This document discusses optimizing Hadoop workloads through hardware and software configuration. Key recommendations include using dual-socket servers with the latest Intel Xeon processors for better performance and scalability. Sufficient memory, SSDs, and an optimized Linux distribution can also improve throughput and reduce costs. Proper configuration of Hadoop masters, slaves, and middleware helps ensure workload demands are met efficiently.
IT@Intel: Creating Smart Spaces with All-in-OnesIT@Intel
Intel IT explains how they used all-in-one devices as collaboration tools both in the office as well as lab spaces. By providing efficient collaboration solutions, we help our employees be more productive and have greater job satisfaction.
Unlock Hidden Potential through Big Data and AnalyticsIT@Intel
Kim Stevenson, Intel's Chief Information Officer, discussed how big data and analytics are driving innovation through increased data volumes, lower computing costs, and new tools. Big data allows for improved customer experiences, more intelligent systems, and richer data analysis. Corporations are using analytics to increase efficiency, assist campaigns, and reduce costs. Intel's data platform aims to enable massive computing power, build an open ecosystem, and reduce complexity to fuel data-driven innovation. Stevenson highlighted opportunities from traffic optimization to personalized healthcare and ways analytics can provide operational efficiency, revenue growth, and cost reduction.
Новые технологии Intel в центрах обработки данныхCisco Russia
This document discusses new technologies in data centers including Intel processors for data centers, new storage architectures using Intel Optane technology and NVMe SSDs, and Intel Rack Scale Design. It provides information on Intel Xeon processors for different workloads and platforms. It also describes Intel Optane technology which uses 3D XPoint memory media to provide ultra-high endurance, low latency storage. NVMe SSDs over PCIe are presented as the future of storage. Finally, Intel Rack Scale Design is mentioned as simplifying platform management and enabling hyperscale agility.
The document discusses several topics related to comparing the performance and capacity of different computing systems. It introduces the concept of workload factor which allows comparing the capacity of systems to process the same workload despite architectural differences. Several industry standard benchmarks are described but they are noted to not always match real customer workloads. Real workloads place more stress on system interconnect and cache performance than most benchmarks.
The document discusses a presentation given by Seth Schneider from Intel and Russ Glaeser from Cascade Game Foundry. It introduces Intel's Graphics Performance Analyzers (GPA) tool and demonstrates how it was used to optimize the game Infinite Scuba developed by Cascade Game Foundry. The presentation covered an overview of GPA, details about Infinite Scuba, and a live demo of using GPA to analyze and improve performance of the game.
Driving Industrial InnovationOn the Path to ExascaleIntel IT Center
This document discusses driving industrial innovation through high performance computing (HPC). It summarizes Intel's progress in HPC technologies including processors, coprocessors, fabrics, and software. Examples are given of how HPC is transforming industries like automotive design at Audi. The top supercomputer is highlighted as using Intel Xeon and Xeon Phi processors. The document envisions continuing innovation to achieve exascale computing and connect more people through technology.
Achieve Unconstrained Collaboration in a Digital WorldIntel IT Center
Technology is at the center of every digitally-savvy workplace, yet organizations are constrained with bridging current tools to more modern solutions. This session from Gartner Digital Workplace Summit will cover a new way to facilitate employee collaboration that is easy, engaging and gives IT an uncompromised security and management experience.
The document discusses the future of storage technologies for cloud computing. It notes that cloud adoption is driving significant business opportunities but also increasing complexity. Intel's strategy is to build an open ecosystem, reduce complexity, and enable massive compute capabilities. New storage technologies like SSDs and NVMe can help optimize performance by providing much higher bandwidth and lower latency compared to hard disk drives. For example, using Intel SSDs with NVMe instead of HDDs can provide over 100x cost savings and 1400x power savings while also improving performance for database restart tasks by over 30 times.
How to create a high quality, fast texture compressor using ISPC Gael Hofemeier
This document discusses Intel's Fast ISPC Texture Compressor for compressing textures using DirectX 11 formats like BC7 and BC6H. It provides an overview of the DX11 texture compression formats and algorithms used in the Fast ISPC Texture Compressor. The compressor uses techniques like PCA initialization, iterative refinement, and fast partition pruning to quickly search for optimal block partitioning, endpoints, and weights. It achieves high quality compression through the use of SIMD acceleration with Intel's ISPC compiler.
Jeff Rous from Intel and Niklas Smedberg from Epic Games discussed optimizing the Unreal Engine 4 (UE4) game engine for Intel processors. They described measuring performance using Intel's Graphics Performance Analyzers, common pain points like memory bandwidth and dense geometry on Intel graphics, and shader optimizations. The presentation also covered optimizing UE4 for DirectX 12, adding support for Android x86/x64, and announcing fast ASTC texture compression support in UE4.
More explosions, more chaos, and definitely more blowing stuff upIntel® Software
This document discusses optimizations and new DirectX features for Intel graphics hardware. It begins with an introduction of Avalanche Studios, the developer of the game Just Cause 3. It then discusses the use of Intel's Graphics Performance Analyzers tools to analyze Just Cause 3 and identify optimization opportunities. The document outlines several low-level shader optimizations performed, including reworking math operations, rearranging variables, and reusing intermediate values. It also discusses leveraging new DirectX features pioneered by Intel. The goal of these optimizations is to improve performance for the large install base of gamers using Intel graphics.
TDC2019 Intel Software Day - Inferencia de IA em edge devicestdc-globalcode
This document discusses Intel's compiler optimizations and how they may differ depending on the microprocessor. It notes that:
- Intel's compilers may optimize differently for non-Intel microprocessors, including optimizations for SSE2, SSE3, and SSSE3 instruction sets.
- Intel does not guarantee the availability, functionality, or effectiveness of any optimization on non-Intel microprocessors.
- Microprocessor-dependent optimizations are intended for use with Intel microprocessors only. Certain non-Intel specific optimizations are also reserved for Intel microprocessors.
This document provides an update on the Intel-powered clamshell classmate PC. Key points include:
- The code name Cherry Point has been changed to the formal name Intel-powered clamshell CMPC.
- Certification status and OS support have been updated. Software stack availability has also been updated.
- Details are provided on the hardware specifications, software solutions, certifications, and other technical aspects of the Intel-powered clamshell CMPC.
This document discusses using hardware metrics to optimize Unity games for performance. It introduces Intel's Graphics Performance Analyzers tools which can measure CPU, GPU, memory and other metrics. Key metrics that can indicate bottlenecks are pixel shader duration, sampler stalls and memory bandwidth utilization. The document demonstrates analyzing a sample Unity project using these tools to identify optimization opportunities like simplifying geometry or materials. It encourages developers to measure performance on a range of hardware to optimize for lower-end devices.
Intel® Xeon® Processor E5-2600 v4 Product Family EAMGIntel IT Center
See why the new Intel® Xeon® processor E5-2600 v4 product family is ideal for next-generation application workloads and is the powerhouse for software-defined infrastructure (SDI) environments where automation and orchestration capabilities are foundational. Higher core counts, enhanced virtualization capabilities, and increased memory bandwidth provide the resources that are necessary to drive improvements in performance across a wide range of workloads. These processors also include technologies that can help IT organizations and cloud providers orchestrate resources more intelligently so they can optimize performance, agility, and efficiency. From 3-D data visualization and virtual prototyping, to personalized content delivery, new software capabilities provide the foundation for smarter, faster, and more agile business solutions.
Ade Justus Oyewole is an IT professional with over 15 years of experience in engineering, administration, support of information systems. He has extensive expertise in implementing, analyzing, troubleshooting and documenting computer hardware and software. He is proficient in Windows, Active Directory, networking, desktop support, and has several Microsoft certifications. Currently he works as a Helpdesk Support System Engineer at Sechaba Computer Services, where he provides technical support to clients and resolves any issues.
Intel provides several IT tools to help IT decision makers evaluate and communicate the business value of key IT activities. The tools include an Intel IT Server Sizing Tool, Xeon Refresh Estimator, and Laptop Refresh Savings Estimator. Each tool allows users to input variables specific to their environment to model scenarios and compare options to justify IT decisions and resource allocation. Additional resources from Intel on cloud computing solutions and examples of how Intel IT creates business value are also provided.
Hw09 Fingerpointing Sourcing Performance IssuesCloudera, Inc.
The document discusses automated problem diagnosis for large-scale distributed systems like Hadoop. It presents techniques for analyzing system logs and metrics to detect faults and localize their root causes. The goal is to automate diagnosis and provide early detection of problems to improve administrators' ability to manage complex systems.
This document summarizes Rackspace's use of Hadoop to process and query logs from multiple datacenters. Key points:
- Rackspace needed to query logs from mail/app servers to answer support and analytics questions. Previous solutions using single databases could not scale across datacenters.
- Hadoop allowed ingesting raw logs, building Lucene indexes for querying, and storing data across multiple datacenters. Real-time queries used Solr, batch queries used MapReduce.
- Implementation collected logs into Hadoop, used SolrOutputFormat to generate indexes, and queried via distributed Solr and MapReduce. This provided scalable storage, analysis, and querying across datacenters.
The document discusses scalable stream processing and map-reduce. It describes eBay's research labs and some of the large volumes of data it handles on a daily basis. It then discusses challenges in analyzing massive transaction and session data streams in real-time. The rest of the document describes Mobius, eBay's stream processing system, which uses a query language called MQL to detect patterns in streams and perform analytics in parallel across large clusters.
eHarmony used Amazon Web Services (AWS) like EC2 and Hadoop on EMR to build a scalable solution for processing large amounts of user data to power their online matchmaking services. This allowed them to overcome limitations of their existing infrastructure and realize significant cost savings compared to managing everything on-premises. Some challenges included ensuring reliability of each stage of processing and handling failures, as well as reducing data shuffling times between MapReduce jobs.
Doug Cutting on the State of the Hadoop EcosystemCloudera, Inc.
Doug Cutting, Apache Hadoop Co-founder, explains how the growth of the Hadoop ecosystem has made Hadoop a much more powerful machine, and how the continued expansion will lead to great things.
HBaseCon 2012 | Living Data: Applying Adaptable Schemas to HBase - Aaron Kimb...Cloudera, Inc.
HBase application developers face a number of challenges: schema management is performed at the application level, decoupled components of a system can break one another in unexpected ways, less-technical users cannot easily access data, and evolving data collection and analysis needs are difficult to plan for. In this talk, we describe a schema management methodology based on Apache Avro that enables users and applications to share data in HBase in a scalable, evolvable fashion. By adopting these practices, engineers independently using the same data have guarantees on how their applications interact. As data collection needs change, applications are resilient to drift in the underlying data representation. This methodology results in a data dictionary that allows less-technical users to understand what data is available to them for analysis and inspect data using general-purpose tools (for example, export it via Sqoop to an RDBMS). And because of Avro’s cross-language capabilities, HBase’s power can reach new domains, like web apps built in Ruby.
This document contains information about Embree ray tracing kernels. It discusses how Embree provides highly optimized ray tracing kernels to accelerate rendering performance for applications. Embree supports the latest CPUs and instruction sets and contains features like support for triangles, subdivision surfaces, and displacement mapping. It also contains performance results showing Embree achieving 1.5-6x speedups over other renderers on Intel Xeon and Xeon Phi platforms.
Software-defined Visualization, High-Fidelity Visualization: OpenSWR and OSPRayIntel® Software
This document discusses software-defined and high-fidelity visualization rendering techniques that run exclusively on CPUs. It introduces OpenSWR, an open-source software rasterizer that provides a drop-in replacement for OpenGL, and OSPRay, a ray tracing library that is not limited by legacy APIs. OpenSWR implements a subset of OpenGL to work with existing visualization applications and focuses on performance through threading and vectorization. OSPRay allows for more flexibility in rendering capabilities but requires more effort for existing apps to use. Both aim to provide scalable, flexible CPU-based rendering that can run on various system types and sizes.
This document contains several legal disclaimers and notices regarding Intel products and technologies. It states that information in the document is provided in connection with Intel products, and that no license is granted to any intellectual property. It also disclaims warranties and liability. The document notes that product plans and figures are preliminary and subject to change, and that errata may exist in products.
This document provides a roadmap for Intel's desktop, mobile, and datacenter products from the second half of 2013 through the first quarter of 2014. It outlines planned processor and chipset releases, including Ivy Bridge, Haswell, and Bay Trail architectures. The document also contains legal disclaimers regarding the provision of information, product warranties, mission critical applications, product specifications, and characterization of engineering samples.
AI & Computer Vision (OpenVINO) - CPBR12Jomar Silva
This document discusses Intel's compiler optimizations. It states that Intel's compilers may optimize Intel microprocessors differently than non-Intel microprocessors. Some optimizations like SSE2, SSE3, and SSSE3 instructions are designed for Intel microprocessors. Intel does not guarantee the availability, functionality, effectiveness of optimizations on non-Intel microprocessors. The document advises checking product guides for specific instruction set coverage. It provides a notice revision date of August 4, 2011.
LF_DPDK17_Enabling hardware acceleration in DPDK data plane applicationsLF_DPDK
This document discusses hardware acceleration models for DPDK data plane applications, including lookaside, inline, and full pipeline models. It provides examples of using these models to accelerate IPsec processing via crypto or protocol acceleration. Key considerations for using hardware acceleration include understanding the impact on the application architecture and pipeline, whether the accelerator supports all scenarios or exceptions must be handled in software, and any restrictions placed on the application. Metadata propagation is important to allow applications to determine if packets have been accelerated or not and support hybrid solutions.
The document discusses Intel's Open Image Denoise (OIDN) library, an open source denoising solution for lightmaps in the Unity game engine. It begins with an agenda for the talk and provides an overview of OIDN, including examples of its C++ API. It then discusses how OIDN can improve lightmap baking performance in Unity. The document contains several legal disclaimers and notices regarding Intel technologies.
Explore, design and implement threading parallelism with Intel® Advisor XEIntel IT Center
The document discusses Intel Advisor XE, a tool that enables parallelism and threading design. It allows users to quickly prototype threading options, project scaling on larger systems, and find synchronization errors before implementation. The tool's approach involves analyzing applications, designing parallelism, tuning, and checking implementations. It aims to help users make best use of multicore and manycore systems with hundreds of cores.
Gary Brown (Movidius, Intel): Deep Learning in AR: the 3 Year HorizonAugmentedWorldExpo
A talk from the Develop Track at AWE USA 2017 - the largest conference for AR+VR in Santa Clara, California May 31- June 2, 2017.
Gary Brown (Movidius, Intel): Deep Learning in AR: the 3 Year Horizon
Deep learning techniques are gaining in popularity in many facets of embedded vision, and this holds true for AR and VR. Will they soon dominate every facet of vision processing? This talk explores this question by examining the theory and practice of applying deep learning to real world problems for Augmented Reality, with real examples describing how this shift is happening today quickly in some areas, and slower in others.
http://AugmentedWorldExpo.com
Reinforcement Learning Coach — an open source research framework for training and evaluating reinforcement learning (RL) agents by harnessing the power of multi-core CPU processing to achieve state-of-the-art results.
The document discusses Intel's HPC portfolio and roadmap update. It provides an overview of the new Intel Xeon E5-2600 v2 processor family, highlighting its efficiency, performance, and security features. The Xeon E5-2600 v2 is expected to deliver up to 30% more performance using the same or less power compared to the previous generation. It offers up to 12 cores, 30MB of cache, and support for the latest I/O and memory technologies to provide powerful and efficient processing for modern data centers.
Accelerating SparkML Workloads on the Intel Xeon+FPGA Platform with Srivatsan...Databricks
FPGA has recently gained attention throughout the industry because of its performance-per-power efficiency, re-programmable flexibility and wide range of applicableness. As a prediction to this phenomenon, Intel has been planning a new product line which offers a Xeon processor with integrated FPGA that will enable datacenters to easily deploy high-performance accelerators with a relatively low cost of ownership. The new Xeon+FPGA Platform is supported with a software ecosystem that eliminates the difficulties traditional FPGA devices had such as datacenter wide accelerator deployment.
In this session, Intel will present their design and implementation of FPGA as a supplement to vcores in Spark YARN mode to accelerate SparkML applications on the Intel Xeon+FPGA platform. In particular, they have added new options to Spark core that provides an interface for the user to describe the accelerator dependencies of the application. The FPGA info in the Spark context will be used by the new APIs and DRF policy implemented on YARN to schedule the Spark executor to a host with Xeon+FPGA installed. Experimental results using ALS scoring applications that accelerate GEneral Matrix to Matrix Multiplication operations demonstrate that Xeon+FPGA improves the FLOPS throughput by 1.5× compared to a CPU-only cluster.
This document discusses new hardware features including CAT, COD, and Haswell, as well as network platforms. It provides an overview of run-to-completion and pipeline software models for network processing. Run-to-completion allows I/O and application work to be handled on a single core, while pipeline distributes packets to other cores for application work. Lockless queues are used to share data between cores and threads. Rings are the primary mechanism to move data between software units and I/O sources in DPDK.
The document discusses Intel's Network Builders program, which aims to accelerate software-defined infrastructure adoption through open standards and platforms. It does this by investing in strong ecosystems, committing to open source, and leveraging Intel's technology leadership. The program enables partners through technical resources, matchmaking opportunities, and marketing support. It also works with network operators on proofs of concept and trials. The goal is to move the industry from early SDN/NFV trials to commercial deployments through this ecosystem collaboration.
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The document discusses using Cloudera DataFlow to address challenges with collecting, processing, and analyzing log data across many systems and devices. It provides an example use case of logging modernization to reduce costs and enable security solutions by filtering noise from logs. The presentation shows how DataFlow can extract relevant events from large volumes of raw log data and normalize the data to make security threats and anomalies easier to detect across many machines.
Cloudera Data Impact Awards 2021 - Finalists Cloudera, Inc.
The document outlines the 2021 finalists for the annual Data Impact Awards program, which recognizes organizations using Cloudera's platform and the impactful applications they have developed. It provides details on the challenges, solutions, and outcomes for each finalist project in the categories of Data Lifecycle Connection, Cloud Innovation, Data for Enterprise AI, Security & Governance Leadership, Industry Transformation, People First, and Data for Good. There are multiple finalists highlighted in each category demonstrating innovative uses of data and analytics.
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Cloudera is proud to present the 2020 Data Impact Awards Finalists. This annual program recognizes organizations running the Cloudera platform for the applications they've built and the impact their data projects have on their organizations, their industries, and the world. Nominations were evaluated by a panel of independent thought-leaders and expert industry analysts, who then selected the finalists and winners. Winners exemplify the most-cutting edge data projects and represent innovation and leadership in their respective industries.
The document outlines the agenda for Cloudera's Enterprise Data Cloud event in Vienna. It includes welcome remarks, keynotes on Cloudera's vision and customer success stories. There will be presentations on the new Cloudera Data Platform and customer case studies, followed by closing remarks. The schedule includes sessions on Cloudera's approach to data warehousing, machine learning, streaming and multi-cloud capabilities.
Machine Learning with Limited Labeled Data 4/3/19Cloudera, Inc.
Cloudera Fast Forward Labs’ latest research report and prototype explore learning with limited labeled data. This capability relaxes the stringent labeled data requirement in supervised machine learning and opens up new product possibilities. It is industry invariant, addresses the labeling pain point and enables applications to be built faster and more efficiently.
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In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
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Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as -
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Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
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Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
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Choosing the Best Outlook OST to PST Converter: Key Features and Considerationswebbyacad software
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The Zaitechno Handheld Raman Spectrometer is a powerful and portable tool for rapid, non-destructive chemical analysis. It utilizes Raman spectroscopy, a technique that analyzes the vibrational fingerprint of molecules to identify their chemical composition. This handheld instrument allows for on-site analysis of materials, making it ideal for a variety of applications, including:
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Field analysis: Analyze materials in the field, such as during environmental monitoring or forensic investigations.
The Zaitechno Handheld Raman Spectrometer is easy to use and features a user-friendly interface. It is compact and lightweight, making it ideal for field applications. With its rapid analysis capabilities, the Zaitechno Handheld Raman Spectrometer can help you improve efficiency and productivity in your research or quality control workflows.
It's your unstructured data: How to get your GenAI app to production (and spe...Zilliz
So you've successfully built a GenAI app POC for your company -- now comes the hard part: bringing it to production. Aparavi addresses the challenges of AI projects while addressing data privacy and PII. Our Service for RAG helps AI developers and data scientists to scale their app to 1000s to millions of users using corporate unstructured data. Aparavi’s AI Data Loader cleans, prepares and then loads only the relevant unstructured data for each AI project/app, enabling you to operationalize the creation of GenAI apps easily and accurately while giving you the time to focus on what you really want to do - building a great AI application with useful and relevant context. All within your environment and never having to share private corporate data with anyone - not even Aparavi.
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TrustArc Webinar - Innovating with TRUSTe Responsible AI CertificationTrustArc
In a landmark year marked by significant AI advancements, it’s vital to prioritize transparency, accountability, and respect for privacy rights with your AI innovation.
Learn how to navigate the shifting AI landscape with our innovative solution TRUSTe Responsible AI Certification, the first AI certification designed for data protection and privacy. Crafted by a team with 10,000+ privacy certifications issued, this framework integrated industry standards and laws for responsible AI governance.
This webinar will review:
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