CSI High Performance Computing Seminar Series 2024
from
Monday 1 January 2024 (09:00)
to
Tuesday 31 December 2024 (16:00)
Monday 1 January 2024
Tuesday 2 January 2024
Wednesday 3 January 2024
Thursday 4 January 2024
Friday 5 January 2024
Saturday 6 January 2024
Sunday 7 January 2024
Monday 8 January 2024
Tuesday 9 January 2024
Wednesday 10 January 2024
Thursday 11 January 2024
Friday 12 January 2024
Saturday 13 January 2024
Sunday 14 January 2024
Monday 15 January 2024
Tuesday 16 January 2024
Wednesday 17 January 2024
Thursday 18 January 2024
Friday 19 January 2024
Saturday 20 January 2024
Sunday 21 January 2024
Monday 22 January 2024
Tuesday 23 January 2024
Wednesday 24 January 2024
Thursday 25 January 2024
Friday 26 January 2024
Saturday 27 January 2024
Sunday 28 January 2024
Monday 29 January 2024
Tuesday 30 January 2024
Wednesday 31 January 2024
14:00
Programming future heterogeneous quantum-classical supercomputing architectures
Programming future heterogeneous quantum-classical supercomputing architectures
14:00 - 15:00
**Speaker**: Alexander McCaskey, Quantum Computing Software Architect, NVIDIA **Abstract**: Supercomputing architectures based on GPU acceleration have greatly improved our scientific computing workflows and applications over the past decade. Quantum computing has recently been proposed as a potential addition to this heterogeneous compute architecture, serving as another node-level accelerator to continue problem scalability in domains such as quantum many-body physics and artificial intelligence. As stand-alone quantum processing units (QPUs) continue to evolve and improve, the applied computational science community is left to wonder - how do we build, program, and deploy large-scale quantum-classical heterogeneous architectures that incorporate both GPUs and QPUs? In this talk, we will demonstrate how NVIDIA is leveraging its current suite of multi-GPU platforms to define and deploy the NVIDIA quantum platform. Specifically, we will highlight CUDA Quantum - a quantum-classical programming model in C++ with Python bindings, and associated compiler toolchain built on the MLIR and LLVM frameworks. This talk will focus on technical details of the programming model and compiler architecture and demonstrate the utility of CUDA Quantum when targeting both real and emulated quantum coprocessors. **Speaker Bio**: Alexander McCaskey is a quantum computing software architect at NVIDIA, and the manager of the Quantum Computing Architecture team. His work is focused on programming models, compilers, and languages for heterogeneous quantum-classical computing. He is the lead architect for the CUDA Quantum project, a novel quantum-classical programming model in C++ and Python enabling performant workflows on heterogeneous architectures. Previously, he was a Staff Scientist at Oak Ridge National Laboratory where he led the development of the XACC system-level quantum framework and the QCOR quantum-classical C++ compiler platform. He received B.Sc. degrees in 2010 in Physics and Mathematics from the University of Tennessee, and a M.Sc. degree in physics from the Virginia Polytechnic and State University in 2014.
Thursday 1 February 2024
Friday 2 February 2024
Saturday 3 February 2024
Sunday 4 February 2024
Monday 5 February 2024
Tuesday 6 February 2024
Wednesday 7 February 2024
Thursday 8 February 2024
Friday 9 February 2024
Saturday 10 February 2024
Sunday 11 February 2024
Monday 12 February 2024
Tuesday 13 February 2024
Wednesday 14 February 2024
Thursday 15 February 2024
Friday 16 February 2024
Saturday 17 February 2024
Sunday 18 February 2024
Monday 19 February 2024
Tuesday 20 February 2024
Wednesday 21 February 2024
Thursday 22 February 2024
Friday 23 February 2024
Saturday 24 February 2024
Sunday 25 February 2024
Monday 26 February 2024
Tuesday 27 February 2024
Wednesday 28 February 2024
Thursday 29 February 2024
Friday 1 March 2024
Saturday 2 March 2024
Sunday 3 March 2024
Monday 4 March 2024
Tuesday 5 March 2024
Wednesday 6 March 2024
Thursday 7 March 2024
Friday 8 March 2024
Saturday 9 March 2024
Sunday 10 March 2024
Monday 11 March 2024
Tuesday 12 March 2024
Wednesday 13 March 2024
Thursday 14 March 2024
Friday 15 March 2024
Saturday 16 March 2024
Sunday 17 March 2024
Monday 18 March 2024
Tuesday 19 March 2024
Wednesday 20 March 2024
Thursday 21 March 2024
Friday 22 March 2024
Saturday 23 March 2024
Sunday 24 March 2024
Monday 25 March 2024
Tuesday 26 March 2024
Wednesday 27 March 2024
14:00
Advancing Intelligent Scheduling for Complex Large-Scale Systems
Advancing Intelligent Scheduling for Complex Large-Scale Systems
14:00 - 15:00
**Speaker**: Jing Li, Assistant Professor, Department of Computer Science at New Jersey Institute of Technology **Abstract**: As computer architecture and software continue to evolve, large-scale systems like high-performance computing and supercomputers are becoming increasingly complex, consisting of diverse processing units, specialized accelerators, and complex memory hierarchies. Concurrently, scientific workflows are also growing in complexity and dynamism. Maintaining optimal application performance for timely processing while efficiently utilizing resources poses a significant challenge, exacerbated by the intricate scheduling problems inherent in these systems. Traditional ad hoc heuristic-based approaches are no longer sufficient, and manual resource allocation decisions are cumbersome and time-consuming for developers. To address these challenges, there is a pressing need for an intelligent scheduling framework capable of automating resource allocation to enhance system performance. However, existing learning-based approaches face limitations in handling combinatorial optimization, long-distance dependencies, and generalizing across diverse workflows. This talk will discuss potential avenues to leverage theoretical insights in resource allocation problems and develop efficient reinforcement learning formulations to tackle these challenges head-on. **Speaker Bio**: Jing Li is an assistant professor in the Department of Computer Science at New Jersey Institute of Technology. She received her Ph.D. degree from Washington University in St. Louis in 2017. Her research interests include parallel computing, real-time systems, and reinforcement learning for system design and optimization. She has high impact publications in top conferences with three outstanding paper awards. Jing is the recipient of the NSF CAREER Award in 2024 and the Department of Energy Early Career Research Program (ECRP) Award in 2023.
Thursday 28 March 2024
Friday 29 March 2024
Saturday 30 March 2024
Sunday 31 March 2024
Monday 1 April 2024
Tuesday 2 April 2024
Wednesday 3 April 2024
Thursday 4 April 2024
Friday 5 April 2024
Saturday 6 April 2024
Sunday 7 April 2024
Monday 8 April 2024
Tuesday 9 April 2024
Wednesday 10 April 2024
Thursday 11 April 2024
Friday 12 April 2024
Saturday 13 April 2024
Sunday 14 April 2024
Monday 15 April 2024
Tuesday 16 April 2024
Wednesday 17 April 2024
Thursday 18 April 2024
Friday 19 April 2024
Saturday 20 April 2024
Sunday 21 April 2024
Monday 22 April 2024
Tuesday 23 April 2024
Wednesday 24 April 2024
Thursday 25 April 2024
Friday 26 April 2024
Saturday 27 April 2024
Sunday 28 April 2024
Monday 29 April 2024
Tuesday 30 April 2024
Wednesday 1 May 2024
Thursday 2 May 2024
Friday 3 May 2024
Saturday 4 May 2024
Sunday 5 May 2024
Monday 6 May 2024
Tuesday 7 May 2024
Wednesday 8 May 2024
Thursday 9 May 2024
Friday 10 May 2024
Saturday 11 May 2024
Sunday 12 May 2024
Monday 13 May 2024
Tuesday 14 May 2024
Wednesday 15 May 2024
Thursday 16 May 2024
Friday 17 May 2024
Saturday 18 May 2024
Sunday 19 May 2024
Monday 20 May 2024
Tuesday 21 May 2024
Wednesday 22 May 2024
11:00
Supercomputer-based in-silico virtual humans: the future of medicine NOW
Supercomputer-based in-silico virtual humans: the future of medicine NOW
11:00 - 12:00
**Speaker**: Mariano Vazquez, CTO / CSO, ELEM Biotech **Abstract**: ELEM Biotech is a startup company of the Barcelona Supercomputing Center, BSC. We develop Virtual Humans based on supercomputing and high-fidelity mutliscale / multiphysics modellization. Together with supercomputing power and accurate modellization, we develop mathematical tools to create populations of Virtual Humans representative of Real ones. The goal is to put in the hands of the biomedical stakeholders a tool for (a) run in-silico clinical trials and (b) personalize the virtual humans to a given real patient under a certain condition. Our tools allow to improve and optimize therapies. Today we are focus on cardiac and vascular diseases. In this talk we will discuss our latest achievements. **Speaker Bio**: MV is co-founder and CTO/CSO of the ELEM Biotech (The Virtual Humans Factory), a spinoff company of the Spanish Barcelona Supercomputing Center (BSC), founded with the goal of speeding-up the technology transfer of BSC modelling technology for he biomedical domain, in particular, the code Alya. He is also one of the two leaders of the Alya Development Team at the BSC, with more than 70 scientists and developers. Graduated in Physical Sciences from the University of Buenos Aires, Argentina, in 1993, he completed his bachelor's thesis on Chaos in Dynamical Systems. Doctor in Physical Sciences from the Polytechnic University of Catalonia (UPC), Spain, in 1999, he completed his doctoral thesis in Computational Fluid Mechanics (on numerical schemes for stabilization of compressible flow equations for finite elements). He has carried out post-doctoral stays at the Pole Scientifique Univ. Paris VI / Dassault Aviation (in multigrid for compressible and incompressible turbulent flow, funded by a Marie-Curie scholarship from the EC) and at INRIA Sophia Antipolis (shape optimization using the adjoint method), both in France, for 3 years. He was a consultant for the company Gridsystems (grid computing) in Palma de Mallorca (Spain) and a lecturer at the University of Girona (Spain). Since 2012 he has been a senior scientist at the CSIC, on leave since July 2018, when he co-founded ELEM. In 2004, his scientific interests experienced the irresistible grasp of computational biomedicine until this day (and counting).
Thursday 23 May 2024
Friday 24 May 2024
Saturday 25 May 2024
Sunday 26 May 2024
Monday 27 May 2024
Tuesday 28 May 2024
Wednesday 29 May 2024
Thursday 30 May 2024
Friday 31 May 2024
Saturday 1 June 2024
Sunday 2 June 2024
Monday 3 June 2024
Tuesday 4 June 2024
Wednesday 5 June 2024
Thursday 6 June 2024
Friday 7 June 2024
Saturday 8 June 2024
Sunday 9 June 2024
Monday 10 June 2024
Tuesday 11 June 2024
Wednesday 12 June 2024
Thursday 13 June 2024
Friday 14 June 2024
Saturday 15 June 2024
Sunday 16 June 2024
Monday 17 June 2024
Tuesday 18 June 2024
Wednesday 19 June 2024
Thursday 20 June 2024
Friday 21 June 2024
Saturday 22 June 2024
Sunday 23 June 2024
Monday 24 June 2024
Tuesday 25 June 2024
Wednesday 26 June 2024
Thursday 27 June 2024
Friday 28 June 2024
Saturday 29 June 2024
Sunday 30 June 2024
Monday 1 July 2024
Tuesday 2 July 2024
Wednesday 3 July 2024
Thursday 4 July 2024
Friday 5 July 2024
Saturday 6 July 2024
Sunday 7 July 2024
Monday 8 July 2024
Tuesday 9 July 2024
Wednesday 10 July 2024
Thursday 11 July 2024
Friday 12 July 2024
Saturday 13 July 2024
Sunday 14 July 2024
Monday 15 July 2024
Tuesday 16 July 2024
Wednesday 17 July 2024
Thursday 18 July 2024
Friday 19 July 2024
Saturday 20 July 2024
Sunday 21 July 2024
Monday 22 July 2024
Tuesday 23 July 2024
Wednesday 24 July 2024
Thursday 25 July 2024
Friday 26 July 2024
Saturday 27 July 2024
Sunday 28 July 2024
Monday 29 July 2024
Tuesday 30 July 2024
Wednesday 31 July 2024
Thursday 1 August 2024
Friday 2 August 2024
Saturday 3 August 2024
Sunday 4 August 2024
Monday 5 August 2024
Tuesday 6 August 2024
Wednesday 7 August 2024
14:00
Time-Series Hamiltonian Kernels: A Parallel Quantum-Classical Approach for Temporal Data Classification
Time-Series Hamiltonian Kernels: A Parallel Quantum-Classical Approach for Temporal Data Classification
14:00 - 15:00
**Speaker**: Santosh Kumar Radha, Agnostiq **Abstract**: This talk introduces a novel hybrid quantum-classical machine-learning framework for time-series classification. We present the Time-Series Hamiltonian Kernel (TSHK), constructed using quantum states evolved through parameterized time evolution operators and integrated into a Quantum-Classical-Convex neural network (QCC-net). This end-to-end learnable system produces dataset-generalized kernel functions that are purpose-tuned for temporal and ordered data. We demonstrate the performance on synthetic and real-world datasets and showcase efficient parallel implementation on superconducting quantum processors using Quantum Multi-Programming (QMP). Our approach exploits the quantum-native property of time series evolution in quantum processes to map and identify a corresponding process that best represents the classification of temporal data, addressing the challenges of temporal data analysis in the NISQ era. **Speaker Bio**: Santosh is the Head of R&D and Product at Agnostiq, where he is working on various R&D projects involving quantum algorithms and software. Santosh holds a Ph.D in theoretical physics from Case Western Reserve University, where he started working on massive gravity and moved to condensed matter physics. His research was to theoretically and computationally understand the topological effects occurring in quantum systems as a result of "knotted" wave functions in both interacting and non-interacting fermionic systems and its impact in lower dimensional entanglement. Currently, Santosh plays a pivotal role in shaping Agnotiq’s product strategy, particularly by enhancing the scalability and performance of next-generation AI applications and large-scale scientific simulations across multi-cloud environments.
Thursday 8 August 2024
Friday 9 August 2024
Saturday 10 August 2024
Sunday 11 August 2024
Monday 12 August 2024
Tuesday 13 August 2024
Wednesday 14 August 2024
Thursday 15 August 2024
Friday 16 August 2024
Saturday 17 August 2024
Sunday 18 August 2024
Monday 19 August 2024
Tuesday 20 August 2024
Wednesday 21 August 2024
14:00
On Training Large Foundation Models on Frontier
On Training Large Foundation Models on Frontier
14:00 - 15:00
**Speaker**: Sajal Dash, ORNL **Abstract**: Training large-scale language models (LLMs) presents significant computational challenges, particularly for models with billions to trillions of parameters. This talk explores efficient distributed training strategies on Frontier, the world's first exascale supercomputer, to tackle these challenges. We examine various parallelism techniques—tensor, pipeline, and sharded data parallelism—to train trillion-parameter models. Through empirical analysis and hyperparameter tuning, we achieve GPU throughputs of 31.96% to 38.38% across models of different sizes and demonstrate 100% weak scaling efficiency on up to 3072 MI250X GPUs. Additionally, we explore the potential of sparsely activated models, such as those using mixture of expert mechanisms, as a more resource-efficient alternative to dense LLMs, providing insights into their design and performance. **Speaker Bio**: As a research scientist, Sajal Dash explores scaling approaches for large-scale deep learning applications by focusing on convergence behavior and problems associated with large batch size. He will also continue his research on mitigating catastrophic forgetting during incremental training of deep learning models in a streaming setting. Before joining Oak Ridge National Laboratory, Sajal completed his Ph.D. in Computer Science at Virginia Tech. His Ph.D. dissertation titled “Exploring the Landscape of Big Data Analytics Through Domain-Aware Algorithm Design” focused on solving large-scale domain problems by leveraging domain-knowledge with properties of big data. His dissertation solved a big data problem in cancer biology by efficiently distributing the combinatorial workload across nodes while regularizing memory access patterns. Dr. Dash’s Ph.D. has been greatly impacted by two summer internships at Oak Ridge National Laboratory in 2018 and 2019 under the mentorship of Dr. Junqi Yin and Dr. Mallikarjun Shankar. Sajal received his B.Sc. in Computer Science and Engineering from BUET, Bangladesh, and MS in Computer Science from UNC Chapell Hill before getting his Ph.D. in Computer Science from Virginia Tech.
Thursday 22 August 2024
Friday 23 August 2024
Saturday 24 August 2024
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Tuesday 27 August 2024
Wednesday 28 August 2024
Thursday 29 August 2024
Friday 30 August 2024
Saturday 31 August 2024
Sunday 1 September 2024
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Wednesday 11 September 2024
Thursday 12 September 2024
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Sunday 15 September 2024
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Thursday 19 September 2024
Friday 20 September 2024
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Tuesday 24 September 2024
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Thursday 26 September 2024
Friday 27 September 2024
Saturday 28 September 2024
Sunday 29 September 2024
Monday 30 September 2024
Tuesday 1 October 2024
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Saturday 5 October 2024
Sunday 6 October 2024
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Tuesday 15 October 2024
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Tuesday 31 December 2024