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Sign up today for the latest overviews, insights, and how-to’s on today's central topics—AI, DC, DL, HPC, IoT, ML, and other essential acronyms—that you can use right away.

Wednesday, July 31, 2019 9:00 am PDT
#DataCenterCloudComputing

Up to 50% Performance Gain on Hadoop* Clusters? You Bet Your Tweets.

Storage I/O can be a serious bottleneck for Hadoop* clusters, especially in hyperscale deployments like those at Twitter. Find out how a trifecta of Intel software and hardware removed the obstacles.

Processing over 1 trillion events per day, Twitter is one of the largest Hadoop* users in the world—typical clusters contain over 100,000 HDDs, half a million compute threads, and an exabyte of physical storage.

But there was scaling problem. The company’s configuration was reaching an I/O performance limit that could not be solved by simply adding more and bigger HDDs due to space and power limitations.

Join Milind Damle, Senior Director of Intel Big Data Technologies, to find out how Twitter got a new handle on this ocean of data, including how they:

  • Reduced runtimes by up to 50% on existing hardware
  • Removed a storage I/O bottleneck that enabled them to increase processor utilization
  • Achieved higher data center density by reducing the number of required HDDs
  • Increased total cost of ownership (TCO) savings by a projected 30%

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Milind Damle, Senior Director, Big Data Technologies, Intel Corporation

Milind is a Senior Director of Big Data technologies at Intel and leads a responsible for performance analysis, tuning, optimization, and benchmarking of big data workloads and applications on Intel® Architecture (IA) and other platforms. Additionally, this team delivers new features into Apache hadoop* and Spark* projects, and helps internal and external customers incorporate them into their respective IA optimizations. Milind joined Intel in 2002 and has a Master’s in Computer Science and Engineering from the Indian Institute of Technology in Mumbai.

Wednesday, August 14, 2019 9:00 am PDT
#CodeModernization

Tune Workloads & System Configurations—New Low-Overhead, Long-Duration Profiling

Getting a complete view of an entire system is critical to understanding performance and system component utilization. Learn how a new, free profiling tool helps you do precisely that.

Have you ever wondered how well your system configuration matches your workloads—that is, which workloads need tuning and which would benefit from a different system configuration?

Of course, you have. And then you’ve likely reached for the nearest performance analysis tool to figure it out.

But if the tool you use is designed for detailed, deep-dive analysis of short application runs, you will be overwhelmed by an avalanche of data.

What you need first is a holistic assessment of the entire system, which is exactly what Intel® VTune™ Amplifier’s Platform Profiler feature delivers.

Join Technical Consulting Engineer Munara Tolubaeva to learn about the benefits of Platform Profiler, including:

  • An overview of its in-depth, long-term (hours) analysis of a system
  • How to use it to gather low-overhead, course-grain metrics that identify system configuration issues and workloads that will benefit from optimization
  • Real-world use cases that demonstrate how the tool can identify performance issues like insufficient memory, memory configuration errors, and I/O bottlenecks

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Munara Tolubaeva, Technical Consulting Engineer, Intel Corporation

Munara is a technical consulting engineer responsible for helping customers use Intel® Software Tools to tune and optimize their applications for Intel® architecture. Her expertise includes high performance computing, and parallel programming techniques, performance analysis and optimization, compilers, and heterogeneous computing.

Munara joined Intel in 2016. She holds a Master’s Degree in Informatics from Middle East Technical University and a PhD in Computer Science from the University of Houston in Texas.

Wednesday, August 21, 2019 9:00 am PDT
#DataScience

Machine Learning 101 with Python and daal4py

Whether you’re new to machine learning or looking for a brush up, tune in to learn how Intel’s optimized Python* packages can increase your ML performance.

Machine learning (ML) is far past being merely a buzz word. It’s matured into a major disruptor, profoundly impacting business and transforming how we interact in the world and with each other.

For software developers, it’s increasingly becoming a major differentiator that leads to the obvious question: How do you increase your machine learning performance?

One way is by using the Intel® Distribution for Python*—a set of accelerated numeric Python packages, including scikit-learn, NumPy, SciPy, and daal4py, all optimized to work in both single and distributed modes.

Join Intel Technical Consulting Engineer David Liu, for an overview of Intel’s Python distribution and daal4py package, including:

  • How they can decrease your ML compute time
  • Where and when to use daal4py in your ML application
  • The new High-Performance Analytics Toolkit (HPAT) feature and how it accelerates getting data into your ML app

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David Liu, Technical Consulting Engineer, Intel Corporation

David Liu is a Lead Python* Technical Consulting Engineer who specializes in open source software development and focuses on machine learning, deep learning, AI, software architecture and build infrastructure. In his current role, he is responsible for assisting customers and the open-source community in all phases of improving software quality and optimizing it for Intel hardware. David joined Intel in 2015 and holds a Master’s of Science in Software Engineering from the University of Texas, Austin.

Wednesday, August 28, 2019 9:00 am PDT
#CodeModernization

Scale Your C++ Apps Efficiently with TBB Concurrent Container Classes

A concurrent container allows multiple threads to simultaneously access and update items in the container … but it often comes at a performance cost. Find out how Threading Building Blocks can help.

Ever wondered how to scale your C++ application most effectively? Or how two threads can compete for limited resourcing in a concurrent environment?

Providing concurrent access to container classes is not trivial and can burn a large portion of scalable application time. In this webinar, Software Development Engineer Aleksei Fedotov will discuss:

  • Why exclusive access to the Standard Template Library (STL) containers such as vector or map does not guarantee scalability
  • How the situation could be improved to support a concurrent environment properly
  • An overview of the highly concurrent container classes available in Threading Building Blocks library and the purposes they can be useful for

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Aleksei Fedotov, Software Development Engineer, Intel Corporation

Aleksei Fedotov is a Software Development Engineer at Intel responsible for the Threading Building Blocks (TBB) library, including key features such as flow graph, parallel algorithms, and concurrent containers. His expertise includes parallel processing, machine learning, and hardware architectures. Prior to joining Intel, Aleksei worked on a parallel framework for cache-efficient image processing, and implemented mathematical and statistical algorithms for serial, parallel, and distributed computing.

Aleksei holds a master’s degree in Applied Mathematics and Informatics from N. I. Lobachevsky State University in Nizhny Novgorod.

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