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Supermicro GPU Solutions Optimized for NVIDIA Nvlink
SuperServers Optimized For NVIDIA® Tesla® GPUs Maximizing Throughput and Scalability 16 Tesla® V100 GPUs With NVLink™ and NVSwitch™ Most Powerful Solution for Deep Learning Training • New Supermicro NVIDIA® HGX-2 based platform • 16 Tesla® V100 SXM3 GPUs (512GB total GPU memory) • 16 NICs for GPUDirect RDMA • 16 hot-swap NVMe drive bays • Fully configurable to order SYS-9029GP-TNVRT 8 Tesla® V100 GPUs With NVLink™ 4 Tesla® V100 GPUs With NVLink™ SYS-1029GQ-TVRT SYS-4029GP-TVRT www.supermicro.com/GPU March 2019 Maximum Acceleration for AI/DL Training Workloads PERFORMANCE: Highest Parallel peak performance with NVIDIA Tesla V100 GPUs THROUGHPUT: Best in class GPU-to-GPU bandwidth with a maximum speed of 300GB/s SCALABILITY: Designed for direct interconections between multiple GPU nodes FLEXIBILITY: PCI-E 3.0 x16 for low latency I/O expansion capacity & GPU Direct RDMA support DESIGN: Optimized GPU cooling for highest sustained parallel computing performance EFFICIENCY: Redundant Titanium Level power supplies & intelligent cooling control Model SYS-1029GQ-TVRT SYS-4029GP-TVRT • Dual Intel® Xeon® Scalable processors with 3 UPI up to • Dual Intel® Xeon® Scalable processors with 3 UPI up to 10.4GT/s CPU Support 10.4GT/s • Supports up to 205W TDP CPU • Supports up to 205W TDP CPU • 8 NVIDIA® Tesla® V100 GPUs • 4 NVIDIA Tesla V100 GPUs • NVIDIA® NVLink™ GPU Interconnect up to 300GB/s GPU Support • NVIDIA® NVLink™ GPU Interconnect up to 300GB/s • Optimized for GPUDirect RDMA • Optimized for GPUDirect RDMA • Independent CPU and GPU thermal zones -
Identificação De Textos Em Imagens CAPTCHA Utilizando Conceitos De
Identificação de Textos em Imagens CAPTCHA utilizando conceitos de Aprendizado de Máquina e Redes Neurais Convolucionais Relatório submetido à Universidade Federal de Santa Catarina como requisito para a aprovação da disciplina: DAS 5511: Projeto de Fim de Curso Murilo Rodegheri Mendes dos Santos Florianópolis, Julho de 2018 Identificação de Textos em Imagens CAPTCHA utilizando conceitos de Aprendizado de Máquina e Redes Neurais Convolucionais Murilo Rodegheri Mendes dos Santos Esta monografia foi julgada no contexto da disciplina DAS 5511: Projeto de Fim de Curso e aprovada na sua forma final pelo Curso de Engenharia de Controle e Automação Prof. Marcelo Ricardo Stemmer Banca Examinadora: André Carvalho Bittencourt Orientador na Empresa Prof. Marcelo Ricardo Stemmer Orientador no Curso Prof. Ricardo José Rabelo Responsável pela disciplina Flávio Gabriel Oliveira Barbosa, Avaliador Guilherme Espindola Winck, Debatedor Ricardo Carvalho Frantz do Amaral, Debatedor Agradecimentos Agradeço à minha mãe Terezinha Rodegheri, ao meu pai Orlisses Mendes dos Santos e ao meu irmão Camilo Rodegheri Mendes dos Santos que sempre estiveram ao meu lado, tanto nos momentos de alegria quanto nos momentos de dificuldades, sempre me deram apoio, conselhos, suporte e nunca duvidaram da minha capacidade de alcançar meus objetivos. Agradeço aos meus colegas Guilherme Cornelli, Leonardo Quaini, Matheus Ambrosi, Matheus Zardo, Roger Perin e Victor Petrassi por me acompanharem em toda a graduação, seja nas disciplinas, nos projetos, nas noites de estudo, nas atividades extracurriculares, nas festas, entre outros desafios enfrentados para chegar até aqui. Agradeço aos meus amigos de infância Cássio Schmidt, Daniel Lock, Gabriel Streit, Gabriel Cervo, Guilherme Trevisan, Lucas Nyland por proporcionarem momentos de alegria mesmo a distância na maior parte da caminhada da graduação. -
High Performance Computing and AI Solutions Portfolio
Brochure High Performance Computing and AI Solutions Portfolio Technology and expertise to help you accelerate discovery and innovation Discovery and innovation have always started with great minds Go ahead. dreaming big. As artificial intelligence (AI), high performance computing (HPC) and data analytics continue to converge and Dream big. evolve, they are fueling the next industrial revolution and the next quantum leap in human progress. And with the help of increasingly powerful technology, you can dream even bigger. Dell Technologies will be there every step of the way with the technology you need to power tomorrow’s discoveries and the expertise to bring it all together, today. 463 exabytes The convergence of HPC and AI is driven by data. The data‑driven age is dramatically reshaping industries and reinventing the future. As of data will be created each day by 20251 vast amounts of data pour in from increasingly diverse sources, leveraging that data is both critical and transformational. Whether you’re working to save lives, understand the universe, build better machines, neutralize financial risks or anticipate customer sentiment, 44X ROI data informs and drives decisions that impact the success of your organization — and Average return on investment (ROI) shapes the future of our world. for HPC2 AI, HPC and data analytics are technologies designed to unlock the value of your data. While they have long been treated as separate, the three technologies are converging as 83% of CIOs it becomes clear that analytics and AI are both big‑data problems that require the power, Say they are investing in AI scalable compute, networking and storage provided by HPC. -
Vikas Sindhwani Google May 17-19, 2016 2016 Summer School on Signal Processing and Machine Learning for Big Data
Real-time Learning and Inference on Emerging Mobile Systems Vikas Sindhwani Google May 17-19, 2016 2016 Summer School on Signal Processing and Machine Learning for Big Data Abstract: We are motivated by the challenge of enabling real-time "always-on" machine learning applications on emerging mobile platforms such as next-generation smartphones, wearable computers and consumer robotics systems. On-device models in such settings need to be highly compact, and need to support fast, low-power inference on specialized hardware. I will consider the problem of building small-footprint non- linear models based on kernel methods and deep learning techniques, for on-device deployments. Towards this end, I will give an overview of various techniques, and introduce new notions of parsimony rooted in the theory of structured matrices. Such structured matrices can be used to recycle Gaussian random vectors in order to build randomized feature maps in sub-linear time for approximating various kernel functions. In the deep learning context, low-displacement structured parameter matrices admit fast function and gradient evaluation. I will discuss how such compact nonlinear transforms span a rich range of parameter sharing configurations whose statistical modeling capacity can be explicitly tuned along a continuum from structured to unstructured. I will present empirical results on mobile speech recognition problems, and image classification tasks. I will also briefly present some basics of TensorFlow: a open-source library for numerical computations on data flow graphs. Tensorflow enables large-scale distributed training of complex machine learning models, and their rapid deployment on mobile devices. Bio: Vikas Sindhwani is Research Scientist in the Google Brain team in New York City. -
Warned That Big, Messy AI Systems Would Generate Racist, Unfair Results
JULY/AUG 2021 | DON’T BE EVIL warned that big, messy AI systems would generate racist, unfair results. Google brought her in to prevent that fate. Then it forced her out. Can Big Tech handle criticism from within? BY TOM SIMONITE NEW ROUTES TO NEW CUSTOMERS E-COMMERCE AT THE SPEED OF NOW Business is changing and the United States Postal Service is changing with it. We’re offering e-commerce solutions from fast, reliable shipping to returns right from any address in America. Find out more at usps.com/newroutes. Scheduled delivery date and time depend on origin, destination and Post Office™ acceptance time. Some restrictions apply. For additional information, visit the Postage Calculator at http://postcalc.usps.com. For details on availability, visit usps.com/pickup. The Okta Identity Cloud. Protecting people everywhere. Modern identity. For one patient or one billion. © 2021 Okta, Inc. and its affiliates. All rights reserved. ELECTRIC WORD WIRED 29.07 I OFTEN FELT LIKE A SORT OF FACELESS, NAMELESS, NOT-EVEN- A-PERSON. LIKE THE GPS UNIT OR SOME- THING. → 38 ART / WINSTON STRUYE 0 0 3 FEATURES WIRED 29.07 “THIS IS AN EXTINCTION EVENT” In 2011, Chinese spies stole cybersecurity’s crown jewels. The full story can finally be told. by Andy Greenberg FATAL FLAW How researchers discovered a teensy, decades-old screwup that helped Covid kill. by Megan Molteni SPIN DOCTOR Mo Pinel’s bowling balls harnessed the power of physics—and changed the sport forever. by Brendan I. Koerner HAIL, MALCOLM Inside Roblox, players built a fascist Roman Empire. -
Gmail Smart Compose: Real-Time Assisted Writing
Gmail Smart Compose: Real-Time Assisted Writing Mia Xu Chen∗ Benjamin N Lee∗ Gagan Bansal∗ [email protected] [email protected] [email protected] Google Google Google Yuan Cao Shuyuan Zhang Justin Lu [email protected] [email protected] [email protected] Google Google Google Jackie Tsay Yinan Wang Andrew M. Dai [email protected] [email protected] [email protected] Google Google Google Zhifeng Chen Timothy Sohn Yonghui Wu [email protected] [email protected] [email protected] Google Google Google Figure 1: Smart Compose Screenshot. ABSTRACT our proposed system design and deployment approach. This system In this paper, we present Smart Compose, a novel system for gener- is currently being served in Gmail. ating interactive, real-time suggestions in Gmail that assists users in writing mails by reducing repetitive typing. In the design and KEYWORDS deployment of such a large-scale and complicated system, we faced Smart Compose, language model, assisted writing, large-scale serv- several challenges including model selection, performance eval- ing uation, serving and other practical issues. At the core of Smart ACM Reference Format: arXiv:1906.00080v1 [cs.CL] 17 May 2019 Compose is a large-scale neural language model. We leveraged Mia Xu Chen, Benjamin N Lee, Gagan Bansal, Yuan Cao, Shuyuan Zhang, state-of-the-art machine learning techniques for language model Justin Lu, Jackie Tsay, Yinan Wang, Andrew M. Dai, Zhifeng Chen, Timothy training which enabled high-quality suggestion prediction, and Sohn, and Yonghui Wu. 2019. Gmail Smart Compose: Real-Time Assisted constructed novel serving infrastructure for high-throughput and Writing. In The 25th ACM SIGKDD Conference on Knowledge Discovery and real-time inference. -
The Machine Learning Journey with Google
The Machine Learning Journey with Google Google Cloud Professional Services The information, scoping, and pricing data in this presentation is for evaluation/discussion purposes only and is non-binding. For reference purposes, Google's standard terms and conditions for professional services are located at: https://enterprise.google.com/terms/professional-services.html. 1 What is machine learning? 2 Why all the attention now? Topics How Google can support you inyour 3 journey to ML 4 Where to from here? © 2019 Google LLC. All rights reserved. What is machine0 learning? 1 Machine learning is... a branch of artificial intelligence a way to solve problems without explicitly codifying the solution a way to build systems that improve themselves over time © 2019 Google LLC. All rights reserved. Key trends in artificial intelligence and machine learning #1 #2 #3 #4 Democratization AI and ML will be core Specialized hardware Automation of ML of AI and ML competencies of for deep learning (e.g., MIT’s Data enterprises (CPUs → GPUs → TPUs) Science Machine & Google’s AutoML) #5 #6 #7 Commoditization of Cloud as the platform ML set to transform deep learning for AI and ML banking and (e.g., TensorFlow) financial services © 2019 Google LLC. All rights reserved. Use of machine learning is rapidly accelerating Used across products © 2019 Google LLC. All rights reserved. Google Translate © 2019 Google LLC. All rights reserved. Why all the attention0 now? 2 Machine learning allows us to solve problems without codifying the solution. © 2019 Google LLC. All rights reserved. San Francisco New York © 2019 Google LLC. All rights reserved. -
Google's 'Project Nightingale' Gathers Personal Health Data
Google's 'Project Nightingale' Gathers Personal Health Data on Millions of Americans; Search giant is amassing health records from Ascension facilities in 21 states; patients not yet informed Copeland, Rob . Wall Street Journal (Online) ; New York, N.Y. [New York, N.Y]11 Nov 2019. ProQuest document link FULL TEXT Google is engaged with one of the U.S.'s largest health-care systems on a project to collect and crunch the detailed personal-health information of millions of people across 21 states. The initiative, code-named "Project Nightingale," appears to be the biggest effort yet by a Silicon Valley giant to gain a toehold in the health-care industry through the handling of patients' medical data. Amazon.com Inc., Apple Inc. and Microsoft Corp. are also aggressively pushing into health care, though they haven't yet struck deals of this scope. Share Your Thoughts Do you trust Google with your personal health data? Why or why not? Join the conversation below. Google began Project Nightingale in secret last year with St. Louis-based Ascension, a Catholic chain of 2,600 hospitals, doctors' offices and other facilities, with the data sharing accelerating since summer, according to internal documents. The data involved in the initiative encompasses lab results, doctor diagnoses and hospitalization records, among other categories, and amounts to a complete health history, including patient names and dates of birth. Neither patients nor doctors have been notified. At least 150 Google employees already have access to much of the data on tens of millions of patients, according to a person familiar with the matter and the documents. -
NVIDIA Gpudirect RDMA (GDR) Host Memory Host Memory • Pipeline Through Host for Large Msg 2 IB IB 4 2
Efficient and Scalable Communication Middleware for Emerging Dense-GPU Clusters Ching-Hsiang Chu Advisor: Dhabaleswar K. Panda Network Based Computing Lab Department of Computer Science and Engineering The Ohio State University, Columbus, OH Outline • Introduction • Problem Statement • Detailed Description and Results • Broader Impact on the HPC Community • Expected Contributions Network Based Computing Laboratory SC 19 Doctoral Showcase 2 Trends in Modern HPC Architecture: Heterogeneous Multi/ Many-core High Performance Interconnects Accelerators / Coprocessors SSD, NVMe-SSD, Processors InfiniBand, Omni-Path, EFA high compute density, NVRAM <1usec latency, 100Gbps+ Bandwidth high performance/watt Node local storage • Multi-core/many-core technologies • High Performance Storage and Compute devices • High Performance Interconnects • Variety of programming models (MPI, PGAS, MPI+X) #1 Summit #2 Sierra (17,280 GPUs) #8 ABCI #22 DGX SuperPOD (27,648 GPUs) #10 Lassen (2,664 GPUs) (4,352 GPUs) (1,536 GPUs) Network Based Computing Laboratory SC 19 Doctoral Showcase 3 Trends in Modern Large-scale Dense-GPU Systems • Scale-up (up to 150 GB/s) • Scale-out (up to 25 GB/s) – PCIe, NVLink/NVSwitch – InfiniBand, Omni-path, Ethernet – Infinity Fabric, Gen-Z, CXL – Cray Slingshot Network Based Computing Laboratory SC 19 Doctoral Showcase 4 GPU-enabled HPC Applications Lattice Quantum Chromodynamics Weather Simulation Wave propagation simulation Fuhrer O, Osuna C, Lapillonne X, Gysi T, Bianco M, Schulthess T. Towards GPU-accelerated operational weather -
Dell EMC Poweredge C4140 Technical Guide
Dell EMC PowerEdge C4140 Technical Guide Regulatory Model: E53S Series Regulatory Type: E53S001 Notes, cautions, and warnings NOTE: A NOTE indicates important information that helps you make better use of your product. CAUTION: A CAUTION indicates either potential damage to hardware or loss of data and tells you how to avoid the problem. WARNING: A WARNING indicates a potential for property damage, personal injury, or death. © 2017 - 2019 Dell Inc. or its subsidiaries. All rights reserved. Dell, EMC, and other trademarks are trademarks of Dell Inc. or its subsidiaries. Other trademarks may be trademarks of their respective owners. 2019 - 09 Rev. A00 Contents 1 System overview ......................................................................................................................... 5 Introduction............................................................................................................................................................................ 5 New technologies.................................................................................................................................................................. 5 2 System features...........................................................................................................................7 Specifications......................................................................................................................................................................... 7 Product comparison............................................................................................................................................................. -
HPE Apollo 6500 Gen10 System Overview
QuickSpecs HPE Apollo 6500 Gen10 System Overview HPE Apollo 6500 Gen10 System The ability of computers to autonomously learn, predict, and adapt using massive datasets is driving innovation and competitive advantage across many industries and applications. The HPE Apollo 6500 Gen10 System is an ideal HPC and Deep Learning platform providing unprecedented performance with industry leading GPUs, fast GPU interconnect, high bandwidth fabric and a configurable GPU topology to match your workloads. The system with rock-solid RAS features (reliable, available, secure) includes up to eight high power GPUs per server tray (node), NVLink 2.0 for fast GPU-to-GPU communication, Intel® Xeon® Scalable Processors support, choice of up to four high-speed / low latency fabric adapters, and the ability to optimize your configurations to match your workload and choice of GPU. And while the HPE Apollo 6500 Gen10 System is ideal for deep learning workloads, the system is suitable for complex high performance computing workloads such as simulation and modeling. Eight GPU per server for faster and more economical deep learning system training compared to more servers with fewer GPU each. Keep your researchers productive as they iterate on a model more rapidly for a better solution, in less time. Now available with NVLink 2.0 to connect GPUs at up to 300 GB/s for the world’s most powerful computing servers. HPC and AI models that would consume days or weeks can now be trained in a few hours or minutes. Front View –with two HPE DL38X Gen10 Premium 6 SFF SAS/SATA + 2 NVMe drive cages shown 1. -
The Computer That Could Be Smarter Than Us Cognitive Computing
outthink limits The Computer that could be smarter than us Cognitive Computing Ingolf Wittmann Technical Director Leader of HPC Kinetica - Unstructured Databases What is Kinetica? Unparalleled acceleration Kinetica’s in-memory database powered by graphics processing units (GPUs) was built from the ground up to deliver truly real-time insights on data in 2.5x motion: orders of magnitude faster Bandwidth performance (potentially 100, 1000X) at 100100 ticktick QueryQuery Time:Time: CompetingCompeting SystemSystem PCI-EPCI-E x16x16 3.03.0 10% to 25% of the cost of traditional An industry first — POWER8 with data platforms. NVIDIA NVLink delivers 2.5X the Calculation* bandwidth to GPU accelerators, Data Transfer allowing you to experience Kinetica at 73 ticks 27 ticks the speed it was intended compared to x86 based systems. What are the Key Markets? 65% Reduction • Retail: Inventory Mgt, BI, Apps, Big Data tools, HPA Real-time results • Distribution / Logistics : Supply Chain Data Transfer Calculation* Mgt 26 ticks 14 ticks • Financial Services : Fraud Detection, AML 4040 ticktick QueryQuery Time:Time: S822LCS822LC forfor HPC,HPC, NVLinkNVLink 10x • Ad-Tech : More Targeted Marketing * Includes non-overlapping: CPU, GPU, and idle times. Performance • IoT : End Point Management, RFID With the unique capabilities of Tesla 65% reduction in data transfer time (3X improvement) in for P100 + POWER8, Kinetica has 2.4x POWER8 with NVLink the performance of competing Kinetica GPU-accelerated DB PCIe x16 3.0/x86 System System systems enabling you to analyze and • Less data-induced latency in all applications Xeon E5-2640 v4 Power Systems S822LC visualize large datasets in • with 4 Tesla K80s : Unique to POWER8 with NVLink with 4 Tesla P100s: milliseconds vs.