Using Docker for GPU Accelerated Applications
Download as pptx, pdf21 likes127,561 views
The document discusses the use of Docker for GPU-accelerated applications, highlighting the challenges and solutions in GPU computing, especially within NVIDIA's ecosystem. It covers applications in various fields such as deep learning, medical imaging, and autonomous machines, while stressing the importance of proper driver management and container support. The presentation emphasizes how Docker facilitates the workflow for developing and deploying GPU applications effectively.
1 of 18
Downloaded 237 times
![5
// Vector sum in C
void vector_add(int n, const float* a, const float* b, float* c)
{
for (int idx = 0; idx < n; ++idx)
c[idx] = a[idx] + b[idx];
}
// Vector sum in CUDA
__global__ void vector_add(int n, const float* a, const float* b, float* c)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < n)
c[idx] = a[idx] + b[idx];
}
CUDA C++ PROGRAMMING](https://image.slidesharecdn.com/146387dockercon16-160622172714/85/Using-Docker-for-GPU-Accelerated-Applications-5-320.jpg)
Ad
Recommended
Enabling Cognitive Workloads on the Cloud: GPUs with Mesos, Docker and Marath...
Enabling Cognitive Workloads on the Cloud: GPUs with Mesos, Docker and Marath...Indrajit Poddar This document provides an overview of enabling cognitive workloads on the cloud using GPUs with Mesos, Docker, and Marathon on IBM's POWER systems. It discusses requirements for GPUs in the cloud like exposing GPUs to containers and supporting multiple GPUs per node. It also summarizes Mesos and Kubernetes support for GPUs, and demonstrates running a deep learning workload on OpenPOWER hardware to identify dog breeds using Docker containers and GPUs.
OpenCL Programming 101
OpenCL Programming 101Yoss Cohen The document provides an overview of the OpenCL execution model, detailing key concepts such as kernels, programs, and the data-parallel execution model. It outlines the compilation process, including context creation, program loading, and kernel execution, supported by examples. Additionally, it emphasizes the asynchronous nature of queue operations and highlights further learning requirements for kernel function writing and argument setting.
Distributed tensorflow on kubernetes
Distributed tensorflow on kubernetesinwin stack This document discusses running distributed TensorFlow on Kubernetes. It introduces Kubernetes as a platform for container orchestration that can schedule containers across clusters. It then covers scheduling GPUs on Kubernetes nodes and introducing distributed TensorFlow for model replication and training. The remainder discusses building Docker images for distributed TensorFlow jobs, specifying configurations in Kubernetes YAML templates, and running distributed TensorFlow workflows on Kubernetes with workers, parameter servers and shared storage.
Introduction to OpenCL
Introduction to OpenCLUnai Lopez-Novoa This document introduces OpenCL, a framework for parallel programming across heterogeneous systems. OpenCL allows developers to write programs that access GPU and multi-core processors. It provides portability so the same code can run on different processor architectures. The document outlines OpenCL programming basics like kernels, memory objects, and host code that manages kernels. It also provides a simple "Hello World" example of vector addition in OpenCL and recommends additional resources for learning OpenCL.
Monitor PowerKVM using Ganglia, Nagios
Monitor PowerKVM using Ganglia, NagiosPradeep Kumar This document discusses monitoring PowerKVM nodes using the Ganglia and Nagios monitoring systems. It provides an overview of Ganglia and how it can be used to monitor metrics like CPU, memory, disk, and network usage on PowerKVM nodes and their virtual machines. It also discusses how Nagios can monitor PowerKVM hosts and services, how to add PowerKVM nodes to the Nagios server, and examples of host states and performance monitoring that Nagios provides.
Hands on OpenCL
Hands on OpenCLVladimir Starostenkov This document provides an agenda and overview for a Hands On OpenCL course. The course will cover setting up OpenCL platforms, important OpenCL concepts like the platform and memory models, and programming with OpenCL APIs. Exercises will allow students to set up OpenCL, run simple programs, and optimize programs for performance. Lectures will cover topics like kernels, work-items, memory hierarchies, profiling, and debugging OpenCL programs. The goal is for students to learn portable parallel programming with OpenCL for heterogeneous systems like CPUs, GPUs, and other processors.
GKE vs OpenStack Magnum
GKE vs OpenStack MagnumMotohiro OTSUKA This document compares Google Container Engine (GKE) and OpenStack Magnum. It summarizes that while Magnum provides container orchestration using Kubernetes similar to GKE, it has some usability disadvantages compared to GKE due to being integrated with OpenStack. The document demonstrates using Magnum and GKE to deploy similar Kubernetes clusters and discusses some areas Magnum could improve like integrating additional OpenStack services and improving node management capabilities.
Immutable kubernetes architecture by linuxkit
Immutable kubernetes architecture by linuxkit어형 이 The document discusses LinuxKit, an open-source toolkit for building secure, portable and immutable Linux distributions using containers. It provides an overview of LinuxKit's key features such as building Linux distributions from code, immutable infrastructure approach, and running on various platforms using the same binaries. The document also compares different infrastructure management methods like using scripts, configuration management and immutable infrastructure using LinuxKit.
OpenCVとPCLでのRealSenseのサポート状況+α
OpenCVとPCLでのRealSenseのサポート状況+αTsukasa Sugiura このドキュメントは、OpenCVとPCLにおけるRealSenseのサポート状況を説明し、RealSense SDKを使用してOpenCVでのビデオフレームのキャプチャやPCLでの点群データの取得を解説しています。また、OpenCVとPCLのそれぞれにおけるRealSenseの設定方法やコーディング例も提供されています。
容器化後，持續交付不可缺的敲門磚 - Helm
容器化後，持續交付不可缺的敲門磚 - HelmHung-Yen Chen Helm is an essential tool for continuous delivery after containerization. Helm is a package manager that provides template engines, release management, versioning, and other features to help manage Kubernetes applications and infrastructure as code. It allows defining common templates for repetitive workloads like workers to simplify management complexity. Migrating infrastructure to Kubernetes and using Helm can save costs, reduce deployment time, and increase productivity compared to alternatives. Helm is a stepping stone to future approaches like GitOps for pull-based DevOps workflows.
Kubernetes Basis: Pods, Deployments, and Services
Kubernetes Basis: Pods, Deployments, and ServicesJian-Kai Wang The document is a comprehensive guide on using Kubernetes, covering various deployment environments including public cloud services and bare metal servers. It provides detailed instructions on setting up Kubernetes clusters, operating pods, and managing resources, as well as utilizing health checks, labels, annotations, and service discovery. Additionally, it includes basic commands for managing Kubernetes objects and configurations through YAML manifests.
Introduction to OpenCL, 2010
Introduction to OpenCL, 2010Tomasz Bednarz This document provides an introduction to OpenCL, including:
- An overview of the OpenCL model and how work is distributed across CPUs and GPUs.
- A demonstration of an N-body simulation and how it can be parallelized with OpenCL.
- Details on OpenCL concepts like platforms, devices, memory model, and how applications are organized with host code and kernels.
PT-4057, Automated CUDA-to-OpenCL™ Translation with CU2CL: What's Next?, by W...
PT-4057, Automated CUDA-to-OpenCL™ Translation with CU2CL: What's Next?, by W...AMD Developer Central The document discusses the automated translation of CUDA code to OpenCL using the cu2cl tool, emphasizing the importance of OpenCL's portability without losing existing code functionality. It outlines the challenges involved in translation, particularly when ambiguities arise between CUDA and OpenCL constructs. The authors also address the current state of cu2cl, its effectiveness, and future directions for improving robustness and performance.
Tectonic Summit 2016: The Origins of Kubernetes
Tectonic Summit 2016: The Origins of KubernetesCoreOS The document outlines the history and development of Kubernetes, tracing its origins back to early container technologies and the experience of its co-founder Brendan Burns at Google. It highlights significant milestones such as the invention of Docker and the need for open source orchestration in cloud environments. The document also reflects on lessons learned, community growth, and ongoing challenges in configuration and scaling in the Kubernetes ecosystem.
GPU Accelerated Virtual Desktop Infrastructure (VDI) on OpenStack
GPU Accelerated Virtual Desktop Infrastructure (VDI) on OpenStackBrian Schott The document discusses GPU-accelerated Virtual Desktop Infrastructure (VDI) on OpenStack by Nimbis Services, Inc., outlining challenges and solutions for utilizing GPUs in virtual environments. It details various technologies and tests conducted, including integrations with Microsoft Hyper-V, XenServer, and open-source solutions, while highlighting the current state of development and the need for community support. The summary concludes with a statement on the feasibility of GPU-accelerated VDI pools within OpenStack, although production readiness remains an issue.
How to make cloud native platform by kubernetes
How to make cloud native platform by kubernetes어형 이 This document discusses how to build a cloud native platform using Kubernetes. It explains that Kubernetes provides a container-centric environment for orchestrating computing, networking, and storage infrastructure. It then discusses using Kubernetes objects like deployments and services to manage user applications. The document also covers using custom resource definitions to manage application metadata, exposing applications using Ingress, and supporting continuous delivery. Key aspects covered include Kubernetes architecture and controllers, object types, operations, and the Ingress controller.
Performance characteristics of traditional v ms vs docker containers (dockerc...
Performance characteristics of traditional v ms vs docker containers (dockerc...Boden Russell Docker containers provide significantly lower resource usage and higher density than traditional virtual machines when running multiple workloads concurrently on a server.
When booting 15 Ubuntu VMs with MySQL sequentially, Docker containers boot on average 3.5 seconds compared to 5.8 seconds for KVMs. During steady state operation of 15 active VMs, Docker uses on average 0.2% CPU and 49MB RAM per container, while KVMs use 1.9% CPU and 292MB RAM each. Docker maintains low 1-minute load averages of 0.15, while KVMs average 35.9% under load.
Building Docker images with Puppet
Building Docker images with PuppetNick Jones The document discusses building Docker images with Puppet, outlining the components of OpenStack and the need for configuration management to create custom images. It emphasizes using Puppet to manage infrastructure by describing desired states and integrating Docker for deploying OpenStack components. The challenges of integrating Puppet with Docker are highlighted, along with proposed solutions and alternative tools for configuration management.
Docker on docker leveraging kubernetes in docker ee
Docker on docker leveraging kubernetes in docker eeDocker, Inc. This document summarizes Docker's experience dogfooding Docker Enterprise Edition 2.0 which focuses on Kubernetes. Key aspects covered include planning the migration process over several months, preparing infrastructure to support both Swarm and Kubernetes workloads, upgrading control plane components, and migrating select internal applications to Kubernetes. Benefits realized include leveraging Kubernetes features like pods, cronjobs, and the ability to provide feedback that improved the product before general release.
Making cloud native platform by kubernetes
Making cloud native platform by kubernetes어형 이 This document discusses making a cloud native platform using Kubernetes. It introduces Nucleo, a platform that handles resource pooling, continuous delivery, scaling, logging and other tasks so developers can focus on development. It then describes Kubernetes and key Kubernetes concepts like ingress, persistent volumes, custom resource definitions, node selectors and taints/tolerations. Ingress is used for load balancing while persistent volumes handle storage. Custom resource definitions allow defining custom APIs and controllers to interact with them.
Moby and Kubernetes entitlements
Moby and Kubernetes entitlements Docker, Inc. The document proposes a new system called "entitlements" for defining high-level security profiles and permissions for containers. It would allow defining permissions profiles like "network.admin" instead of low-level options. Entitlements would be attached to container images to define their security properties. The goals are to improve the user experience, support a standard across platforms, and securely tie security profiles to images through signing. Implementation is still needed in Moby and Kubernetes. Feedback is welcomed on usability, default entitlements, and design.
Kubernetes
KubernetesMeng-Ze Lee Kubespray and Ansible can be used to automate the installation of Kubernetes in a production-ready environment. Kubespray provides tools to configure highly available Kubernetes clusters across multiple Linux distributions. Ansible is an IT automation tool that can deploy software and configure systems. The document then provides a 6 step guide for installing Kubernetes on Ubuntu using kubeadm, including installing Docker, kubeadm, kubelet and kubectl, disabling swap, configuring system parameters, initializing the cluster with kubeadm, and joining nodes. It also briefly explains Kubernetes architecture including the master node, worker nodes, addons, CNI, CRI, CSI and key concepts like pods, deployments, networking,
DevOps in AWS with Kubernetes
DevOps in AWS with KubernetesOleg Chunikhin The document provides an overview of Kubernetes, an open-source system designed for managing and orchestrating containerized applications in clusters. It covers key concepts such as Kubernetes architecture, objects like pods and services, and usage scenarios, as well as deployment options, especially on AWS with CloudFormation. The document also discusses pros and cons, future developments, and highlights Kubernetes' extensibility, scalability, and rich functionalities.
Learn kubernetes in 90 minutes
Learn kubernetes in 90 minutesLarry Cai This document provides an agenda and instructions for learning Kubernetes in 90 minutes. The agenda includes exercises on running a first web service in Kubernetes, revisiting pods, deployments and services, deploying with YAML files, and installing a microservices application called Guestbook. Key Kubernetes concepts covered include pods, deployments, services, YAML descriptors, and using deployments to scale applications. The document also provides background on containers, Docker, and the Kubernetes architecture.
Scalable TensorFlow Deep Learning as a Service with Docker, OpenPOWER, and GPUs
Scalable TensorFlow Deep Learning as a Service with Docker, OpenPOWER, and GPUsIndrajit Poddar This document discusses scaling TensorFlow deep learning using Docker, OpenPOWER systems, and GPUs. It provides an overview of distributing TensorFlow in a cluster with parameter servers and worker nodes. Example Dockerfiles are shown for creating deep learning images. The discussion also covers infrastructure components like Docker, OpenStack, and Mesos for managing compute resources in a deep learning cluster as a service.
VMware ESXi - Intel and Qlogic NIC throughput difference v0.6
VMware ESXi - Intel and Qlogic NIC throughput difference v0.6David Pasek The document compares network throughput differences between Intel x710 and QLogic FastLinQ QL41xxx NICs on ESXi 6.7, revealing significant performance disparities: Intel achieved around 300 Mbps while QLogic only managed about 100 Mbps. It explores potential causes, suggesting issues with the QLogic driver/firmware and the advantages of Intel's hardware features. The goal is to document NIC information and hypotheses for troubleshooting the observed performance issues in OpenShift environments.
Kubernetes extensibility
Kubernetes extensibilityDocker, Inc. The document discusses Kubernetes extensibility, highlighting its architecture as an abstraction layer over infrastructure, allowing users to customize networking, storage, and resource management through plugins and APIs. It explains various plugin frameworks like CNI for networking and CSI for storage, emphasizing Kubernetes' adaptability for diverse environments and use cases. Additionally, it covers the importance of API extensibility to support new resource types and admission webhooks for policy management.
Monitoring, Logging and Tracing on Kubernetes
Monitoring, Logging and Tracing on KubernetesMartin Etmajer The document discusses monitoring, logging and tracing tools for Kubernetes including Heapster, Grafana, Fluentd, Elastic Stack, Jolokia and OpenTracing. It provides examples of deploying Heapster with InfluxDB and Grafana for metrics collection, Fluentd to ingest container logs into Elasticsearch, and using Jolokia and OpenTracing for remote access to JMX metrics and distributed tracing functionality.
Using Docker for GPU-accelerated Applications by Felix Abecassis and Jonathan...
Using Docker for GPU-accelerated Applications by Felix Abecassis and Jonathan...Docker, Inc. The document discusses the use of Docker for GPU-accelerated applications, highlighting challenges and solutions for GPU isolation and remote deployment. It covers the CUDA ecosystem and various applications in machine learning, autonomous machines, and deep learning. Key points include the need for driver packaging and the benefits of using Nvidia Docker for efficient GPU support in containerized environments.
Raul sena - Apresentação Analiticsemtudo - Scientific Applications using GPU
Raul sena - Apresentação Analiticsemtudo - Scientific Applications using GPUEduardo Gaspar The document discusses the applications of GPU-accelerated computing in scientific fields, particularly emphasizing deep learning, analytics, and engineering. It provides examples of projects and research using CUDA, such as kernel density estimation and geospatial data analysis, while highlighting the performance benefits of GPU computing compared to traditional methods. Additionally, it outlines various practical applications across sectors like bioinformatics, finance, and medical imaging, showcasing the importance of GPUs in enhancing computational efficiency.
More Related Content
What's hot (20)
OpenCVとPCLでのRealSenseのサポート状況+α
OpenCVとPCLでのRealSenseのサポート状況+αTsukasa Sugiura このドキュメントは、OpenCVとPCLにおけるRealSenseのサポート状況を説明し、RealSense SDKを使用してOpenCVでのビデオフレームのキャプチャやPCLでの点群データの取得を解説しています。また、OpenCVとPCLのそれぞれにおけるRealSenseの設定方法やコーディング例も提供されています。
容器化後，持續交付不可缺的敲門磚 - Helm
容器化後，持續交付不可缺的敲門磚 - HelmHung-Yen Chen Helm is an essential tool for continuous delivery after containerization. Helm is a package manager that provides template engines, release management, versioning, and other features to help manage Kubernetes applications and infrastructure as code. It allows defining common templates for repetitive workloads like workers to simplify management complexity. Migrating infrastructure to Kubernetes and using Helm can save costs, reduce deployment time, and increase productivity compared to alternatives. Helm is a stepping stone to future approaches like GitOps for pull-based DevOps workflows.
Kubernetes Basis: Pods, Deployments, and Services
Kubernetes Basis: Pods, Deployments, and ServicesJian-Kai Wang The document is a comprehensive guide on using Kubernetes, covering various deployment environments including public cloud services and bare metal servers. It provides detailed instructions on setting up Kubernetes clusters, operating pods, and managing resources, as well as utilizing health checks, labels, annotations, and service discovery. Additionally, it includes basic commands for managing Kubernetes objects and configurations through YAML manifests.
Introduction to OpenCL, 2010
Introduction to OpenCL, 2010Tomasz Bednarz This document provides an introduction to OpenCL, including:
- An overview of the OpenCL model and how work is distributed across CPUs and GPUs.
- A demonstration of an N-body simulation and how it can be parallelized with OpenCL.
- Details on OpenCL concepts like platforms, devices, memory model, and how applications are organized with host code and kernels.
PT-4057, Automated CUDA-to-OpenCL™ Translation with CU2CL: What's Next?, by W...
PT-4057, Automated CUDA-to-OpenCL™ Translation with CU2CL: What's Next?, by W...AMD Developer Central The document discusses the automated translation of CUDA code to OpenCL using the cu2cl tool, emphasizing the importance of OpenCL's portability without losing existing code functionality. It outlines the challenges involved in translation, particularly when ambiguities arise between CUDA and OpenCL constructs. The authors also address the current state of cu2cl, its effectiveness, and future directions for improving robustness and performance.
Tectonic Summit 2016: The Origins of Kubernetes
Tectonic Summit 2016: The Origins of KubernetesCoreOS The document outlines the history and development of Kubernetes, tracing its origins back to early container technologies and the experience of its co-founder Brendan Burns at Google. It highlights significant milestones such as the invention of Docker and the need for open source orchestration in cloud environments. The document also reflects on lessons learned, community growth, and ongoing challenges in configuration and scaling in the Kubernetes ecosystem.
GPU Accelerated Virtual Desktop Infrastructure (VDI) on OpenStack
GPU Accelerated Virtual Desktop Infrastructure (VDI) on OpenStackBrian Schott The document discusses GPU-accelerated Virtual Desktop Infrastructure (VDI) on OpenStack by Nimbis Services, Inc., outlining challenges and solutions for utilizing GPUs in virtual environments. It details various technologies and tests conducted, including integrations with Microsoft Hyper-V, XenServer, and open-source solutions, while highlighting the current state of development and the need for community support. The summary concludes with a statement on the feasibility of GPU-accelerated VDI pools within OpenStack, although production readiness remains an issue.
How to make cloud native platform by kubernetes
How to make cloud native platform by kubernetes어형 이 This document discusses how to build a cloud native platform using Kubernetes. It explains that Kubernetes provides a container-centric environment for orchestrating computing, networking, and storage infrastructure. It then discusses using Kubernetes objects like deployments and services to manage user applications. The document also covers using custom resource definitions to manage application metadata, exposing applications using Ingress, and supporting continuous delivery. Key aspects covered include Kubernetes architecture and controllers, object types, operations, and the Ingress controller.
Performance characteristics of traditional v ms vs docker containers (dockerc...
Performance characteristics of traditional v ms vs docker containers (dockerc...Boden Russell Docker containers provide significantly lower resource usage and higher density than traditional virtual machines when running multiple workloads concurrently on a server.
When booting 15 Ubuntu VMs with MySQL sequentially, Docker containers boot on average 3.5 seconds compared to 5.8 seconds for KVMs. During steady state operation of 15 active VMs, Docker uses on average 0.2% CPU and 49MB RAM per container, while KVMs use 1.9% CPU and 292MB RAM each. Docker maintains low 1-minute load averages of 0.15, while KVMs average 35.9% under load.
Building Docker images with Puppet
Building Docker images with PuppetNick Jones The document discusses building Docker images with Puppet, outlining the components of OpenStack and the need for configuration management to create custom images. It emphasizes using Puppet to manage infrastructure by describing desired states and integrating Docker for deploying OpenStack components. The challenges of integrating Puppet with Docker are highlighted, along with proposed solutions and alternative tools for configuration management.
Docker on docker leveraging kubernetes in docker ee
Docker on docker leveraging kubernetes in docker eeDocker, Inc. This document summarizes Docker's experience dogfooding Docker Enterprise Edition 2.0 which focuses on Kubernetes. Key aspects covered include planning the migration process over several months, preparing infrastructure to support both Swarm and Kubernetes workloads, upgrading control plane components, and migrating select internal applications to Kubernetes. Benefits realized include leveraging Kubernetes features like pods, cronjobs, and the ability to provide feedback that improved the product before general release.
Making cloud native platform by kubernetes
Making cloud native platform by kubernetes어형 이 This document discusses making a cloud native platform using Kubernetes. It introduces Nucleo, a platform that handles resource pooling, continuous delivery, scaling, logging and other tasks so developers can focus on development. It then describes Kubernetes and key Kubernetes concepts like ingress, persistent volumes, custom resource definitions, node selectors and taints/tolerations. Ingress is used for load balancing while persistent volumes handle storage. Custom resource definitions allow defining custom APIs and controllers to interact with them.
Moby and Kubernetes entitlements
Moby and Kubernetes entitlements Docker, Inc. The document proposes a new system called "entitlements" for defining high-level security profiles and permissions for containers. It would allow defining permissions profiles like "network.admin" instead of low-level options. Entitlements would be attached to container images to define their security properties. The goals are to improve the user experience, support a standard across platforms, and securely tie security profiles to images through signing. Implementation is still needed in Moby and Kubernetes. Feedback is welcomed on usability, default entitlements, and design.
Kubernetes
KubernetesMeng-Ze Lee Kubespray and Ansible can be used to automate the installation of Kubernetes in a production-ready environment. Kubespray provides tools to configure highly available Kubernetes clusters across multiple Linux distributions. Ansible is an IT automation tool that can deploy software and configure systems. The document then provides a 6 step guide for installing Kubernetes on Ubuntu using kubeadm, including installing Docker, kubeadm, kubelet and kubectl, disabling swap, configuring system parameters, initializing the cluster with kubeadm, and joining nodes. It also briefly explains Kubernetes architecture including the master node, worker nodes, addons, CNI, CRI, CSI and key concepts like pods, deployments, networking,
DevOps in AWS with Kubernetes
DevOps in AWS with KubernetesOleg Chunikhin The document provides an overview of Kubernetes, an open-source system designed for managing and orchestrating containerized applications in clusters. It covers key concepts such as Kubernetes architecture, objects like pods and services, and usage scenarios, as well as deployment options, especially on AWS with CloudFormation. The document also discusses pros and cons, future developments, and highlights Kubernetes' extensibility, scalability, and rich functionalities.
Learn kubernetes in 90 minutes
Learn kubernetes in 90 minutesLarry Cai This document provides an agenda and instructions for learning Kubernetes in 90 minutes. The agenda includes exercises on running a first web service in Kubernetes, revisiting pods, deployments and services, deploying with YAML files, and installing a microservices application called Guestbook. Key Kubernetes concepts covered include pods, deployments, services, YAML descriptors, and using deployments to scale applications. The document also provides background on containers, Docker, and the Kubernetes architecture.
Scalable TensorFlow Deep Learning as a Service with Docker, OpenPOWER, and GPUs
Scalable TensorFlow Deep Learning as a Service with Docker, OpenPOWER, and GPUsIndrajit Poddar This document discusses scaling TensorFlow deep learning using Docker, OpenPOWER systems, and GPUs. It provides an overview of distributing TensorFlow in a cluster with parameter servers and worker nodes. Example Dockerfiles are shown for creating deep learning images. The discussion also covers infrastructure components like Docker, OpenStack, and Mesos for managing compute resources in a deep learning cluster as a service.
VMware ESXi - Intel and Qlogic NIC throughput difference v0.6
VMware ESXi - Intel and Qlogic NIC throughput difference v0.6David Pasek The document compares network throughput differences between Intel x710 and QLogic FastLinQ QL41xxx NICs on ESXi 6.7, revealing significant performance disparities: Intel achieved around 300 Mbps while QLogic only managed about 100 Mbps. It explores potential causes, suggesting issues with the QLogic driver/firmware and the advantages of Intel's hardware features. The goal is to document NIC information and hypotheses for troubleshooting the observed performance issues in OpenShift environments.
Kubernetes extensibility
Kubernetes extensibilityDocker, Inc. The document discusses Kubernetes extensibility, highlighting its architecture as an abstraction layer over infrastructure, allowing users to customize networking, storage, and resource management through plugins and APIs. It explains various plugin frameworks like CNI for networking and CSI for storage, emphasizing Kubernetes' adaptability for diverse environments and use cases. Additionally, it covers the importance of API extensibility to support new resource types and admission webhooks for policy management.
Monitoring, Logging and Tracing on Kubernetes
Monitoring, Logging and Tracing on KubernetesMartin Etmajer The document discusses monitoring, logging and tracing tools for Kubernetes including Heapster, Grafana, Fluentd, Elastic Stack, Jolokia and OpenTracing. It provides examples of deploying Heapster with InfluxDB and Grafana for metrics collection, Fluentd to ingest container logs into Elasticsearch, and using Jolokia and OpenTracing for remote access to JMX metrics and distributed tracing functionality.
PT-4057, Automated CUDA-to-OpenCL™ Translation with CU2CL: What's Next?, by W...
PT-4057, Automated CUDA-to-OpenCL™ Translation with CU2CL: What's Next?, by W...AMD Developer Central
Similar to Using Docker for GPU Accelerated Applications (20)
Using Docker for GPU-accelerated Applications by Felix Abecassis and Jonathan...
Using Docker for GPU-accelerated Applications by Felix Abecassis and Jonathan...Docker, Inc. The document discusses the use of Docker for GPU-accelerated applications, highlighting challenges and solutions for GPU isolation and remote deployment. It covers the CUDA ecosystem and various applications in machine learning, autonomous machines, and deep learning. Key points include the need for driver packaging and the benefits of using Nvidia Docker for efficient GPU support in containerized environments.
Raul sena - Apresentação Analiticsemtudo - Scientific Applications using GPU
Raul sena - Apresentação Analiticsemtudo - Scientific Applications using GPUEduardo Gaspar The document discusses the applications of GPU-accelerated computing in scientific fields, particularly emphasizing deep learning, analytics, and engineering. It provides examples of projects and research using CUDA, such as kernel density estimation and geospatial data analysis, while highlighting the performance benefits of GPU computing compared to traditional methods. Additionally, it outlines various practical applications across sectors like bioinformatics, finance, and medical imaging, showcasing the importance of GPUs in enhancing computational efficiency.
Computing using GPUs
Computing using GPUsShree Kumar The document discusses GPU computing, covering its history, advantages, and the differences between CPU and GPU architectures. It highlights the capabilities of GPUs for high-performance computing, including programming challenges and the tools available, such as CUDA and OpenCL. Additionally, it addresses future developments in GPU technology and provides resources for further exploration.
GPU enablement for data science on OpenShift | DevNation Tech Talk
GPU enablement for data science on OpenShift | DevNation Tech TalkRed Hat Developers The document is a presentation by Pete Mackinnon on GPU enablement for data science on OpenShift, detailing technical aspects of the AI/ML lifecycle and components of GPU support. It discusses the significant performance improvements GPUs can provide in data processing compared to CPUs, including hardware specifications and examples of accelerated workloads. The NVIDIA GPU operator and its deployment in OpenShift environments are explored, emphasizing its role in facilitating GPU resource management and monitoring.
Cuda
CudaGopi Saiteja This document provides an introduction to the CUDA parallel computing platform from NVIDIA. It discusses the CUDA hardware capabilities including GPUDirect, Dynamic Parallelism, and HyperQ. It then outlines three main programming approaches for CUDA: using libraries, OpenACC directives, and programming languages. It provides examples of libraries like cuBLAS and cuRAND. For OpenACC, it shows how to add directives to existing Fortran/C code to parallelize loops. And for languages, it lists supports like CUDA C/C++, CUDA Fortran, Python with PyCUDA etc. The document aims to provide developers with maximum flexibility in choosing the best approach to accelerate their applications using CUDA and GPUs.
Cuda Without a Phd - A practical guick start
Cuda Without a Phd - A practical guick startLloydMoore This document provides an overview of CUDA, NVIDIA's framework for developing applications on GPU hardware, emphasizing the benefits of using GPU for massively parallel processing. It outlines the architecture of GPUs compared to CPUs, explains the setup required for CUDA, and presents a simple example of computing the hypotenuse of triangles using both CPU and GPU implementations. The document also touches on kernel execution, debugging techniques, and additional resources for further learning in CUDA development.
GPU Programming
GPU ProgrammingWilliam Cunningham This document provides an introduction to parallel programming using GPUs. It outlines the hardware architecture of GPUs, which have hundreds of cores optimized for processing pixels in parallel. It then discusses CUDA programming, with examples of initializing the GPU, allocating and transferring memory, executing kernels, and common applications in physics, finance, and other fields. The document concludes by discussing the sparse conjugate gradient method for inverting matrices on the GPU as an example application in computational physics.
DCSF 19 Accelerating Docker Containers with NVIDIA GPUs
DCSF 19 Accelerating Docker Containers with NVIDIA GPUsDocker, Inc. The document discusses the acceleration of Docker containers using NVIDIA GPUs, emphasizing the significant performance improvements for compute-intensive tasks such as deep learning. It highlights the evolution of GPU support in Docker, including the introduction of native support and containerized drivers, making it easier for developers to deploy GPU-optimized applications. Furthermore, it outlines the future potential of integrating NVIDIA runtime capabilities with container orchestration tools like Kubernetes.
NASA Advanced Supercomputing (NAS) Division - Programming and Building HPC Ap...
NASA Advanced Supercomputing (NAS) Division - Programming and Building HPC Ap...VICTOR MAESTRE RAMIREZ This document provides an overview of programming and building high-performance computing (HPC) applications for NVIDIA GPUs, focusing on methods recommended by NVIDIA for CPU offloading and explicit/implicit data movement. It contrasts the architecture and functions of CPU and GPU and discusses relevant CUDA programming techniques and libraries, including examples and compiler directives. Additionally, it addresses the importance of compute capability for GPUs and includes details about CUDA toolkit and driver compatibility.
OpenSUSE Conference 2019 - Building GPU aware containers
OpenSUSE Conference 2019 - Building GPU aware containersAlessandro Festa The document discusses a developer program for data scientists focused on using GPU-aware containers for machine learning and deep learning applications. It covers technical requirements, challenges with NVIDIA drivers, and the available container images on NVIDIA's Docker Hub. The session emphasizes the need for easy deployment and transport of machine learning frameworks while addressing the complexities faced by data scientists in using these technologies.
GPU Computing: An Introduction
GPU Computing: An Introductionijtsrd The document provides an introduction to GPU computing, emphasizing its role as a co-processor that accelerates scientific and engineering tasks through its high-performance, parallel processing capabilities. It highlights the evolution of GPUs from graphics rendering devices to essential components in various computational applications such as biological simulation, financial modeling, and computational fluid dynamics. Key benefits of GPU computing include increased performance and efficiency, although it faces challenges like memory management and limitations in multithreading for complex data structures.
Cuda meetup presentation 5
Cuda meetup presentation 5Rihards Gailums This document provides a summary of a meeting about CUDA parallel programming and gaming:
- The meeting covered topics like Nvidia news from CES, the Jetson TK1 board, parallel programming experience with CUDA, and announcements about upcoming meetups.
- Attendees learned about using the Jetson TK1 for projects involving drones, computer vision, and 3D printing. Demonstrations showed the Parrot drone working with the Jetson TK1.
- Resources were shared for installing CUDA and OpenCV on the Jetson TK1 along with links to documentation and tutorials. Questions were taken about potential research projects using the Jetson TK1.
Hardware & Software Platforms for HPC, AI and ML
Hardware & Software Platforms for HPC, AI and MLinside-BigData.com The document provides an overview of NVIDIA's hardware and software solutions for high-performance computing (HPC), artificial intelligence (AI), and machine learning (ML), highlighting the capabilities of Tesla GPUs and various systems. It details the achievements of NVIDIA in powering the world's fastest supercomputers and discusses various NVIDIA products, SDKs, and libraries designed to enhance performance and energy efficiency in computational tasks. Additionally, it mentions innovations like mixed-precision computing, GPU Direct technology, and software ecosystems aimed at optimizing data-intensive workflows.
Parallel computing with Gpu
Parallel computing with GpuRohit Khatana This document discusses parallel computing with GPUs. It introduces parallel computing, GPUs, and CUDA. It describes how GPUs are well-suited for data-parallel applications due to their large number of cores and throughput-oriented design. The CUDA programming model is also summarized, including how kernels are launched on the GPU from the CPU. Examples are provided of simple CUDA programs to perform operations like squaring elements in parallel on the GPU.
Application Optimisation using OpenPOWER and Power 9 systems
Application Optimisation using OpenPOWER and Power 9 systemsGanesan Narayanasamy This document discusses various ways to accelerate applications using GPUs and CUDA programming. It provides examples of using libraries, programming languages like OpenACC and CUDA, and tools like Nsight to add GPU acceleration. It also highlights many success stories and how applications from fields like HPC, deep learning, and computational chemistry have achieved speedups using these techniques. Resources and compilers are available to help developers get started with GPU programming.
Report on GPGPU at FCA (Lyon, France, 11-15 October, 2010)
Report on GPGPU at FCA (Lyon, France, 11-15 October, 2010)PhtRaveller The document discusses the advancements in GPU computing, emphasizing its relevance for scientific computations due to its high parallelism capabilities. It outlines the CUDA architecture, performance comparisons with traditional CPUs, various applications including physics and medical imaging, and the evolution of GPU programming. The future prospects of GPU computing are highlighted as a means to achieve powerful desktop supercomputing.
The Rise of Parallel Computing
The Rise of Parallel Computingbakers84 The document discusses the limitations of traditional CPU performance as driven by Moore's Law and advocates for a shift to parallel processing using GPUs for enhanced productivity and efficiency. It outlines various types of parallelism, programming models such as CUDA, C++ AMP, and OpenCL, as well as applications in image processing and other fields. The conclusion emphasizes the necessity for adapting to parallel computing to achieve significant performance gains as CPUs reach their limits.
Gpu perf-presentation
Gpu perf-presentationGiannisTsagatakis This document provides a tutorial introduction to GPGPU computation using NVIDIA CUDA. It begins with a brief overview and warnings about the large numbers involved in GPGPU. The agenda then outlines topics to be covered including general purpose GPU computing using CUDA and optimization topics like memory bandwidth optimization. Key aspects of CUDA programming are introduced like the CUDA memory model, compute capabilities of GPUs, and profiling tools. Examples are provided of simple CUDA kernels and how to configure kernel launches for grids and blocks of threads. Optimization techniques like choosing block/grid sizes to maximize occupancy are also discussed.
GPGPU Computation
GPGPU Computationjtsagata This document provides a tutorial introduction to GPGPU computation using NVIDIA CUDA. It begins with a brief overview and warnings about the large numbers involved in GPGPU. The agenda then outlines topics to be covered including general purpose GPU computing using CUDA and optimization topics like memory bandwidth optimization. Key aspects of CUDA programming are introduced like the CUDA memory model, compute capabilities of GPUs, and profiling tools. Examples are provided of simple CUDA kernels and how to configure kernel launches for grids and blocks of threads. Optimization techniques like choosing block/grid sizes to maximize occupancy are also discussed.
OpenACC Monthly Highlights: January 2021
OpenACC Monthly Highlights: January 2021OpenACC The January 2021 OpenACC Monthly Highlights detail the growth of the OpenACC programming model for parallel computing on CPUs and GPUs, highlighting its wide adoption in key HPC codes like VASP and performance benefits over CUDA. Important advancements include the upcoming Frontier supercomputer at Oak Ridge National Laboratory and initiatives to optimize GPU computing in various scientific fields through events and collaborative research. Resources such as papers and webinars are provided to enhance understanding and application of OpenACC directives in high-performance computing.
NASA Advanced Supercomputing (NAS) Division - Programming and Building HPC Ap...
NASA Advanced Supercomputing (NAS) Division - Programming and Building HPC Ap...VICTOR MAESTRE RAMIREZ
Ad
More from NVIDIA (20)
NVIDIA Story 2023.pdf
NVIDIA Story 2023.pdfNVIDIA Nvidia is at the forefront of accelerated computing and AI, transforming various industries through innovations that span gaming, healthcare, and robotics. With a robust ecosystem of developers and applications, Nvidia is driving advancements in generative AI, sustainable computing, and advanced graphics technology. Their commitment to creating cutting-edge hardware and cloud solutions positions them as a leader in enabling the next generation of AI infrastructure and digitalization.
NVIDIA GTC2022 Spring Highlights
NVIDIA GTC2022 Spring HighlightsNVIDIA Promising to transform trillion-dollar industries and address the “grand challenges” of our time, NVIDIA founder and CEO Jensen Huang shared a vision of an era where intelligence is created on an industrial scale and woven into real and virtual worlds at GTC 2022.
NVIDIA Brochure 2021 Company Overview
NVIDIA Brochure 2021 Company OverviewNVIDIA NVIDIA pioneered accelerated computing and GPUs for AI. It has reinvented itself through innovations like RTX ray tracing and Omniverse simulation. NVIDIA now powers the world's top supercomputers, data centers, industries and is a leader in autonomous vehicles and healthcare with its AI platforms.
NVIDIA GTC 2020 October Summary
NVIDIA GTC 2020 October SummaryNVIDIA The document discusses NVIDIA's Drive Sim technology, which utilizes sensors to enhance vehicle dynamics and traffic scenarios. It is integrated with the Omniverse platform to improve control and perception in simulated environments. This technology aims to advance the capabilities of autonomous driving simulations.
The Best of AI and HPC in Healthcare and Life Sciences
The Best of AI and HPC in Healthcare and Life SciencesNVIDIA The 2020 GPU Technology Conference (GTC) focuses on AI and HPC applications in healthcare, featuring over 600 sessions and 8,000 attendees. Participants can engage in hands-on deep learning trainings, meet industry experts, and explore the latest advancements in medical imaging and genomics. The conference will take place at the San Jose McEnery Convention Center from March 23-26, 2020.
NVIDIA CEO Jensen Huang Presentation at Supercomputing 2019
NVIDIA CEO Jensen Huang Presentation at Supercomputing 2019NVIDIA The document discusses the advancements in High-Performance Computing (HPC) through the use of NVIDIA technologies, including AI supercomputers and various applications in scientific research and industry. It highlights the capabilities of the NVIDIA EGX stack, CUDA optimizations, and extreme I/O performance to address complex computational problems across diverse domains. Additionally, it presents benchmarks and comparisons of cloud vs. traditional supercomputing approaches, emphasizing the cost-effectiveness and speed of GPU-based solutions.
NLP for Biomedical Applications
NLP for Biomedical ApplicationsNVIDIA The document discusses advancements in natural language understanding (NLU) within the biomedical and clinical fields, highlighting models like BioBERT and ClinicalBERT that outperform generic models like BERT on specific tasks. It emphasizes the importance of pre-training on domain-specific data and showcases the rise of multi-modal datasets and transformer models in conversational AI. Additionally, it mentions various applications in healthcare, including text classification, sentiment analysis, and speech recognition.
Top 5 Deep Learning and AI Stories - August 30, 2019
Top 5 Deep Learning and AI Stories - August 30, 2019NVIDIA The document discusses the top 5 trends in deep learning and artificial intelligence, emphasizing the importance of producing business value, transforming major industries, optimizing storage solutions, making strategic decisions, and the significance of data annotation. It highlights that AI must deliver substantial benefits to be considered successful and notes that the AI market is expected to grow significantly in the coming years. Furthermore, it underlines that proper implementation of AI can lead to improved operations, enhanced customer experiences, and increased revenues.
Seven Ways to Boost Artificial Intelligence Research
Seven Ways to Boost Artificial Intelligence ResearchNVIDIA The document outlines 7 ways to boost AI research including streamlining workflow productivity through container technology on NVIDIA's NGC container registry, accessing hundreds of optimized applications through NVIDIA's GPU applications catalog, iterating large datasets faster through discounted NVIDIA TITAN RTX GPUs, solving real-world problems through NVIDIA's deep learning institute courses, gaining insights from industry leaders through talks at the GPU technology conference, acquiring high quality research data through open databases, and learning more about NVIDIA's solutions for higher education and research.
NVIDIA Developer Program Overview
NVIDIA Developer Program OverviewNVIDIA The NVIDIA Developer Program offers numerous benefits, including product downloads, early access opportunities, technical documentation, and community engagement across various developer platforms. It provides essential resources for developers, such as training workshops, code samples, and bug submission tools, alongside access to exclusive events and insights from NVIDIA's technical staff. Developers can easily get started by creating an account to access tools, software, and support for accelerated computing and AI frameworks.
NVIDIA at Computex 2019
NVIDIA at Computex 2019 NVIDIA The document provides a comparison of NVIDIA graphics card architectures from the Pascal series to the Turing series, highlighting their performance metrics and market launch details. It includes sales figures and estimates for the first 15 weeks of the new architecture's availability. The focus is on how the advancements in technology translate into improved performance for gaming PCs and workstations.
Top 5 DGX Sessions From GTC 2019
Top 5 DGX Sessions From GTC 2019NVIDIA The document highlights five key sessions from GTC 2019 focused on deep learning implementation. Expert panels discussed challenges and solutions deployed by companies like BMW and Capital One, while sessions covered best practices for using NVIDIA's DGX systems and tools for maximizing AI capabilities in enterprises. Additionally, the document outlines insights into programming NVIDIA's DGX-2 architecture and the collaboration between Red Hat and NVIDIA for enhanced enterprise solutions.
DGX POD Top 4 Sessions From GTC 2019
DGX POD Top 4 Sessions From GTC 2019NVIDIA The document discusses sessions from GTC 2019 focused on deploying AI infrastructure effectively using NVIDIA DGX Pod technology. It emphasizes the importance of building scalable AI environments and addresses challenges in data management and processing across various industries. Additionally, it highlights practical insights and lessons learned from global deep learning deployments.
Top 5 Data Science Sessions from GTC 2019
Top 5 Data Science Sessions from GTC 2019NVIDIA NVIDIA's GPU Technology Conference (GTC) 2019 featured prominent data science sessions focusing on advancements in AI and deep learning applications. The top five sessions included topics from GPU-based dataframes to enhancing forecast accuracy and cybersecurity solutions with RAPIDS, a library for GPU-accelerated data analysis. The conference highlighted the significance of using GPUs in improving computational efficiency across various real-world challenges.
This Week in Data Science - Top 5 News - April 26, 2019
This Week in Data Science - Top 5 News - April 26, 2019NVIDIA Business leaders are leveraging data science to create competitive advantages, with a focus on essential skills like machine learning and Python. Key developments include partnerships between NVIDIA and top universities to advance graph analytics, and the evolution of data science workstations for enhanced AI deployment. Data science is being recognized as a critical component of the scientific method, emphasizing its growing importance in research and business decision-making.
GTC 2019 Keynote in Silicon Valley
GTC 2019 Keynote in Silicon ValleyNVIDIA The document outlines NVIDIA's advancements in accelerated computing and AI, highlighting the growth of CUDA developers and applications, as well as the company's initiatives in graphics, AI, and robotics. Key announcements include the development of the Omniverse for studio collaboration, advancements in AI ecosystems, and innovations in autonomous vehicle technology through the DRIVE platform. It emphasizes NVIDIA's ongoing commitment to enhancing performance and efficiency across various sectors through their GPU technologies.
CUDA DLI Training Courses at GTC 2019
CUDA DLI Training Courses at GTC 2019NVIDIA The document outlines the CUDA DLI training sessions at GTC 2019, emphasizing hands-on training in various computing domains including accelerated computing, data science, and GPU programming. Participants will learn to enhance applications using CUDA, Numba, and RAPIDS, covering topics such as memory management, performance optimization, and data science workflows. The sessions aim to equip attendees with practical skills through industry-standard tools and frameworks for effective GPU utilization.
DGX Sessions You Won't Want to Miss at GTC 2019
DGX Sessions You Won't Want to Miss at GTC 2019NVIDIA The document outlines a series of NVIDIA-led sessions at GTC 2019, focusing on various aspects of AI deployment and infrastructure, including best practices for creating AI workgroups, insights into NVIDIA's DGX systems, and customer-led discussions on deep learning challenges across industries. Noteworthy sessions include panels with experts on AI implementation, discussions on AI infrastructure from NVIDIA DGX Pod, and technical talks on programming and optimizing NVIDIA systems. The content emphasizes practical experience and expertise to support organizations in effectively leveraging AI technologies.
Transforming Healthcare at GTC Silicon Valley
Transforming Healthcare at GTC Silicon ValleyNVIDIA The GPU Technology Conference (GTC) 2019 in Silicon Valley focuses on innovations in AI and healthcare, featuring over 40 sessions on topics like genomics and medical imaging. Keynote speaker Dr. Eric Topol will discuss the impact of AI on health systems, while various sessions will cover data augmentation techniques and the use of deep learning in medical imaging. Attendees can learn about improvements in operational efficiencies and patient care through high-performance computing and deep learning applications.
OpenACC Monthly Highlights February 2019
OpenACC Monthly Highlights February 2019NVIDIA The February 2019 OpenACC monthly highlights document provides an overview of the OpenACC programming model, which enables parallel computing on CPUs and GPUs through simple directives. It emphasizes the model's growing adoption across various high-performance computing applications, offers details on upcoming events and bootcamps, and includes educational resources such as video tutorials and research papers. The document underscores OpenACC's advantages in performance and portability, especially in collaboration with GPU technologies.
Ad
Recently uploaded (20)
AI VIDEO MAGAZINE - June 2025 - r/aivideo
AI VIDEO MAGAZINE - June 2025 - r/aivideo1pcity Studios, Inc AI VIDEO MAGAZINE - r/aivideo community newsletter – Exclusive Tutorials: How to make an AI VIDEO from scratch, PLUS: How to make AI MUSIC, Hottest ai videos of 2025, Exclusive Interviews, New Tools, Previews, and MORE - JUNE 2025 ISSUE -
Curietech AI in action - Accelerate MuleSoft development
Curietech AI in action - Accelerate MuleSoft developmentshyamraj55 CurieTech AI in Action – Accelerate MuleSoft Development
Overview:
This presentation demonstrates how CurieTech AI’s purpose-built agents empower MuleSoft developers to create integration workflows faster, more accurately, and with less manual effort
linkedin.com
+12
curietech.ai
+12
meetups.mulesoft.com
+12
.
Key Highlights:
Dedicated AI agents for every stage: Coding, Testing (MUnit), Documentation, Code Review, and Migration
curietech.ai
+7
curietech.ai
+7
medium.com
+7
DataWeave automation: Generate mappings from tables or samples—95%+ complete within minutes
linkedin.com
+7
curietech.ai
+7
medium.com
+7
Integration flow generation: Auto-create Mule flows based on specifications—speeds up boilerplate development
curietech.ai
+1
medium.com
+1
Efficient code reviews: Gain intelligent feedback on flows, patterns, and error handling
youtube.com
+8
curietech.ai
+8
curietech.ai
+8
Test & documentation automation: Auto-generate MUnit test cases, sample data, and detailed docs from code
curietech.ai
+5
curietech.ai
+5
medium.com
+5
Why Now?
Achieve 10× productivity gains, slashing development time from hours to minutes
curietech.ai
+3
curietech.ai
+3
medium.com
+3
Maintain high accuracy with code quality matching or exceeding manual efforts
curietech.ai
+2
curietech.ai
+2
curietech.ai
+2
Ideal for developers, architects, and teams wanting to scale MuleSoft projects with AI efficiency
Conclusion:
CurieTech AI transforms MuleSoft development into an AI-accelerated workflow—letting you focus on innovation, not repetition.
GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdf
GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now.pdfPriyanka Aash GenAI Opportunities and Challenges - Where 370 Enterprises Are Focusing Now
UserCon Belgium: Honey, VMware increased my bill
UserCon Belgium: Honey, VMware increased my billstijn40 VMware’s pricing changes have forced organizations to rethink their datacenter cost management strategies. While FinOps is commonly associated with cloud environments, the FinOps Foundation has recently expanded its framework to include Scopes—and Datacenter is now officially part of the equation. In this session, we’ll map the FinOps Framework to a VMware-based datacenter, focusing on cost visibility, optimization, and automation. You’ll learn how to track costs more effectively, rightsize workloads, optimize licensing, and drive efficiency—all without migrating to the cloud. We’ll also explore how to align IT teams, finance, and leadership around cost-aware decision-making for on-prem environments. If your VMware bill keeps increasing and you need a new approach to cost management, this session is for you!
WebdriverIO & JavaScript: The Perfect Duo for Web Automation
WebdriverIO & JavaScript: The Perfect Duo for Web Automationdigitaljignect In today’s dynamic digital landscape, ensuring the quality and dependability of web applications is essential. While Selenium has been a longstanding solution for automating browser tasks, the integration of WebdriverIO (WDIO) with Selenium and JavaScript marks a significant advancement in automation testing. WDIO enhances the testing process by offering a robust interface that improves test creation, execution, and management. This amalgamation capitalizes on the strengths of both tools, leveraging Selenium’s broad browser support and WDIO’s modern, efficient approach to test automation. As automation testing becomes increasingly vital for faster development cycles and superior software releases, WDIO emerges as a versatile framework, particularly potent when paired with JavaScript, making it a preferred choice for contemporary testing teams.
Enhance GitHub Copilot using MCP - Enterprise version.pdf
Enhance GitHub Copilot using MCP - Enterprise version.pdfNilesh Gule Slide deck related to the GitHub Copilot Bootcamp in Melbourne on 17 June 2025
Oh, the Possibilities - Balancing Innovation and Risk with Generative AI.pdf
Oh, the Possibilities - Balancing Innovation and Risk with Generative AI.pdfPriyanka Aash Oh, the Possibilities - Balancing Innovation and Risk with Generative AI
Smarter Aviation Data Management: Lessons from Swedavia Airports and Sweco
Smarter Aviation Data Management: Lessons from Swedavia Airports and SwecoSafe Software Managing airport and airspace data is no small task, especially when you’re expected to deliver it in AIXM format without spending a fortune on specialized tools. But what if there was a smarter, more affordable way?
Join us for a behind-the-scenes look at how Sweco partnered with Swedavia, the Swedish airport operator, to solve this challenge using FME and Esri.
Learn how they built automated workflows to manage periodic updates, merge airspace data, and support data extracts – all while meeting strict government reporting requirements to the Civil Aviation Administration of Sweden.
Even better? Swedavia built custom services and applications that use the FME Flow REST API to trigger jobs and retrieve results – streamlining tasks like securing the quality of new surveyor data, creating permdelta and baseline representations in the AIS schema, and generating AIXM extracts from their AIS data.
To conclude, FME expert Dean Hintz will walk through a GeoBorders reading workflow and highlight recent enhancements to FME’s AIXM (Aeronautical Information Exchange Model) processing and interpretation capabilities.
Discover how airports like Swedavia are harnessing the power of FME to simplify aviation data management, and how you can too.
MuleSoft for AgentForce : Topic Center and API Catalog
MuleSoft for AgentForce : Topic Center and API Catalogshyamraj55 This presentation dives into how MuleSoft empowers AgentForce with organized API discovery and streamlined integration using Topic Center and the API Catalog. Learn how these tools help structure APIs around business needs, improve reusability, and simplify collaboration across teams. Ideal for developers, architects, and business stakeholders looking to build a connected and scalable API ecosystem within AgentForce.
Connecting Data and Intelligence: The Role of FME in Machine Learning
Connecting Data and Intelligence: The Role of FME in Machine LearningSafe Software In this presentation, we want to explore powerful data integration and preparation for Machine Learning. FME is known for its ability to manipulate and transform geospatial data, connecting diverse data sources into efficient and automated workflows. By integrating FME with Machine Learning techniques, it is possible to transform raw data into valuable insights faster and more accurately, enabling intelligent analysis and data-driven decision making.
OpenPOWER Foundation & Open-Source Core Innovations
OpenPOWER Foundation & Open-Source Core InnovationsIBM penPOWER offers a fully open, royalty-free CPU architecture for custom chip design.
It enables both lightweight FPGA cores (like Microwatt) and high-performance processors (like POWER10).
Developers have full access to source code, specs, and tools for end-to-end chip creation.
It supports AI, HPC, cloud, and embedded workloads with proven performance.
Backed by a global community, it fosters innovation, education, and collaboration.
Using the SQLExecutor for Data Quality Management: aka One man's love for the...
Using the SQLExecutor for Data Quality Management: aka One man's love for the...Safe Software The SQLExecutor is one of FME’s most powerful and flexible transformers. Pivvot maintains a robust internal metadata hierarchy used to support ingestion and curation of thousands of external data sources that must be managed for quality before entering our platform. By using the SQLExecutor, Pivvot can efficiently detect problems and perform analysis before data is extracted from our staging environment, removing the need for rollbacks or cycles waisted on a failed job. This presentation will walk through three distinct examples of how Pivvot uses the SQLExecutor to engage its metadata hierarchy and integrate with its Data Quality Management workflows efficiently and within the source postgres database. Spatial Validation –Validating spatial prerequisites before entering a production environment. Reference Data Validation - Dynamically validate domain-ed columns across any table and multiple columns per table. Practical De-duplication - Removing identical or near-identical well point locations from two distinct source datasets in the same table.
Quantum AI Discoveries: Fractal Patterns Consciousness and Cyclical Universes
Quantum AI Discoveries: Fractal Patterns Consciousness and Cyclical UniversesSaikat Basu Embark on a cosmic journey exploring the intersection of quantum
computing, consciousness, and ancient wisdom. Together we'll uncover the
recursive patterns that bind our reality.
Wenn alles versagt - IBM Tape schützt, was zählt! Und besonders mit dem neust...
Wenn alles versagt - IBM Tape schützt, was zählt! Und besonders mit dem neust...Josef Weingand IBM LTO10
Lessons Learned from Developing Secure AI Workflows.pdf
Lessons Learned from Developing Secure AI Workflows.pdfPriyanka Aash Lessons Learned from Developing Secure AI Workflows
10 Key Challenges for AI within the EU Data Protection Framework.pdf
10 Key Challenges for AI within the EU Data Protection Framework.pdfPriyanka Aash 10 Key Challenges for AI within the EU Data Protection Framework
Techniques for Automatic Device Identification and Network Assignment.pdf
Techniques for Automatic Device Identification and Network Assignment.pdfPriyanka Aash Techniques for Automatic Device Identification and Network Assignment
OpenACC and Open Hackathons Monthly Highlights June 2025
OpenACC and Open Hackathons Monthly Highlights June 2025OpenACC The OpenACC organization focuses on enhancing parallel computing skills and advancing interoperability in scientific applications through hackathons and training. The upcoming 2025 Open Accelerated Computing Summit (OACS) aims to explore the convergence of AI and HPC in scientific computing and foster knowledge sharing. This year's OACS welcomes talk submissions from a variety of topics, from Using Standard Language Parallelism to Computer Vision Applications. The document also highlights several open hackathons, a call to apply for NVIDIA Academic Grant Program and resources for optimizing scientific applications using OpenACC directives.
Using Docker for GPU Accelerated Applications
- 1. Felix Abecassis, Systems Software Engineer Jonathan Calmels, Systems Software Engineer USING DOCKER FOR GPU ACCELERATED APPLICATIONS
- 2. 2 AGENDA GPU COMPUTING CUDA Ecosystem Applications NVIDIA DOCKER Challenges Our Solution DEMOS GPU Isolation Machine Learning Remote Deployment
- 4. 4 HETEROGENEOUS COMPUTING GPU CPU + Optimized for parallel tasks Optimized for serial tasks
- 5. 5 // Vector sum in C void vector_add(int n, const float* a, const float* b, float* c) { for (int idx = 0; idx < n; ++idx) c[idx] = a[idx] + b[idx]; } // Vector sum in CUDA __global__ void vector_add(int n, const float* a, const float* b, float* c) { int idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx < n) c[idx] = a[idx] + b[idx]; } CUDA C++ PROGRAMMING
- 6. 6 ECOSYSTEM AmgX cuBLAS LIBRARIES COMPILER DIRECTIVES PROGRAMMING LANGUAGES x86
- 8. 8 APPLICATIONS: DEEP LEARNING INTERNET & CLOUD Image Classification Speech Recognition Language Translation Language Processing Sentiment Analysis Recommendation MEDICINE & BIOLOGY Cancer Cell Detection Diabetic Grading Drug Discovery MEDIA & ENTERTAINMENT Video Captioning Video Search Real Time Translation SECURITY & DEFENSE Face Detection Video Surveillance Satellite Imagery AUTONOMOUS MACHINES Pedestrian Detection Lane Tracking Recognize Traffic Sign
- 9. 9 GPU-ACCELERATED DEEP LEARNING TENSORFLOW CNTK TORCH CAFFE WATSON CHAINER THEANO MATCONVNET
- 11. 11 CHALLENGES A Typical Cluster Ubuntu 14.04 Drivers 352 4x Maxwell CentOS 7 Drivers 346 4x Kepler Ubuntu 16.04 Drivers 362 8x Pascal CUDA 7.5 CUDA 7.0 cuDNN 3 CUDA 7.5 cuDNN 4 CUDA 8.0 Patches
- 12. 12 PACKAGING DRIVER FILES? FROM ubuntu:14.04 RUN apt-get update && apt-get install --no-install-recommends -y gcc make libc-dev wget RUN wget http://us.download.nvidia.com/XFree86/Linux- x86_64/361.42/NVIDIA- Linux-x86_64-361.42.run RUN sh NVIDIA-Linux-x86_64-361.42.run --silent --no-kernel-module Never install the driver in the Dockerfile, not portable!
- 13. 13 BRINGING GPU SUPPORT TO DOCKER
- 14. 14 INTERNALS Docker Daemon NVIDIA Docker Plugin NVIDIA Drivers NVIDIA Docker Docker Registry 1 2 3 4 5 6 HTTP + SSH HTTP + UNIX LDCACHE + NVML HTTPS CUDA 8.0 CAFFE CUDA 7.5 TENSORFLOW HTTPS HTTPSDocker CLI
- 15. 15 DOCKERHUB IMAGES CUDA 7.0 runtime CUDA 7.5 runtime CUDA 8.0 runtime cuDNN v4 runtime CUDA 7.0 devel cuDNN v4 runtime CUDA 7.5 devel cuDNN v5 runtime CUDA 8.0 devel NVIDIA/Caffe 0.14.2 cuDNN v4 devel cuDNN v4 devel NVIDIA/Caffe 0.14.5 cuDNN v5 devel DIGITS 3.0 CNTK BVLC/Caffe TensorFlow DIGITS 3.3 Ubuntu 14.04 Ubuntu 16.04
- 16. 16 GPU APPLICATIONS WORKFLOW Research/Develop Test/Package Deploy Video Transcoding Image Processing Deep Learning HPC Visualization Container Based Applications GPU-Accelerated Data Center
- 18. THANK YOU