{"id":23195,"date":"2019-09-03T08:30:43","date_gmt":"2019-09-03T15:30:43","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=23195"},"modified":"2019-09-04T08:37:03","modified_gmt":"2019-09-04T15:37:03","slug":"interview-terry-deem-and-david-liu-at-intel","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2019\/09\/03\/interview-terry-deem-and-david-liu-at-intel\/","title":{"rendered":"Interview: Terry Deem and David Liu at Intel"},"content":{"rendered":"\n

I recently caught up with Terry Deem, Product Marketing Manager for Data Science, Machine Learning and Intel\u00ae Distribution for Python, and David Liu, Software Technical Consultant Engineer for the Intel\u00ae Distribution for Python*, both from Intel, to discuss the Intel\u00ae Distribution for Python (IDP): targeted classes of developers, use with commonly used Python packages for data science, benchmark comparisons, the solution\u2019s use in scientific computing, and a look to the future with respect to IPD. This Q&A is a follow-up to a previous sponsored post, \u201cSupercharge Data Science Applications with the Intel\u00ae Distribution for Python<\/a>.\u201d<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

Terry specializes in developer tools and the developer community. Terry has covered a wide variety of tools for Intel from the highly popular XDK to the industry-standard Media Server Studio. He currently covers Intel\u2019s machine learning tool such as Intel\u00ae Data Analytics Acceleration Library (Intel\u00ae DAAL), Intel\u00ae Math Kernel Library for Deep Neural Networks (Intel\u00ae MKL-DNN) and Intel Distribution for Python. <\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

David specializes in Python* and Machine Learning applications and open-source software development.  David has represented the Intel\u00ae Distribution for Python* and other Python-based projects at Intel and does much of the community work in the Python and SciPy ecosystem.<\/p>\n\n\n\n

insideBIGDATA:<\/strong> Hello Terry and David! Let\u2019s talk\nabout the Intel\u00ae Distribution for Python*. Can you tell our readers who will\nbenefit most from this distribution? <\/p>\n\n\n\n

Terry Deem:<\/strong> The Intel\u00ae Distribution for Python is\nbest suited for the needs of data scientists, data engineers, deep learning\npractitioners, scientific programmers, and HPC developers\u2014really, any Python\nprogrammer in these fields. Because it supports SciPy, NumPy and scikit-learn,\nit supports the needs of this breadth of developers really well. They\u2019ll want\nto use the accelerated version of scikit-learn, rather than the stock\nscikit-learn. With this accelerated version, we can demonstrate orders of\nmagnitude performance increases in some cases, without changing any code. If these\ndevelopers are using the packages in stock format, they’re really leaving performance\non the table.<\/p>\n\n\n\n

insideBIGDATA:<\/strong> Yes, I know that many of our readers\nuse Python, so this accelerated solution should be of great interest. Actually,\nthis leads me to the next question, about commonly used Python packages. It is\nsaid that of data scientists who use Python, 90% use Pandas for data\ntransformation tasks. How well does the Intel Distribution for Python perform\nwith such packages?<\/p>\n\n\n\n

David Liu:<\/strong> There are two places where you can\nobtain these accelerations in code. For Pandas, you have NumPy* accelerations; Pandas\nis composed on top of *NumPy and inherits its accelerations. We\u2019ve also developed\nan open source technology, the High\nPerformance Analytics Toolkit<\/a> (HPAT), which compiles the Pandas\ncalls utilizing Numba. HPAT enables acceleration beyond the limitations of\nstock Pandas by bypassing the Global Interpreter Lock (GIL) in Python for\ncomputation. <\/p>\n\n\n\n

Terry Deem: And\nHPAT is really simple to use. You basically just have one line of code that you\nadd to your existing code and away you go.<\/p>\n\n\n\n

insideBIGDATA:<\/strong> Our readers love benchmarks, i.e.\nspeed comparison for different solutions. Can you comment on benchmarks for the\nIntel Distribution for Python? Do these benchmarks include any typical use\ncases often performed in a data science pipeline?<\/p>\n\n\n\n

David Liu:<\/strong> Yes, we\u2019ve developed extensive\nbenchmarks<\/a> for Intel Distribution for\nPython, such as the ibench<\/a>\nbenchmark, which I helped create. With this benchmark, you can actually trace\nevery one of the individual calls, such as NumPy\u2019s\ndot()<\/a>function, an FFT or a convolution. Using ibench,\nyou can view each of those simple connects as isolated function definitions, along\nwith the benchmarking. ibench runs the test for each, and then gives you the runtimes.\nYou can then use this data to test against stock Python and our distribution. This\nis the source of many of the website benchmarks as\nwell. ibench allows for a simplified\nbenchmarking case where you can do A\/B testing with relative times.<\/p>\n\n\n\n

insideBIGDATA: <\/strong>My next question has to do with\nscientific computing. I know that Python’s NumPy* and SciPy* packages are the\nheart of scientific computing with Python*. Can you speak to Intel\u2019s work in\nPython for scientific computing? <\/p>\n\n\n\n

David Liu:<\/strong> In the case of NumPy and SciPy, there\nare a number of important optimizations for scientific functions. The biggest\none is transcendental functions, which is where many of the big vectorization\ncapabilities come through. We\u2019ve done a lot of optimization of numpy.dot (), FFTs,\nLower\/Upper Decomposition (LU), and QR factorization. And we did some\noptimization on memory management and vectorization engine management to get the\nmajority of the calls to work. <\/p>\n\n\n\n

We really look at two forms of acceleration\u2014vectorization scaling and core scaling. The concept is that the new additional types of vectorizations in Intel\u00ae Advanced Vector Extensions 512 (Intel\u00ae AVX-512) can give you one set of advantages. The next set of advantages will come from core scaling. The concept underlying core scaling is that if you increase the number of cores that are available to scale, your workload will get faster as you add more cores to it. So, it\u2019s important that we address both vectorization and core scaling.<\/p>\n\n\n\n

\"\"<\/figure><\/div>\n\n\n\n

insideBIGDATA:<\/strong> As you look forward using your\nproverbial crystal ball, can you give us insight into the roadmap for the Intel\nDistribution for Python?<\/p>\n\n\n\n

Terry Deem:<\/strong> I\u2019m looking forward to the next\nrelease, due out soon. It will have updated algorithms for the Intel\u00ae Data Analytics\nAcceleration Library and daal4py, the data science-first Python package to the\nlibrary. <\/p>\n\n\n\n

David Liu:<\/strong> Yes, there are additional algorithms\nand new compute modes available for them, specifically distributed and\nstreaming, in this next release. One of the things that we’re also looking into\nis optimizing Python for Intel\u2019s latest hardware, such as our FPGAs and\naccelerators. We\u2019re looking at how we take our great portfolio of hardware\u2014both\nexisting and in the future\u2014and ensure that users get the most out of it.<\/p>\n\n\n\n

insideBIGDATA:<\/strong> Is there anything else you\u2019d like to\nadd that our highly focused audience might like to hear about?<\/p>\n\n\n\n

Terry Deem:<\/strong> I think the Intel Distribution for\nPython offers incredible performance and ease of use for a breadth of\ndevelopers, data scientists and scientific computing professionals, without the\nneed for them to rewrite their algorithms or code or get away from what they\nwant to do. We understand that they don’t want to sit down, tune code and try\nto figure it out\u2014they\u2019d rather get the results. The value of Intel Distribution\nfor Python is that they can download and start using it right away and get the\nperformance that was just sitting there hidden inside their hardware all this\ntime. Performance they didn’t even know they had because their code wasn\u2019t\nusing the fast instruction set in the CPU. They can get results overnight by\nusing this simple technique. <\/p>\n\n\n\n

David Liu:<\/strong> Another important point is\nreproducibility. That’s something that we care about. We ensure reproducibility,\nwhile delivering performance. I think our commitment to open source and our\ncommitment to doing what is scientifically correct and achieving true reproducibility\nis of great importance.<\/p>\n\n\n\n

Want to\nlearn more? Download\nIntel\u00ae Distribution for Python* today ><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

I recently caught up with Terry Deem, Product Marketing Manager for Data Science, Machine Learning and Intel\u00ae Distribution for Python, and David Liu, Software Technical Consultant Engineer for the Intel\u00ae Distribution for Python*, both from Intel, to discuss the Intel\u00ae Distribution for Python (IDP): targeted classes of developers, use with commonly used Python packages for data science, benchmark comparisons, the solution\u2019s use in scientific computing, and a look to the future with respect to IPD.<\/p>\n","protected":false},"author":37,"featured_media":23199,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[68,87,180,210,773,67,56,1],"tags":[133,284,774,568,277,337,95],"acf":[],"yoast_head":"\nInterview: Terry Deem and David Liu at Intel - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2019\/09\/03\/interview-terry-deem-and-david-liu-at-intel\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Interview: Terry Deem and David Liu at Intel - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"I recently caught up with Terry Deem, Product Marketing Manager for Data Science, Machine Learning and Intel\u00ae Distribution for Python, and David Liu, Software Technical Consultant Engineer for the Intel\u00ae Distribution for Python*, both from Intel, to discuss the Intel\u00ae Distribution for Python (IDP): targeted classes of developers, use with commonly used Python packages for data science, benchmark comparisons, the solution\u2019s use in scientific computing, and a look to the future with respect to IPD.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/insidebigdata.com\/2019\/09\/03\/interview-terry-deem-and-david-liu-at-intel\/\" \/>\n<meta property=\"og:site_name\" content=\"insideBIGDATA\" \/>\n<meta property=\"article:publisher\" content=\"http:\/\/www.facebook.com\/insidebigdata\" \/>\n<meta property=\"article:published_time\" content=\"2019-09-03T15:30:43+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2019-09-04T15:37:03+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_IPD_logo.png\" \/>\n\t<meta property=\"og:image:width\" content=\"379\" \/>\n\t<meta property=\"og:image:height\" content=\"266\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Daniel Gutierrez\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@AMULETAnalytics\" \/>\n<meta name=\"twitter:site\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Daniel Gutierrez\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/insidebigdata.com\/2019\/09\/03\/interview-terry-deem-and-david-liu-at-intel\/\",\"url\":\"https:\/\/insidebigdata.com\/2019\/09\/03\/interview-terry-deem-and-david-liu-at-intel\/\",\"name\":\"Interview: Terry Deem and David Liu at Intel - 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/08\/Intel_IPD_logo.png","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-627","jetpack-related-posts":[{"id":23011,"url":"https:\/\/insidebigdata.com\/2019\/07\/29\/supercharge-data-science-applications-with-the-intel-distribution-for-python\/","url_meta":{"origin":23195,"position":0},"title":"Supercharge Data Science Applications with the Intel\u00ae Distribution for Python","date":"July 29, 2019","format":false,"excerpt":"Intel\u00ae Distribution for Python is a distribution of commonly used packages for computation and data intensive domains, such as scientific and engineering computing, big data, and data science. With Intel\u00ae Distribution for Python you can supercharge Python applications and speed up core computational packages with this performance-oriented distribution. Professionals who\u2026","rel":"","context":"In "Data Science"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2019\/07\/Intel_Python_dist.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":15122,"url":"https:\/\/insidebigdata.com\/2016\/06\/01\/technical-collaboration-expanding-anaconda-ecosystem\/","url_meta":{"origin":23195,"position":1},"title":"Intel and Continuum Analytics Work Together to Extend the Power of Python-based Analytics Across the Enterprise","date":"June 1, 2016","format":false,"excerpt":"Continuum Analytics, the creator and driving force behind Anaconda, a leading open data science platform powered by Python, welcomes Intel into the Anaconda ecosystem. Intel has adopted the Anaconda packaging and distribution and is working with Continuum to provide interoperability.","rel":"","context":"In "Big Data"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":18847,"url":"https:\/\/insidebigdata.com\/2017\/09\/13\/intel-parallel-studio-xe-helps-developers-take-hpc-enterprise-cloud-applications-max\/","url_meta":{"origin":23195,"position":2},"title":"Intel\u00ae Parallel Studio XE Helps Developers Take their HPC, Enterprise, and Cloud Applications to the Max","date":"September 13, 2017","format":false,"excerpt":"Intel\u00ae Parallel Studio XE is a comprehensive suite of development tools that make it fast and easy to build modern code that gets every last ounce of performance out of the newest Intel\u00ae processors. 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