Publications (16)
Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions
Keita Teranishi, Harshitha Menon, William F. Godoy +25
We discuss the challenges and propose research directions for using AI to revolutionize the development of high-performance computing (HPC) software. AI technologies, in particular…
Time-Based Roofline for Deep Learning Performance Analysis
Yunsong Wang, Charlene Yang, Steven Farrell +3
Deep learning applications are usually very compute-intensive and require a long run time for training and inference. This has been tackled by researchers from both hardware and so…
Modern Gyrokinetic Particle-In-Cell Simulation of Fusion Plasmas on Top Supercomputers
Bei Wang, Stephane Ethier, William Tang +4
The Gyrokinetic Toroidal Code at Princeton (GTC-P) is a highly scalable and portable particle-in-cell (PIC) code. It solves the 5D Vlasov-Poisson equation featuring efficient utili…
Understanding Quantum Control Processor Capabilities and Limitations through Circuit Characterization
Anastasiia Butko, George Michelogiannakis, Samuel Williams +5
Continuing the scaling of quantum computers hinges on building classical control hardware pipelines that are scalable, extensible, and provide real time response. The instruction s…
Understanding the Impact of Input Entropy on FPU, CPU, and GPU Power
Sridutt Bhalachandra, Brian Austin, Samuel Williams +1
Power is increasingly becoming a limiting resource in high-performance, GPU-accelerated computing systems. Understanding the range and sources of power variation is essential in se…
Optimizing PyTorch Inference with LLM-Based Multi-Agent Systems
Kirill Nagaitsev, Luka Grbcic, Samuel Williams +1
Maximizing performance on available GPU hardware is an ongoing challenge for modern AI inference systems. Traditional approaches include writing custom GPU kernels and using specia…
FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings via Deep Neural Networks
Zhe Bai, Xishuo Wei, William Tang +3
Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel model reduction methods,…
Low-dimensional geometry learning for turbulence prediction in optimized stellarators
Xishuo Wei, Handi Huang, Haotian Chen +4
The optimized stellarator is an attractive concept for which the averaged particle radial drift is zero, and the single particle loss can be significantly reduced. But for the reac…
Hierarchical Roofline Performance Analysis for Deep Learning Applications
Charlene Yang, Yunsong Wang, Steven Farrell +2
This paper presents a practical methodology for collecting performance data necessary to conduct hierarchical Roofline analysis on NVIDIA GPUs. It discusses the extension of the Em…
Large Scale Multi-GPU Based Parallel Traffic Simulation for Accelerated Traffic Assignment and Propagation
Xuan Jiang, Raja Sengupta, James Demmel +1
Traffic propagation simulation is crucial for urban planning, enabling congestion analysis, travel time estimation, and route optimization. Traditional micro-simulation frameworks…
Evaluating the Potential of Disaggregated Memory Systems for HPC applications
Nan Ding, Pieter Maris, Hai Ah Nam +8
Disaggregated memory is a promising approach that addresses the limitations of traditional memory architectures by enabling memory to be decoupled from compute nodes and shared acr…
An efficient basis set representation for calculating electrons in molecules
Jeremiah R. Jones, Francois-Henry Rouet, Keith V. Lawler +9
The method of McCurdy, Baertschy, and Rescigno, J. Phys. B, 37, R137 (2004) is generalized to obtain a straightforward, surprisingly accurate, and scalable numerical representation…
StFT: Spatio-temporal Fourier Transformer for Long-term Dynamics Prediction
Da Long, Shandian Zhe, Samuel Williams +2
Simulating the long-term dynamics of multi-scale and multi-physics systems poses a significant challenge in understanding complex phenomena across science and engineering. The comp…
Comprehensive Performance Modeling and System Design Insights for Foundation Models
Shashank Subramanian, Ermal Rrapaj, Peter Harrington +6
Generative AI, in particular large transformer models, are increasingly driving HPC system design in science and industry. We analyze performance characteristics of such transforme…
Exploiting Multiple Levels of Parallelism in Sparse Matrix-Matrix Multiplication
Ariful Azad, Grey Ballard, Aydin Buluc +5
Sparse matrix-matrix multiplication (or SpGEMM) is a key primitive for many high-performance graph algorithms as well as for some linear solvers, such as algebraic multigrid. The s…
An efficient multi-core implementation of a novel HSS-structured multifrontal solver using randomized sampling
Pieter Ghysels, Xiaoye S. Li, Francois-Henry Rouet +2
We present a sparse linear system solver that is based on a multifrontal variant of Gaussian elimination, and exploits low-rank approximation of the resulting dense frontal matrice…