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Martin Mendoza

Martin Mendoza

Martin Mendoza

In recent years, hyperscale data centers have been optimized for scale-out stateless applications and zettabyte storage, with a focus on CPU-centric platforms. However, as the infrastructure shifts towards next-generation AI applications, the center of gravity is moving towards GPU/accelerators. This transition from "millions of small stateless applications" to "large AI applications running across clusters of GPUs" is pushing the limits of accelerators, network, memory, topologies, rack power, and other components. To keep up with this dramatic change, innovation is necessary to ensure that hyperscale data centers can continue to support the growing demands of AI applications. This keynote speech will explore the challenges and opportunities of this evolution and highlight the key areas where innovation is needed to enable the future of hyperscale data centers.

Systems Infrastructure/Architecture
AI/ML Compute

Author:

Manoj Wadekar

AI Systems Technologist
Meta
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Manoj Wadekar

AI Systems Technologist
Meta
 

Terri Wiggins

Senior Vice President, Health Equity
American Diabetes Association

Terri Wiggins

Senior Vice President, Health Equity
American Diabetes Association

Terri Wiggins

Senior Vice President, Health Equity
American Diabetes Association
 

Yamile Molina

Director of Community Engagement
University of Illinois Cancer Center

Yamile Molina

Director of Community Engagement
University of Illinois Cancer Center

Yamile Molina

Director of Community Engagement
University of Illinois Cancer Center
 

Myra Parker

Associate Professor
University of Washington

Myra Parker

Associate Professor
University of Washington

Myra Parker

Associate Professor
University of Washington

Memory and Data challenges: HPC-AI view from the energy industry 

Shell Upstream has been processing large subsurface datasets for multiple decades driving significant business value.  Many of the state of the art algorithms for this have been developed using deep domain knowledge and have benefitted from the hardware technology improvements over the years. However, the demand for more efficient processing as datasets get bigger and the algorithms become even more complex is ever-growing. This talk will focus on the memory and data management challenges for a variety of traditional HPC workflows in the energy industry. It will also cover unique challenges for accelerating modern AI-based workflows requiring new innovations. 

Author:

Dr. Vibhor Aggarwal

Manager: Digital & Scientific HPC
Shell

Vibhor is an R&D leader with expertise in HPC Software, Scientific Visualization, Cloud Computing and AI technologies with 14 years of experience. He and his team at Shell are currently work on problems in optimizing HPC software for simulations, large-scale and generative AI, combination of Physics and AI models, developing platform and products for HPC-AI solutions as well as emerging HPC areas for energy transition at the forefront of Digital Innovation. He has two patents and several research publications. Vibhor has a BEng in Computer Engineering from University of Delhi and a PhD in Engineering from University of Warwick.    

... read more

Dr. Vibhor Aggarwal

Manager: Digital & Scientific HPC
Shell

Vibhor is an R&D leader with expertise in HPC Software, Scientific Visualization, Cloud Computing and AI technologies with 14 years of experience. He and his team at Shell are currently work on problems in optimizing HPC software for simulations, large-scale and generative AI, combination of Physics and AI models, developing platform and products for HPC-AI solutions as well as emerging HPC areas for energy transition at the forefront of Digital Innovation. He has two patents and several research publications. Vibhor has a BEng in Computer Engineering from University of Delhi and a PhD in Engineering from University of Warwick.    

Oracle AI Vector Search enables enterprises to leverage their own business data to build cutting-edge generative AI solutions. AI Vectors are data structures that encode the key features or essence of unstructured entities such as images or documents. The more similar two entities are, the shorter the mathematical distance between their corresponding AI vectors. With AI Vector search, Oracle Database is introducing a new vector datatype, new vector indexes (in-memory neighbor graph indexes and neighbor partitioned indexes), and new Vector SQL operators for highly efficient and powerful similarity search queries. Oracle AI Vector Search enables applications to combine their business data with large language models (LLMs) using a technique called Retrieval Augmentation Generation (RAG), to deliver amazingly accurate responses to natural language questions. With AI Vector Search in Oracle Database, users can easily build AI applications that combine relational searches with similarity search, without requiring data movement to a separate vector database, and without any loss of security, data integrity, consistency, or performance.

Author:

Tirthankar Lahiri

SVP, Data & In-Memory Technologies
Oracle

Tirthankar Lahiri is Vice President of the Data and In-Memory Technologies group for Oracle Database and is responsible for the Oracle Database Engine (including Database In-Memory, Data and Indexes, Space Management, Transactions, and the Database File System), the Oracle TimesTen In-Memory Database, and Oracle NoSQLDB. Tirthankar has 22 years of experience in the Database industry and has worked extensively in a variety of areas including Manageability, Performance, Scalability, High Availability, Caching, Distributed Concurrency Control, In-Memory Data Management, NoSQL architectures, etc. He has 27 issued and has several pending patents in these areas. Tirthankar has a B.Tech in Computer Science from the Indian Institute of Technology (Kharagpur) and an MS in Electrical Engineering from Stanford University.

... read more

Tirthankar Lahiri

SVP, Data & In-Memory Technologies
Oracle

Tirthankar Lahiri is Vice President of the Data and In-Memory Technologies group for Oracle Database and is responsible for the Oracle Database Engine (including Database In-Memory, Data and Indexes, Space Management, Transactions, and the Database File System), the Oracle TimesTen In-Memory Database, and Oracle NoSQLDB. Tirthankar has 22 years of experience in the Database industry and has worked extensively in a variety of areas including Manageability, Performance, Scalability, High Availability, Caching, Distributed Concurrency Control, In-Memory Data Management, NoSQL architectures, etc. He has 27 issued and has several pending patents in these areas. Tirthankar has a B.Tech in Computer Science from the Indian Institute of Technology (Kharagpur) and an MS in Electrical Engineering from Stanford University.

Author:

Puja Das

Senior Director, Personalization
Warner Bros. Entertainment

Dr. Puja Das, leads the Personalization team at Warner Brothers Discovery (WBD) which includes offerings on Max, HBO, Discovery+ and many more.

Prior to WBD, she led a team of Applied ML researchers at Apple, who focused on building large scale recommendation systems to serve personalized content on the App Store, Arcade and Apple Books. Her areas of expertise include user modeling, content modeling, recommendation systems, multi-task learning, sequential learning and online convex optimization. She also led the Ads prediction team at Twitter (now X), where she focused on relevance modeling to improve App Ads personalization and monetization across all of Twitter surfaces.

She obtained her Ph.D from University of Minnesota in Machine Learning, where the focus of her dissertation was online learning algorithms, which work on streaming data. Her dissertation was the recipient of the prestigious IBM Ph D. Fellowship Award.

She is active in the research community and part of the program committee at ML and recommendation system conferences. Shas mentored several undergrad and grad students and participated in various round table discussions through Grace Hopper Conference, Women in Machine Learning Program colocated with NeurIPS, AAAI and Computing Research Association- Women’s chapter.

... read more

Puja Das

Senior Director, Personalization
Warner Bros. Entertainment

Dr. Puja Das, leads the Personalization team at Warner Brothers Discovery (WBD) which includes offerings on Max, HBO, Discovery+ and many more.

Prior to WBD, she led a team of Applied ML researchers at Apple, who focused on building large scale recommendation systems to serve personalized content on the App Store, Arcade and Apple Books. Her areas of expertise include user modeling, content modeling, recommendation systems, multi-task learning, sequential learning and online convex optimization. She also led the Ads prediction team at Twitter (now X), where she focused on relevance modeling to improve App Ads personalization and monetization across all of Twitter surfaces.

She obtained her Ph.D from University of Minnesota in Machine Learning, where the focus of her dissertation was online learning algorithms, which work on streaming data. Her dissertation was the recipient of the prestigious IBM Ph D. Fellowship Award.

She is active in the research community and part of the program committee at ML and recommendation system conferences. Shas mentored several undergrad and grad students and participated in various round table discussions through Grace Hopper Conference, Women in Machine Learning Program colocated with NeurIPS, AAAI and Computing Research Association- Women’s chapter.

Los Alamos National Laboratory's (LANL) has a diverse set of High Performance Computing codes. Analysis of many of these codes indicate they are heavily memory bound with sparse memory accesses. High Bandwidth Memory (HBM) has proven a significant advancement in improving the performance of these codes but the roadmap for major (step function) improvements in memory technologies is unclear. Addressing this challenge will require a renewed focus on high performance memory and processor technologies that take a more aggressive and holistic view of advancements in ISA, microarchitecture, and memory controller technologies. Beyond scientific simulations, advancements in performance of sparse memory accesses will benefit graph analysis, DLRM inference, and database workloads.

Author:

Galen Shipman

Computer Scientist
Los Alamos National Laboratories

Galen Shipman is a computer scientist at Los Alamos National Laboratory (LANL). His interests include programming models, scalable runtime systems, and I/O.  As Chief Architect he leads architecture and technology of Advanced Technology Systems (ATS) at LANL. He has led performance engineering across LANL’s multi-physics integrated codes and the advancement and integration of next-generation programming models such as the Legion programming system as part of LANL's next-generation code project, Ristra. His work in storage systems and I/O is currently focused on composable micro-services as part of the Mochi project. His prior work in scalable software for HPC include major contributions to broadly used technologies including the Lustre parallel file system and Open MPI.

... read more

Galen Shipman

Computer Scientist
Los Alamos National Laboratories

Galen Shipman is a computer scientist at Los Alamos National Laboratory (LANL). His interests include programming models, scalable runtime systems, and I/O.  As Chief Architect he leads architecture and technology of Advanced Technology Systems (ATS) at LANL. He has led performance engineering across LANL’s multi-physics integrated codes and the advancement and integration of next-generation programming models such as the Legion programming system as part of LANL's next-generation code project, Ristra. His work in storage systems and I/O is currently focused on composable micro-services as part of the Mochi project. His prior work in scalable software for HPC include major contributions to broadly used technologies including the Lustre parallel file system and Open MPI.

Author:

Galen Shipman

Computer Scientist
Los Alamos National Laboratories

Galen Shipman is a computer scientist at Los Alamos National Laboratory (LANL). His interests include programming models, scalable runtime systems, and I/O.  As Chief Architect he leads architecture and technology of Advanced Technology Systems (ATS) at LANL. He has led performance engineering across LANL’s multi-physics integrated codes and the advancement and integration of next-generation programming models such as the Legion programming system as part of LANL's next-generation code project, Ristra. His work in storage systems and I/O is currently focused on composable micro-services as part of the Mochi project. His prior work in scalable software for HPC include major contributions to broadly used technologies including the Lustre parallel file system and Open MPI.

... read more

Galen Shipman

Computer Scientist
Los Alamos National Laboratories

Galen Shipman is a computer scientist at Los Alamos National Laboratory (LANL). His interests include programming models, scalable runtime systems, and I/O.  As Chief Architect he leads architecture and technology of Advanced Technology Systems (ATS) at LANL. He has led performance engineering across LANL’s multi-physics integrated codes and the advancement and integration of next-generation programming models such as the Legion programming system as part of LANL's next-generation code project, Ristra. His work in storage systems and I/O is currently focused on composable micro-services as part of the Mochi project. His prior work in scalable software for HPC include major contributions to broadly used technologies including the Lustre parallel file system and Open MPI.