Keynote Talks

Title: Designing High-Performance and Scalable Middleware for the Modern HPC and AI Era

Speaker: Dhabaleswar K. (DK) Panda, Distinguished Professor of Engineering, Computer Science and Engineering, The Ohio State University, USA

Time: 19th December, 2024, 9 AM to 10 AM IST

Abstract: This talk focuses on challenges and opportunities in designing middleware for HPC and AI (Deep/Machine Learning) workloads on modern high-end computing systems. The talk initially presents the challenges in designing HPC middleware by considering support for dense multi-core CPUs, high-performance interconnects, GPUs, and emerging DPUs. Advanced designs and solutions (such as RDMA, in-network computing, GPUDirect RDMA, on-the-fly compression) to exploit novel features of these emerging technologies and their benefits in the context of MVAPICH libraries (http://mvapich.cse.ohio-state.edu) are presented. Next, the talk focuses on MPI-driven solutions for the AI (Deep/Machine Learning) domains to extract performance and scalability for popular Deep Learning frameworks, large out-of-core models, GPUs, and DPUs. MPI-driven solutions to accelerate data science applications like Dask are also highlighted. The talk concludes with an overview of the activities in the NSF-AI Institute ICICLE (https://icicle.osu.edu/) to address challenges in designing future high-performance edge-to-HPC/cloud middleware for AI-driven data-intensive applications over the computing continuum.

Speaker Bio: DK Panda is a Professor and University Distinguished Scholar of Computer Science and Engineering at the Ohio State University.  He is serving as the Director of the ICICLE NSF-AI Institute (https://icicle.ai). He has published over 500 papers. The MVAPICH MPI libraries, designed and developed by his research group (http://mvapich.cse.ohio-state.edu), are currently being used by more than 3,400 organizations worldwide (in 92 countries). More than 1.8 million downloads of this software have taken place from the project’s site. This software is empowering many clusters in the TOP500 list. High-performance and scalable solutions for Deep Learning frameworks and Machine Learning applications from his group are available fromhttps://hidl.cse.ohio-state.edu. Similarly, scalable, and high-performance solutions for Big Data and Data science frameworks are available from https://hibd.cse.ohio-state.edu. Prof. Panda is an IEEE Fellow. He is a recipient of the 2022 IEEE Charles Babbage Award and the 2024 IEEE TCPP Outstanding Service and Contributions Award. More details about Prof. Panda are available at http://www.cse.ohio-state.edu/~panda.

Title: Agile Design of Domain-Specific Hardware Accelerators and Compilers

Speaker: Priyanka Raina, Assistant Professor, Electrical Engineering, Stanford University, USA

Time: 21st December, 2024, 9 AM to 10 AM IST

Abstract: With the slowing of Moore’s law, computer architects have turned to domain-specific hardware accelerators to improve the performance and efficiency of computing systems. However, programming these systems entails significant modifications to the software stack to properly leverage the specialized hardware. Moreover, the accelerators become obsolete quickly as the applications evolve. What is needed is a structured approach for generating programmable accelerators and for updating the software compiler as the accelerator architecture evolves with the applications. In this talk, I will describe a new agile methodology for co-designing programmable hardware accelerators and compilers. Our methodology employs a combination of new programming languages and formal methods to automatically generate the accelerator hardware and its compiler from a single specification. This enables faster evolution and optimization of accelerators, because of the availability of a working compiler. I will showcase this methodology using Amber, a coarse-grained programmable accelerator for imaging and machine learning (ML) we designed and fabricated using our flow in TSMC 16 nm technology. I will show how we agilely evolved Amber into Onyx, our next generation accelerator, using an application-driven design space exploration framework called APEX enabled by our hardware-compiler co-design flow.

Speaker Bio: Priyanka Raina received the B.Tech. degree in Electrical Engineering from IIT Delhi in 2011, and the M.S. and Ph.D. degrees in Electrical Engineering and Computer Science from MIT in 2013 and 2018, respectively. She was a Visiting Research Scientist with NVIDIA Corporation in 2018. Since 2018 she is an Assistant Professor of Electrical Engineering at Stanford University, where she works on domain-specific hardware architectures and agile hardware–software codesign methodology.

Dr. Raina is a 2018 Terman Faculty Fellow. She was a co-recipient of the Best Demo Paper Award at VLSI 2022, the Best Student Paper Award at VLSI 2021, the IEEE Journal of Solid-State Circuits (JSSC) Best Paper Award in 2020, the Best Paper Award at MICRO 2019, and the Best Young Scientist Paper Award at ESSCIRC 2016. She has won the DARPA Young Faculty Award in 2024, Sloan Research Fellowship in 2024, the National Science Foundation (NSF) CAREER Award in 2023, the Intel Rising Star Faculty Award in 2021, and the Hellman Faculty Scholar Award in 2019. She was the Program Chair of the IEEE Hot Chips in 2020. She serves as an Associate Editor for the IEEE Journal of Solid-State Circuits and IEEE Solid-State Circuits Letters.