computing.com


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