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The Confluence of HPC and AI – Intel Customer Use Cases

Vikram Saletore is a Principal Engineer and manages the Customer Solutions Performance Enabling team in the Artificial Intelligence Products Group at Intel.

In this video from the MVAPICH User Group, Vikram Saletore from Intel presents: The Confluence of HPC and AI – A Converged Journey with Intel HPC-AI Solutions.

Driven by an exponential increase and availability in volume and diversity of data, Artificial Intelligence (AI) specifically Deep learning (DL) is transforming many businesses around the globe by enabling them to drive operational efficiencies and build new products and services. AI has also begun to reshape the landscape of scientific computing and enabling scientists to address large problems in ways that were not possible before. Intel collaborates with customers and partners worldwide to build, accelerate, scale and deploy their AI applications on Intel based HPC platforms. We share with you our insights on several customer AI use cases we have enabled, the orders of magnitude performance acceleration we have delivered via popular open-source software framework optimizations, and the best-known methods to advance the convergence of AI and High Performance Computing on Intel Xeon Scalable Processor based servers. We will also demonstrate how large memory systems help real world AI applications efficiently.

Vikram Saletore is a Principal Engineer, Sr. IEEE Member, and Technical Manager focused on Deep Learning (DL) performance optimizations and acceleration for HPC-AI convergence. He collaborates with industry, Enterprise/Government, HPC, & OEM customers on DL Training and Inference. Vikram is also a Co-PI for DL research with a customers; SURFsara B.V., CERN OpenLabs, Max Planck, Novartis, GENCI/CINES/INRIA, others. Vikram has 25+ years of experience and has delivered optimized software to Oracle, Informix, and completed technical readiness for Intel’s 3D-XPoint memory via performance modeling. As a Research Scientist with Intel Labs, he led collaboration with HP Labs, Palo Alto for TCP acceleration. Prior to Intel, as a tenure-track teaching faculty in Computer Science at Oregon State University, Corvallis, OR, Vikram led NSF funded research in parallel programming and distributed computing directly supervising 8 students (PhD, MS). He also developed CPU and network products at DEC and AMD. Vikram received his MS from Berkeley & PhD in EE in Parallel & Distributed Computing from University of Illinois at Urbana-Champaign. He holds multiple patents, 3 pending in DL, ~60 research papers and ~45 white papers, blogs specifically in AI, Machine Learning Analytics, and Deep Learning.

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