"Convergence of AI, HPC, and Data Analytics on HPC Supercomputers", Vikram Saletore, Intel
Invited Talk at EMC2 workshop, 6th Edition : https://www.emc2-ai.org/ Driven by an exponential increase in the volume and diversity of data during the past 15 years, we observe that Data Analytics (DA) and High Performance Computing (HPC) workloads share the same infrastructure. The same convergence is also witnessed with Artificial Intelligence (AI) and HPC and with DA and AI workloads due to the rapid development and use of deep learning frameworks in modeling and simulation algorithms. This convergence has begun to reshape the landscape of scientific computing and enabling scientists address large problems in ways that were not possible before. We present the three pillars that are driving the convergence of AI, HPC, and DA. We will present how the software stacks are supported efficiently over a versatile CPU datacenter infrastructure. We will present AI use cases on large Intel Xeon HPC infrastructure in collaborations with SURF, CERN Open Labs, and Novartis. The use cases include training AI models in histopathology, astrophysics, predicting molecules in chemical reactions, high content screening of phenotypes, and replacing and accelerating HPC simulations in High Energy Physics. We will show scaling of AI workloads significantly reducing the time to train and improving inference performance using Intel DL Boost for quantization. Dr. Vikram Saletore is a Principal Engineer, Sr. IEEE Member, and AI Performance Architect focused on Deep Learning (DL) performance. He collaborates with industry Enterprise/Government, HPC, & OEM customers on DL Training and Inference. Vikram is also a Co-PI for DL research and customer use cases with; SURF, CERN, Taboola, Novartis, & GENCI. 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 network acceleration. Prior to Intel, as a tenure-track faculty in Computer Science at Oregon State University, Corvallis, Oregon, 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 Computing from University of Illinois at Urbana-Champaign. He holds multiple patents issued, 3 patents in AI pending, ~60 research papers and ~40 white papers, blogs specifically in AI, Machine Learning Analytics, and Deep Learning.
Invited Talk at EMC2 workshop, 6th Edition : https://www.emc2-ai.org/ Driven by an exponential increase in the volume and diversity of data during the past 15 years, we observe that Data Analytics (DA) and High Performance Computing (HPC) workloads share the same infrastructure. The same convergence is also witnessed with Artificial Intelligence (AI) and HPC and with DA and AI workloads due to the rapid development and use of deep learning frameworks in modeling and simulation algorithms. This convergence has begun to reshape the landscape of scientific computing and enabling scientists address large problems in ways that were not possible before. We present the three pillars that are driving the convergence of AI, HPC, and DA. We will present how the software stacks are supported efficiently over a versatile CPU datacenter infrastructure. We will present AI use cases on large Intel Xeon HPC infrastructure in collaborations with SURF, CERN Open Labs, and Novartis. The use cases include training AI models in histopathology, astrophysics, predicting molecules in chemical reactions, high content screening of phenotypes, and replacing and accelerating HPC simulations in High Energy Physics. We will show scaling of AI workloads significantly reducing the time to train and improving inference performance using Intel DL Boost for quantization. Dr. Vikram Saletore is a Principal Engineer, Sr. IEEE Member, and AI Performance Architect focused on Deep Learning (DL) performance. He collaborates with industry Enterprise/Government, HPC, & OEM customers on DL Training and Inference. Vikram is also a Co-PI for DL research and customer use cases with; SURF, CERN, Taboola, Novartis, & GENCI. 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 network acceleration. Prior to Intel, as a tenure-track faculty in Computer Science at Oregon State University, Corvallis, Oregon, 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 Computing from University of Illinois at Urbana-Champaign. He holds multiple patents issued, 3 patents in AI pending, ~60 research papers and ~40 white papers, blogs specifically in AI, Machine Learning Analytics, and Deep Learning.