Supermicro’s Universal GPU: Modular, Standards Based and Built for the Future

This document discusses AMD's chiplet architecture for high-performance server and desktop processors. Key points include: - AMD partitions the system-on-a-chip design, using 7nm technology for CPU cores while leaving I/O interfaces in older process nodes. This improves performance and lowers costs. - CPU dies ("chiplets") are connected using high-speed SerDes links both on-package and between dies. This allows for more chiplets and cores than traditional monolithic designs. - Innovations in packaging, power distribution, and operating system scheduling were required to enable the multi-chiplet design and improve performance.

Lee Bushen, Senior Solutions Architect at NVIDIA covers the basics of NVIDIA Virtual GPU. - Why vGPU? - How does it work? - What are the main considerations for VDI? - Which GPU is right for me? - Which License do I need?

In this deck from the UK HPC Conference, Gunter Roeth from NVIDIA presents: Hardware & Software Platforms for HPC, AI and ML. "Data is driving the transformation of industries around the world and a new generation of AI applications are effectively becoming programs that write software, powered by data, vs by computer programmers. Today, NVIDIA’s tensor core GPU sits at the core of most AI, ML and HPC applications, and NVIDIA software surrounds every level of such a modern application, from CUDA and libraries like cuDNN and NCCL embedded in every deep learning framework and optimized and delivered via the NVIDIA GPU Cloud to reference architectures designed to streamline the deployment of large scale infrastructures." Watch the video: https://wp.me/p3RLHQ-l2Y Learn more: http://nvidia.com and http://hpcadvisorycouncil.com/events/2019/uk-conference/agenda.php Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter

Une très bonne présentation qui introduit la technologie NVM Express qui sera à coup sure l'interface du futur (proche) des "disques" SSD. Adieu SAS et SATA, bienvenu au PCI Express dans les serveurs (et postes clients)

This document summarizes a presentation about software defined storage using the open source Gluster file system. It begins with an overview of storage concepts like reliability, performance, and scaling. It then discusses the history and types of storage and provides case studies of proprietary storage systems. The presentation introduces software defined storage and Gluster, describing its modular design, use in cloud computing, pros and cons. Key Gluster concepts are defined and its distributed and replicated volume types are explained. The presentation concludes with instructions for setting up and using Gluster.

This document discusses the key components and architecture of the Linux kernel. It begins by defining the kernel as the central module of an operating system that loads first and remains in memory, providing essential services. It then describes the major subsystems of Linux, including process management, memory management, virtual file systems, network stacks, and device drivers. It concludes that the modular design of the Linux kernel has supported its growth and success through independent and extensible development of these subsystems.

This document provides an overview of Vector Packet Processing (VPP), an open source packet processing platform developed as part of the FD.io project. VPP is based on DPDK for high performance packet processing in userspace. It includes a full networking stack and can perform L2/L3 forwarding and routing at speeds of over 14 million packets per second on a single core. VPP processing is divided into individual nodes connected by a graph. Packets are passed between nodes as vectors to support batch processing. VPP supports both single and multicore modes using different threading models. It can be used to implement routers, switches, and other network functions and topologies.

Red Hat is a leading open source solutions provider with over $1 billion in revenue. Some key points about Red Hat: - Founded in 1993 and went public in 1999. - Provides a range of open source products and solutions including operating systems, middleware, management tools, and more. - Has over 8,300 employees and offices in 35+ countries serving over 90% of Fortune 500 companies. - Offers subscription-based support and assistance for its open source technologies.

The document discusses security issues with remote direct memory access (RDMA) and potential attacks. It proposes a secure RDMA system design where a smartNIC protects packets by authenticating, encrypting, and validating them before performing RDMA operations between endpoints. The smartNIC offloads cryptographic operations to improve performance and security compared to relying only on the host CPU. Future plans include programming InfiniBand RDMA and offloading specific cipher suites to the smartNIC.

OmniXtend is an open source cache coherence protocol that runs over Ethernet. It allows for a unified memory fabric that scales beyond what is possible with traditional CPU-centric architectures. OmniXtend implements the TileLink cache coherence protocol over Ethernet frames, eliminating the need to rewrite software and enabling new data-centric architectures by decoupling compute from memory. The CHIPS Alliance is developing OmniXtend as an open standard with the goal of driving more collaboration in the hardware development community.

NVM Express (NVMe) is a new protocol designed specifically for high performance solid state storage. It addresses limitations of previous protocols like SATA and SAS by supporting many more queues and commands in parallel. NVMe can support over 1 million IOPS compared to 200,000 for SATA. It is supported across operating systems and form factors from mobile to data center storage. NVMe is optimized for emerging non-volatile memory technologies and expected to become the dominant storage interface.

The document summarizes a presentation about AMD's new "Zen" x86 CPU core architecture. The Zen architecture provides a 40% increase in instructions per clock compared to previous cores through improvements in the core engine, caches, floating point capabilities, and the addition of simultaneous multithreading. The Zen core was designed from the ground up to optimize performance and power efficiency across applications from notebooks to supercomputers.

This document discusses NVIDIA's technologies for artificial intelligence and accelerated computing. It highlights NVIDIA's GPUs, systems, SDKs, and frameworks that power AI workloads at scale. These include the H100 GPU, DGX systems, Triton inference server, RAPIDS libraries, and Omniverse platform for simulation and digital twins. The document also outlines key applications and industries that are being accelerated by NVIDIA's technologies like autonomous vehicles, healthcare, robotics, and more.

PCクラスタコンソーシアムの開催する PCCC22（第22回PCクラスタシンポジウム）「HPCシステム技術の最前線」 会員展示として公開中のスライドです。 詳細は下記のWebサイトへお越しください。 ▽PCCC22 「HPCシステム技術の最前線」 （2022年12月5日～6日） https://www.pccluster.org/ja/event/pccc22/

Introducing the new On-Demand Private Cloud. Supermicro and InMotion Hosting joined forces to design a Data Center POD solution that allows data centers to take control of their cloud costs by lowering the total cost per VM. This all-in-one solution consisting of small hyper-converged building blocks, are built for your business to achieve significant on-demand flexibility and scalability. Utilizing a consumption based model eliminates multiple vendors by consolidating hardware, software, networking, management, and administration enabling your data center to grow and shrink based on your business’ needs. Bring your business to the next level with an On-Demand Private Cloud, let it help you achieve greater profitability by eliminating overpriced inflated, high-cost licensing fees. This streamlined model removes overpriced licensing fees, and enables you to increase profits while reducing overhead costs. This On-Demand Private Cloud solution is built with Supermicro’s green, power efficient and high density, compute servers; OpenStack’s open-source software, and Ceph’s object, file, and block storage. Join this webinar to hear industry experts from InMotion Hosting and Supermicro discuss: - Building your next-generation data center infrastructure - Enables private data centers with validated solutions from Supermicro and InMotion - Hosting to improve operational efficiency - Reach new segments with Kubernetes, Machine Learning, and Artificial Intelligence - Achieve on-demand cloud computing, high availability, data redundancy, and flexibility - Take control of your cloud costs and lower your total cost of ownership - Hyper-dense hardware enables economies of scale for power, cooling, and physical space

Hardware for deep learning includes CPUs, GPUs, FPGAs, and ASICs. CPUs are general purpose but support deep learning through instructions like AVX-512 and libraries. GPUs like NVIDIA and AMD models are commonly used due to high parallelism and memory bandwidth. FPGAs offer high efficiency but require specialized programming. ASICs like Google's TPU are customized for deep learning and provide high performance but limited flexibility. Emerging hardware aims to improve efficiency and better match neural network computations.

Join us for a special edition of Supermicro’s TECHTalk as we introduce Supermicro’s new accelerated compute infrastructure solutions. A number of Supermicro experts will share insights and updates on one of the industry’s broadest portfolios of NVIDIA-Certified GPU systems, which deliver new levels of performance for AI infrastructure with the new H100 Tensor Core GPUs.

In this webinar, members of the Server Solution Team as well as a member of Supermicro’s Product Office will discuss Supermicro’s Universal GPU Server, the server’s modular, standards-based design, the important role of OCP Accelerator Module (OAM) form factor, and Universal Baseboard (UBB) in the system, as well as touching on AMD's next generation HPC accelerator. In addition, we will get some insights into trends in the HPC and AI/Machine Learning space, including the different software platforms and best practices that are driving innovation in our industry and daily lives. In particular: • Tools to enable use of the high performance hardware for HPC and Deep Learning applications • Tools to enable use of multiple GPUs, including RDMA, to solve highly demanding HPC and deep learning models, such as BERT • Running applications in containers with AMD’s next generation GPU system