High performance cluster computing architectures systems pdf

The purpose of this paper was to configure a cluster computing system to improve performance over that of a single computer, while typically being much more cost-effective than single computers of … Expand. On a course on computer cluster configuration and administration. Parallel Distributed Comput. View 1 excerpt, cites background. This project aims to construct a non-dedicated, high performance PC cluster using an advanced network processor for solving CPUand memory-intensive applications.

Early cluster projects either use … Expand. An exploration of heterogeneous systems. View 1 excerpt, cites methods.

Low-power cluster using OMAP View 2 excerpts, cites background and methods. A framework for scientific computing with GPUs. Distributed training software stacks such as TensorFlow depend on distributed training software stacks, e.

In addition to these considerations, the AI model architecture, dataset, and training optimizer prevent a seamless use of distributed training. Training time to solution will often scale linearly with small batch size. Figures 2 and 4 show good generalization at 64 GPUs, which amounts to a global batch size of samples. However, it is known that as data sets and number of features grow, naively scaling number of GPUs, and subse- quently batch size, will often take more epochs to achieve an acceptable validation error.

The state-of-the art in AI training at scale was reported in [50]. Therein, ResNet was trained using a batch size of 64k samples, run across Tesla P40s. While achieving this level of scaling required a lot of experimental work, this benchmark, and others [51], indicate that scaling AI models to larger data and feature sets is indeed possible. J Big Data Page 7 of 12 pipeline. A mixture of fast human model development cycle mixed with automated hyper-parameter tuning is a candidate solution to tackle this problem.

In Fig. The scal- ing efficiency, i. Furthermore, Fig. In other words, we can generalize the methods deployed and tested on NSF- funded cyberinfrastructure to HPC platforms that have different scale, hardware and software.

Open challenges A number of challenges remain towards an optimal exploitation of AI and extreme scale computing. For instance, it is recognized that some experimental datasets are not in a suitable format to fully exploit data-driven discovery.

Another challenge concerns the design of AI models whose architecture and optimization schemes incorporate domain knowledge, enabling AI models to converge faster while also enabling intuitive, serendipitous discovery that may not be encapsu- lated by approximate descriptions of complex phenomena [37, 53].

It is also essential to develop a rigorous approach to maximize the use of HPC platforms for distributed training. This requires a systematic approach to select an optimal set of hyperparam- eters that enables faster convergence, and creative methods to use less training data to achieve state-of-the-art performance. NSF has also funded several institutes to advance Fig. J Big Data Page 8 of 12 the state-of-the-art in AI, seeking new modes of data-driven discovery in science and engineering.

These investments aim to sustain, broaden and accelerate recent break- throughs in science, technology and industry driven by AI applications [54]. As these projects evolve and mature, it will be essential to facilitate cross-pollination of expertise, avoiding duplication and empowering new AI practitioners to access AI scientific soft- ware that is open source, interpretable, reproducible and trustworthy. Cloud computing and HPC Cloud computing and containerization became popular for developing customer facing web apps.

It allowed a DevOps team—i. Depending on the business cycle, companies could dynamically scale their infrastructure with virtually no overhead of purchasing hardware, and then relinquish it when it was no longer needed. HPC would do well to adopt a DevOps cycle like the ones seen in startup culture. However HPC has some unique challenges that make this difficult. Cloud computing delivers a unit of compute and storage in tandem as a single instance and isolates distinct resources.

A developer using cloud resources treats a compute instance as only the host for their code and must explicitly choose how to move large volumes of data on and off.

This is usually done by allocating a specialized cloud instance of a data store, e. Improved cloud solutions provide Kubernetes and other cluster manager recipes to allocate a skeleton of these resources, but it is still up to the developers to choose exactly how data are moved between the resources and to code the specific functions of their app. That is, many users with different projects see the same file system and compute resource. Each developer must wait their turn to see their code run.

In cloud computing, a resource belongs and is billed to the developer on demand. When the resource is released, all of its state-full properties get reset. To have high bandwidth and low latency between cloud compute instances, one pays a premium. In the case of distributed training, one needs to ascertain whether the cloud or HPC platforms provide an adequate solution. On-demand, high throughput or cloudbursting of single-node applications are ideally suited for the cloud.

For instance, in the case of genetic data analysis, the KnowEng platform [28] is implemented as a web application where the compute cluster is managed by Kubernetes, and provides an example of a workflow that can be expanded to include methods for intuitively managing library com- patibility and cloud bursting.

This cloud-based solution includes: 1 the ability to access disparate data; 2 set parameters for complex AI experiments effortlessly; 3 deploy computation in a cloud environment; 4 engage with sophisticated visualization tools to evaluate data and study results; and 5 save results and access parameter settings of prior runs.

However, large distributed training workloads that run for many hours or days will continue to excel on a high-end HPC environment. This is far higher than the amortized cost of the HAL cluster and its support. NCSA is spearheading its application to support industry partners from the agriculture, health- care, energy, and financial, sectors to stay competitive on the global market by analyzing bigger and more complex data to uncover hidden patterns, reveal market and cash flow trends, and identify customer preferences [56].

The confluence of modeling, simulation and AI is another area of growing interest among manufacturing and life science part- ners, promising to significantly accelerate many extremely difficult and computationally expensive methods and workflows in model-based design and analysis [37, 57, 58].

Academic innovation in AI pursues ideas that are exciting and productive, though they may not have immediate, tangible benefits. While academic scholarship is curiosity driven research, innovative AI applications in industry have as a goal to address com- putational grand challenges at an accelerated pace, and to apply at scale new solutions to profit from them.

In brief, while academia and industry pursue distinct goals, it is essential that both spheres of activity maintain a close-knit collaboration [59].

This is a critical endeavor because breakthroughs in industry and technology over the last dec- ade were enabled by basic AI applications. As industrial applications reach new frontiers and computational grand challenges arise, it will be essential to continue leveraging AI innovation, and explore ways to translate it into tangible solutions that may be deployed at scale to produce societal and business benefits.

In summary, the training of future AI practitioners demands an interdisciplinary approach that includes a clear vision of industry needs. This approach will ensure that academic AI innovation is readily incor- porated and applied, creating a sustainable paradigm that opens up diverse lines of fund- ing for AI researchers.

Conclusion The convergence of AI and HPC provides the means to address big data challenges in science, engineering and industry, and enables the creation of disruptive approaches for data-driven discovery and innovation. As AI and HPC continue to transform an ever increasing number of disciplines at an accelerated pace, we can only image what the future holds once AI is powered with a rigorous mathematical framework.

In that scenario, it will be possible to optimally use oversubscribed HPC platforms, and create intuitive AI solutions that will lead to trans- formational scientific discoveries, and disruptive solutions in industry and technology Finally, to contribute to the use of realistic datasets to benchmark HPC platforms, we release two neural network models, along with datasets, that we used to produce Figs.

J Big Data Page 10 of 12 powerful HPC platforms for AI research, it is urgent that we provide guidelines to maxi- mize the use of these resources, and continue training new talent that will catalyze the adoption and best AI practices.

This approach was critical in the past to enable the adop- tion of HPC by industry, and will play a more significant role in the future given the eagerness with which industry is adopting AI solutions.

Acknowledgements We thank Nicholas A. All authors contributed to developing the ideas, and writing and reviewing this manuscript. All authors read and approved the final manuscript. Availability of data and materials The neural network models and data used to study characterize black hole mergers, and to classify galaxy images, are are readily available at the Deep Learning Hub DLHub [30, 31] hosted by Argonne National Laboratory ANL [60, 61].

Ethics approval and consent to participate Not applicable Consent for publication The authors approve the publication of this manuscript Competing interests The authors declare that they have no competing interests. Received: 24 June Accepted: 27 September References 1. Big data and extreme-scale computing: Pathways to convergence- toward a shaping strategy for a future software and data ecosystem for scientific inquiry.

National Academies of Sciences, Engineering, and Medicine. Deep Learning. J Big Data Page 11 of 12 4. ImageNet large scale visual recognition challenge.

Int J Comput Vision. Gradient-based learning applied to document recognition. Conclusion The convergence of AI and HPC provides the means to address big data challenges in science, engineering and industry, and enables the creation of disruptive approaches for data-driven discovery and innovation. As AI and HPC continue to transform an ever increasing number of disciplines at an accelerated pace, we can only image what the future holds once AI is powered with a rigorous mathematical framework.

In that scenario, it will be possible to optimally use oversubscribed HPC platforms, and create intuitive AI solutions that will lead to trans- formational scientific discoveries, and disruptive solutions in industry and technology Finally, to contribute to the use of realistic datasets to benchmark HPC platforms, we release two neural network models, along with datasets, that we used to produce Figs.

J Big Data Page 10 of 12 powerful HPC platforms for AI research, it is urgent that we provide guidelines to maxi- mize the use of these resources, and continue training new talent that will catalyze the adoption and best AI practices. This approach was critical in the past to enable the adop- tion of HPC by industry, and will play a more significant role in the future given the eagerness with which industry is adopting AI solutions.

Acknowledgements We thank Nicholas A. All authors contributed to developing the ideas, and writing and reviewing this manuscript. All authors read and approved the final manuscript. Availability of data and materials The neural network models and data used to study characterize black hole mergers, and to classify galaxy images, are are readily available at the Deep Learning Hub DLHub [30, 31] hosted by Argonne National Laboratory ANL [60, 61].

Ethics approval and consent to participate Not applicable Consent for publication The authors approve the publication of this manuscript Competing interests The authors declare that they have no competing interests.

Received: 24 June Accepted: 27 September References 1. Big data and extreme-scale computing: Pathways to convergence- toward a shaping strategy for a future software and data ecosystem for scientific inquiry.

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