The NVIDIA Tesla K80 GPU accelerator delivers significantly faster performance than CPUs and previous GPU models for demanding workloads. It features two GPUs with up to 2.91 TFLOPS double precision and 8.74 TFLOPS single precision performance each, 24GB of total memory, and other advanced capabilities. Benchmark results show the K80 providing up to 2.2x faster performance than the Tesla K20X, up to 2.5x faster than the Tesla K10, and up to 10x faster than CPUs for real-world applications in scientific computing, data analytics, and other fields.
The RAPIDS suite of software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
NF5288M5(AGX-2)
NF5288M5 is a 2U two-sockets rack server consists 8 NVLink GPU or 8PCIe GPU which is specially designed for AI/HPC
• Highest Density: 8 FHFL GPU get emerged in the limited space of 2U chassis
• Highest Performance: support the latest optimized NVLink2.0 with fully connection topology, up to 960 Tensor FLOPs and 376 TOPS on INT8.
• Highest Flexibility: abundant topology for GPU to meet different work load, free to choose different GPUs.
Application scenarios:
• AI field----Intelligent Security, Intelligent Traffic, Intelligent Finance, Intelligent Medical and Intelligent Manufacture that need data analysis and training
• HPC------High performance clusters pursuing ultra-high performances, such as rendering operation, CAD and CAE.
• Video acceleration------Complex and diverse video processing that need paralleled operation for government, education.
The RAPIDS suite of software libraries gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
NF5288M5(AGX-2)
NF5288M5 is a 2U two-sockets rack server consists 8 NVLink GPU or 8PCIe GPU which is specially designed for AI/HPC
• Highest Density: 8 FHFL GPU get emerged in the limited space of 2U chassis
• Highest Performance: support the latest optimized NVLink2.0 with fully connection topology, up to 960 Tensor FLOPs and 376 TOPS on INT8.
• Highest Flexibility: abundant topology for GPU to meet different work load, free to choose different GPUs.
Application scenarios:
• AI field----Intelligent Security, Intelligent Traffic, Intelligent Finance, Intelligent Medical and Intelligent Manufacture that need data analysis and training
• HPC------High performance clusters pursuing ultra-high performances, such as rendering operation, CAD and CAE.
• Video acceleration------Complex and diverse video processing that need paralleled operation for government, education.
At the 2017 GPU Technology Conference in Silicon Valley, NVIDIA CEO Jensen Huang introduced a lineup of new Volta-based AI supercomputers including a powerful new version of our DGX-1 deep learning appliance; announced the Isaac robot-training simulator; unveiled the NVIDIA GPU Cloud platform, giving developers access to the latest, optimized deep learning frameworks; and unveiled a partnership with Toyota to help build a new generation of autonomous vehicles.
Python и программирование GPU (Ивашкевич Глеб)IT-Доминанта
Ивашкевич Глеб - HPC software developer / Gero / Украина, Харьков
Графические процессоры становятся частью стандартного инструментария в высокопроизводительных вычислениях. Одновременно появляются новые и совершенствуются уже существующие программные средства. Мы поговорим об архитектуре графических процессоров Nvidia и о том, как с ними работать из Python.
http://www.it-sobytie.ru/events/2040
GPUIterator: Bridging the Gap between Chapel and GPU PlatformsAkihiro Hayashi
The ACM SIGPLAN 6th Annual Chapel Implementers and Users Workshop (CHIUW2019) co-located with PLDI 2019 / ACM FCRC 2019.
PGAS (Partitioned Global Address Space) programming models were originally designed to facilitate productive parallel programming at both the intra-node and inter-node levels in homogeneous parallel machines. However, there is a growing need to support accelerators, especially GPU accelerators, in heterogeneous nodes in a cluster. Among high-level PGAS programming languages, Chapel is well suited for this task due to its use of locales and domains to help abstract away low-level details of data and compute mappings for different compute nodes, as well as for different processing units (CPU vs. GPU) within a node. In this paper, we address some of the key limitations of past approaches on mapping Chapel on to GPUs as follows. First, we introduce a Chapel module, GPUIterator, which is a portable programming interface that supports GPU execution of a Chapel forall loop. This module makes it possible for Chapel programmers to easily use hand-tuned native GPU programs/libraries, which is an important requirement in practice since there is still a big performance gap between compiler-generated GPU code and hand-turned GPU code; hand-optimization of CPU-GPU data transfers is also an important contributor to this performance gap. Second, though Chapel programs are regularly executed on multi-node clusters, past work on GPU enablement of Chapel programs mainly focused on single-node execution. In contrast, our work supports execution across multiple CPU+GPU nodes by accepting Chapel's distributed domains. Third, our approach supports hybrid execution of a Chapel parallel (forall) loop across both a GPU and CPU cores, which is beneficial for specific platforms. Our preliminary performance evaluations show that the use of the GPUIterator is a promising approach for Chapel programmers to easily utilize a single or multiple CPU+GPU node(s) while maintaining portability.
2015年9月18日開催 GTC Japan 2015 講演資料
エヌビディア合同会社
エンタープライズプロダクト事業部 シニアソリューションアーキテクト Jeremy Main
A walk through of the techniques to monitor existing workstation workloads to create data-driven estimates of recommended user density levels based on the GPU requirements, frame buffer utilization and other factors as well as methods to confirm GPU resource utilization to ensure excellent performing NVIDIA GRID vGPU enabled virtual machines.
re:Invent 2019 BPF Performance Analysis at NetflixBrendan Gregg
Talk by Brendan Gregg at AWS re:Invent 2019. Abstract: "Extended BPF (eBPF) is an open source Linux technology that powers a whole new class of software: mini programs that run on events. Among its many uses, BPF can be used to create powerful performance analysis tools capable of analyzing everything: CPUs, memory, disks, file systems, networking, languages, applications, and more. In this session, Netflix's Brendan Gregg tours BPF tracing capabilities, including many new open source performance analysis tools he developed for his new book "BPF Performance Tools: Linux System and Application Observability." The talk includes examples of using these tools in the Amazon EC2 cloud."
AMD Bridges the X86 and ARM Ecosystems for the Data Center AMD
Presentation by Lisa Su, senior vice president and general manager, Global Business Units, AMD regarding AMD’s announcement that it will design and build 64-bit ARM technology-based processors.
At the 2017 GPU Technology Conference in Silicon Valley, NVIDIA CEO Jensen Huang introduced a lineup of new Volta-based AI supercomputers including a powerful new version of our DGX-1 deep learning appliance; announced the Isaac robot-training simulator; unveiled the NVIDIA GPU Cloud platform, giving developers access to the latest, optimized deep learning frameworks; and unveiled a partnership with Toyota to help build a new generation of autonomous vehicles.
Python и программирование GPU (Ивашкевич Глеб)IT-Доминанта
Ивашкевич Глеб - HPC software developer / Gero / Украина, Харьков
Графические процессоры становятся частью стандартного инструментария в высокопроизводительных вычислениях. Одновременно появляются новые и совершенствуются уже существующие программные средства. Мы поговорим об архитектуре графических процессоров Nvidia и о том, как с ними работать из Python.
http://www.it-sobytie.ru/events/2040
GPUIterator: Bridging the Gap between Chapel and GPU PlatformsAkihiro Hayashi
The ACM SIGPLAN 6th Annual Chapel Implementers and Users Workshop (CHIUW2019) co-located with PLDI 2019 / ACM FCRC 2019.
PGAS (Partitioned Global Address Space) programming models were originally designed to facilitate productive parallel programming at both the intra-node and inter-node levels in homogeneous parallel machines. However, there is a growing need to support accelerators, especially GPU accelerators, in heterogeneous nodes in a cluster. Among high-level PGAS programming languages, Chapel is well suited for this task due to its use of locales and domains to help abstract away low-level details of data and compute mappings for different compute nodes, as well as for different processing units (CPU vs. GPU) within a node. In this paper, we address some of the key limitations of past approaches on mapping Chapel on to GPUs as follows. First, we introduce a Chapel module, GPUIterator, which is a portable programming interface that supports GPU execution of a Chapel forall loop. This module makes it possible for Chapel programmers to easily use hand-tuned native GPU programs/libraries, which is an important requirement in practice since there is still a big performance gap between compiler-generated GPU code and hand-turned GPU code; hand-optimization of CPU-GPU data transfers is also an important contributor to this performance gap. Second, though Chapel programs are regularly executed on multi-node clusters, past work on GPU enablement of Chapel programs mainly focused on single-node execution. In contrast, our work supports execution across multiple CPU+GPU nodes by accepting Chapel's distributed domains. Third, our approach supports hybrid execution of a Chapel parallel (forall) loop across both a GPU and CPU cores, which is beneficial for specific platforms. Our preliminary performance evaluations show that the use of the GPUIterator is a promising approach for Chapel programmers to easily utilize a single or multiple CPU+GPU node(s) while maintaining portability.
2015年9月18日開催 GTC Japan 2015 講演資料
エヌビディア合同会社
エンタープライズプロダクト事業部 シニアソリューションアーキテクト Jeremy Main
A walk through of the techniques to monitor existing workstation workloads to create data-driven estimates of recommended user density levels based on the GPU requirements, frame buffer utilization and other factors as well as methods to confirm GPU resource utilization to ensure excellent performing NVIDIA GRID vGPU enabled virtual machines.
re:Invent 2019 BPF Performance Analysis at NetflixBrendan Gregg
Talk by Brendan Gregg at AWS re:Invent 2019. Abstract: "Extended BPF (eBPF) is an open source Linux technology that powers a whole new class of software: mini programs that run on events. Among its many uses, BPF can be used to create powerful performance analysis tools capable of analyzing everything: CPUs, memory, disks, file systems, networking, languages, applications, and more. In this session, Netflix's Brendan Gregg tours BPF tracing capabilities, including many new open source performance analysis tools he developed for his new book "BPF Performance Tools: Linux System and Application Observability." The talk includes examples of using these tools in the Amazon EC2 cloud."
AMD Bridges the X86 and ARM Ecosystems for the Data Center AMD
Presentation by Lisa Su, senior vice president and general manager, Global Business Units, AMD regarding AMD’s announcement that it will design and build 64-bit ARM technology-based processors.
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
1) NVIDIA-Iguazio Accelerated Solutions for Deep Learning and Machine Learning (30 mins):
About the speaker:
Dr. Gabriel Noaje, Senior Solutions Architect, NVIDIA
http://bit.ly/GabrielNoaje
2) GPUs in Data Science Pipelines ( 30 mins)
- GPU as a Service for enterprise AI
- A short demo on the usage of GPUs for model training and model inferencing within a data science workflow
About the speaker:
Anant Gandhi, Solutions Engineer, Iguazio Singapore. https://www.linkedin.com/in/anant-gandhi-b5447614/
Axel Koehler from Nvidia presented this deck at the 2016 HPC Advisory Council Switzerland Conference.
“Accelerated computing is transforming the data center that delivers unprecedented through- put, enabling new discoveries and services for end users. This talk will give an overview about the NVIDIA Tesla accelerated computing platform including the latest developments in hardware and software. In addition it will be shown how deep learning on GPUs is changing how we use computers to understand data.”
In related news, the GPU Technology Conference takes place April 4-7 in Silicon Valley.
Watch the video presentation: http://insidehpc.com/2016/03/tesla-accelerated-computing/
See more talks in the Swiss Conference Video Gallery:
http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter:
http://insidehpc.com/newsletter
Nvidia Deep Learning Solutions - Alex SabatierSri Ambati
Alex Sabatier from Nvidia talks about the future of Deep Learning from an chipmaker perspective
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
NVIDIA CEO Jensen Huang Presentation at Supercomputing 2019NVIDIA
Broadening support for GPU-accelerated supercomputing to a fast-growing new platform, NVIDIA founder and CEO Jensen Huang introduced a reference design for building GPU-accelerated Arm servers, with wide industry backing.
This is a presentation I presented at NVIDIA AI Conference in Korea. It's about building the largest GPU - DGX-2, the most powerful supercomputer in one node.
NVIDIA GPUs Power HPC & AI Workloads in Cloud with Univainside-BigData.com
In this deck from the Univa Breakfast Briefing at ISC 2018, Duncan Poole from NVIDIA describes how the company is accelerating HPC in the Cloud.
Learn more: https://www.nvidia.com/en-us/data-center/dgx-systems/
and
http://univa.com
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Today’s groundbreaking scientific discoveries are taking place in HPC data centers. Using containers, researchers and scientists gain the flexibility to run HPC application containers on NVIDIA Volta-powered systems including Quadro-powered workstations, NVIDIA DGX Systems, and HPC clusters.
Backend.AI Technical Introduction (19.09 / 2019 Autumn)Lablup Inc.
This slide introduces technical specs and details about Backend.AI 19.09.
* On-premise clustering / container orchestration / scaling on cloud
* Container-level fractional GPU technology to use one GPU as many GPUs on many containers at the same time.
* NVidia GPU Cloud integrations
* Enterprise features
Harnessing the virtual realm for successful real world artificial intelligenceAlison B. Lowndes
Artificial Intelligence is impacting all areas of society, from healthcare and transportation to smart cities and energy. How NVIDIA invests both in internal pure research and accelerated computation to enable its diverse customer base, across gaming & extended reality, graphics, AI, robotics, simulation, high performance scientific computing, healthcare & more. You will be introduced to the GPU computing platform & shown real world successfully deployed applications as well as a glimpse into the current state of the art across academia, enterprise and startups.
Webinar: NVIDIA JETSON – A Inteligência Artificial na palma de sua mãoEmbarcados
Objetivo do Webinar: Venha saber como a plataforma NVIDIA Jetson e suas ferramentas habilitam você a desenvolver e implantar robôs, drones, aplicativos de IVA e outras máquinas autônomas com tecnologia AI que pensam por conta própria.
Apoio: Arrow e NVIDIA.
Convidado: Marcel Saraiva
Gerente de Contas Enterprise da NVIDIA, executivo com 20 anos de expereincia no mercado de TI, teve na sua carreia passagens pela SGI (Silicon Graphics), Intel e Scansource. Engenheiro eletrico formado pela FEI, com pós-graduação em Marketing pela FAAP e MBA em Gestão Empresarial pela FGV.
Link para o Webinar: https://www.embarcados.com.br/webinars/nvidia-jetson-a-inteligencia-artificial-na-palma-de-sua-mao/
Supercomputing has swept rapidly from the far edges of science to the heart of our everyday lives. And propelling it forward – bringing it into the mobile phone already in your pocket and the car in your driveway – is GPU acceleration, NVIDIA CEO Jen-Hsun Huang told a packed house at a rollicking event kicking off this week’s SC15 annual supercomputing show in Austin. The event draws 10,000 researchers, national lab directors and others from around the world.
Jetson AGX Xavier and the New Era of Autonomous MachinesDustin Franklin
Deep-dive on NVIDIA Jetson AGX Xavier, designed to help you deploy advanced AI onboard robots, drones, and other autonomous machines. View the webinar here: https://bit.ly/2BWVWv1
2 Sessione - Macchine virtuali per la scalabilità di calcolo per velocizzare ...Jürgen Ambrosi
La Fondazione CRUI e Microsoft organizzano un ciclo di webinar nell'ambito dell’Accordo Quadro Education Transformation Agreement. L’iniziativa si prefigge lo scopo di approcciare temi specifici della ricerca attraverso le tecnologie Microsoft più avanzate già a disposizione delle Università e degli Enti di Ricerca aderenti l’Accordo.
In questo secondo appuntamento verrà spiegato come la piattaforma Cloud Microsoft Azure può venire incontro all'esigenza di scalabilità, abbattendo i costi e abbassando i tempi di esecuzione nel mondo del calcolo parallelo. Nella sessione verranno utilizzati strumenti presenti nel cloud e software più comuni nell'ambito della ricerca
Teknologjia më e re e disqeve solidë me nande, me ndërfaqe PCI-express për një gjerësi transmetimi të lartë të të dhënave, me shpejtësi deri në 2.8GB/s në lexim dhe 1.5GB/s në shkrim, madhësi fizike e përshtatur për laptop, mini-pc, micro server-a, thinclient dhe workstations me adaptor përshtatës për bus PCI-e.
IMPRO për Mikrofinancë - Një aplikacion intuitiv dhe gjithëpërfshirës, që thjeshtëzon menaxhimin e klientëve tuaj, transaksioneve, riskut dhe portofolit, në kohë reale, në një databazë të qendërzuar dhe nëpërmjet web-it, kudo dhe kurdo! | Informohu: +355664074040 | marketing@commprog.com |
Produkte "Software": Paketa e zgjidhjeve SharePoint, mundësuar nga Communication Progress | Kjo paketë mundëson, midis të tjerash, krijimin e një Sistemi për Menaxhimin e Kontentit (CMS) të një sipërmarrjeje | Përshtatjen e metodave moderne për Menaxhimin e
Dokumentacionit (DM) dhe integrim me sisteme të arkivimit
elektronik (e-Archive) | Trajtimin e proceseve komplekse dhe ndër-departamentale, etj. Informohu: +355664074040 | marketing@commprog.com |
Produkte "Software": IMPRO për Financë-Kontabilitet | Menaxhoni operacionet financiare dhe kontabël në Financë-Kontabilitet nëpërmjet një programi unik | Autentik nga Communication Progress | Informohu: +355664074040 | marketing@commprog.com |
Zgjidhje inovative dhe mjaft e thjeshtë nga IMPRO| Për të qendërzuar dhe menaxhuar lehtësisht ID-të e vizitorëve në institucionin tuaj! | Sistem i qendërzuar skanimi ID (Pasaportë, Patentë, Kartë Identiteti)
Software per menaxhim te proceseceve te punes ne fushen e transportit: Menaxhimi i Transportit (Cikli i Ofertave Transport Tokesor, Magazine, Ajror); Cikli i Praktikes; Administrimi i Blerjeve; Menaxhimi i Magazines (doganore + te thjeshte); Menaxhimi i Burimeve Njerezore; Administrimi i Flotes se Mjeteve | Kontakt: +355664074040
Profil Communication Progress | Kompani ne tregun e Teknologjise se Informacionit dhe Komunikimit (TIK). Integratore dhe zhvilluese software | Zgjidhje per infrastruktura IT, Komunikim, Software dhe produkte e platforma edukative.
IMPRO për Institucione Financiare është një aplikacion web, i cili lehtëson menaxhimin e klientëve, transaksioneve, riskut dhe portofolit tuaj, në kohë reale, në një databasë të qendërzuar, kudo dhe kurdo | Autentik nga Communication Progress | Kontakt: marketing@commprog.com
Software Development Outsourcing Profile -Communication Progress Ltd (1998)Communication Progress
Benefits of Software Outsourcing to Albania! Help your business' growth and trust in Communication Progress Ltd competences with 18 years experience. Contact us now at cp@commprog.com or visit www.commprog.com to get a customized offer!
Gentian Likaj is the founder and CEO of Communication Progress. He started his carrier in 1995 as a software expert in Alcatel Italy for Central Offices exchanges. He developed his technical skills during the preparation of software package and database for the Albanian telecommunication network. Gentian together with two other system engineers colleagues established in 1998 the company “Communication Progress”, staffed it with the best available professional staff, capable of developing, and implementing demanding networking, software applications and high technology projects. Gentian has led many projects, initiated a number of national programs and formed numerous partnerships in the Balkans. As such, Communication Progress has developed partnership relations with many of the world’s leading technology companies such as Alcatel-Lucent, Iskratel, Nokia Siemens Networks, Microsoft, Promethean, Supermicro, Polycom, Optoma, Transition Networks, Fortinet, OpenERP (Odoo) and many others. The company has continuously broadened its service portfolio in the data and private branch exchange networks. Today, Communication Progress has over 40 employees, where over 30 are engineers and software developers. Mr. Likaj holds a Bachelor degree in Telecommunications, and counts numerous professional certificates on data networks, managed networks, telecommunication networks, applications and business management. - See more at: http://startupgrind.com/event/startup-grind-tirana-presents-gentian-likaj-communication-progress/#sthash.rnzwBZn6.dpuf
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1. NVIDIA®
TESLA®
K80
THE WORLD’S FASTEST
GPU ACCELERATOR
Experience 10x faster application performance.
Accelerate your most demanding single and double precision workloads in scientific
computing, seismic processing, and data analytics applications by upgrading to the NVIDIA
Tesla K80 dual-GPU accelerator. It delivers up to 2.2x faster performance than the Tesla K20X,
up to 2.5x faster performance than the Tesla K10, and up to 10x faster performance than CPUs
on real-world applications.
The Tesla K80 features:
> Up to 2.91 Teraflops of double precision performance with NVIDIA GPU Boost™
> Up to 8.74 Terfalops of single precision performance with NVIDIA GPU Boost
> 24 GB of GDDR5 memory (12 GB per GPU)
> 480 GB/sec memory bandwidth per board
> 2x application throughput with the two onboard GPUs
As the latest addition to the Tesla Accelerated Computing Platform, the Tesla K80 leverages a
rich software, hardware, and support eco-system to accelerate the most demanding workloads
in the datacenter.
K80 Features
New: GPU Boost — Dynamically
scales clocks, based on
characteristics of the workload, for
maximum application performance.
This ensures that each application
runs at the highest clocks while
remaining within the power and
thermal envelope.
New: Double shared memory and
register file — Increase effective
bandwidth with 2x shared memory
and 2x register file compared to the
Tesla K20X and K10.
New: Zero-power Idle — Increase
data center energy efficiency by
powering down idle GPUs when
running legacy non-accelerated
workloads.
Multi-GPU Hyper-Q — Efficiently and
easily schedule MPI ranks across
GPUs, increasing GPU utilization and
ease of programming.
System Monitoring — Manage GPU
processors in computing systems
with widely used cluster/Grid
solutions.
Memory Protection — Error
Correcting Codes (ECC) memory
protection for both internal memories
and external GDDR5 DRAM
meets a critical requirement for
computing accuracy and reliability in
supercomputing and data centers.
FASTER
Up to 2.2x Faster than Tesla K20X, Up to 2.5x Faster than Tesla K10
Up to 10x Faster than CPU1
SMARTER
BIGGER
24 GB Memory for High
Performance Data Analytics
Intelligently Maximize
Performance and Efficiency
1. E5-2697v2 @ 2.70 GHz | 2. Amber SPFP-Nucleosome. CPU: E5-2697v2 @ 2.70 GHz. GPU: Single K20X or K80 with GPU Boost enabled | 3. RTM ISO 3D 16th
order. CPU: Dual socket E5-2697v2 @ 2.70 GHz. GPU: Single K10 or K80 with GPU Boost enabled
0
1
2
3
4
5
6
K80K20XCPU
ns/day
Life Science: AMBER2
Benchmark
0
5
10
15
20
K80K10CPU
GC/s
Oil & Gas: RTM3
Benchmark
Upgrade your GPU
The Tesla K80 accelerator delivers more than 2x application speed-up compared to the previous
generation of accelerators, and up to 10x faster performance compared to CPUs. With exclusive
features like 24 GB of GDDR5 memory, 480 GB/s memory bandwidth, and improved GPU Boost
technology, the Tesla K80 delivers the computational horsepower that allows you to crunch through
petabytes of data and run simulations faster than ever before.