Part 1 Maximizing the utilization of GPU resources on-premise and in the cloud
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This document provides an agenda for a presentation on maximizing GPU utilization on-premise and in the cloud. The agenda includes welcome remarks, presentations on Multi-instance GPU capabilities on NVIDIA A100 chips and scheduling GPUs across on-premise and cloud environments. It also discusses an Univa survey on GPU usage which found that most organizations rely on GPUs for applications like analytics, chemistry simulations and machine learning, though barriers include application compatibility, GPU costs and lack of skills.
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Part 2 Maximizing the utilization of GPU resources on-premise and in the cloud
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This document discusses NVIDIA's updates from spring through fall 2020. It introduces the new NVIDIA AMPERE architecture with the GeForce RTX 30 series GPUs. It also discusses the release of the NVIDIA HPC SDK and NVIDIA GTC fall 2020 conference. The document focuses on NVIDIA's role in accelerating AI workloads and introduces the new DGX SuperPod AI infrastructure solution. It outlines challenges with deploying AI on GPUs and proposes solutions around provisioning specialized GPU infrastructure and monitoring GPU utilization.Part 4 Maximizing the utilization of GPU resources on-premise and in the cloud
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2. It introduces Pivotal Container Service (PKS) which provides a production-ready container platform for deploying Kubernetes clusters on VMware vSphere.
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Developing, experimenting, and deploying ML models at scale requires substantial tooling, scripting, tracking, versioning, and monitoring.
Watch full video here: https://cnvrg.io/webinars-and-workshops/scaling-mlops-on-nvidia-dgx-systems/
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They lack user friendly tools needed to track experiments, attach resources, manage datasets and launch multiple ML pipelines.
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We will show best practices to reach high utilization of NVIDIA DGX systems, while conducting meta-scheduling across multiple heterogeneous Kubernetes/OpenShift/Linux server clusters.
In addition, we will introduce the concept of production flows, which automate hundreds of models from the data hub to deployment. We will wrap up with a real-life demo of flows, exercising many experiments across DGX platforms.
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[OpenInfra Days Korea 2018] (NetApp) Open Source with NetApp - 전국섭 상무
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www.theubercloud.com/cloud-hpc
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Hyperconverged Systems for Digital Transformation
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Some key points:
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IBM ALM for aviation safety compliance aerospace
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Event processing in real time: events collected from different parts of the organization should be combined into actionable insights in real time. This is extremely challenging in a context where events can be lost or delayed.
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Part 1 Maximizing the utilization of GPU resources on-premise and in the cloud
- 1. Welcome Maximizing the utilization of GPU resources on-premise and in the cloud 1
- 2. Agenda • Welcome and introduction • What's new from NVIDIA – Barton Fiske • Technical overview of Multi-instance GPU (MIG) on the NVIDIA A100 Tensor Core GPU – Adam Tetelman • Advanced scheduling of GPUs on-premise and in the cloud - Docker and NVIDIA HW & SW - portability of GPU-based applications - Bill Bryce • Q&A and panel discussion
- 3. Univa delivers business value 3 Extreme Scale Largest single cluster in AWS cloud 50 Employees Chicago, Munich, Toronto 475+ years of collective HPC experience Largest independent provider of software-defined computing and workload orchestration 250 3.3M+ cores Under management Univa clients are global leaders in their fields Innovation Platform A source of competitive differentiation for market leaders Cross-industry Customers 7 of top 10 Life Sciences, 2 of the largest Oil & Gas, 5 top HPC ISVs World-class Support Most satisfied enterprise customers Boost application performance Increase user productivity Maximize existing resources Migrate to the cloud Acquired by
- 4. 52% Analytics Chemistry Structural CFD Life Sciences Visualization ML/DL/AI 2020 Univa State of the GPU in HPC Survey 83% Rely on GPUs for HPC Applications Limited usePilot 17% 21% Production 62% What GPU workloads do they run? 57% 40% 33% 28% 26% 24% 19% Application compatibility Cost of GPUs Lack of skills or experience What are the top three barriers to GPU adoption? 69% 80% of users run three or more distinct GPU workloads 57% 40% Public clouds On-prem desktops On-prem servers Where do GPU workloads run? 71% 60% 52% Most GPU applications are in production 57% 26% 17% 7% 33% run distributed GPU applications using MPI or other parallel frameworks. 33% run parallel jobs 57% use HPC workload managers use public cloud 48% run in containers 14% Docker 33% Singularity
- 5. Panel Discussion and Q&A Maximizing the utilization of GPU resources on-premise and in the cloud 5
- 6. Thank you 6