60 citations · 179 across the 33 of their papers we have counts for
36 papers
Multi-GPU Communication Schemes for Iterative Solvers: When CPUs are Not in Charge
Ismayil Ismayilov, Javid Baydamirli, Doğan Sağbili +2
This paper proposes a fully autonomous execution model for multi-GPU applications that completely excludes the involvement of the CPU beyond the initial kernel launch. In a typical…
An Efficient Out-of-Core Tomographic Imaging Framework for Edge Devices
Xuetao Chen, Cong Ma, Xiangyu Meng +10
Computed Tomography (CT) is an essential 3D imaging technology widely used in medical diagnostics and scientific research. However, performing CT imaging on edge devices is challen…
Scalable High-Fidelity Macromolecular Docking for GPU-Accelerated Supercomputers
Xiangyu Meng, Peng Chen, Mingzhen Li +7
Flexible macromolecular docking offers high-fidelity predictions of biomolecular interactions, but remains prohibitively expensive at scale. Among existing approaches, LightDock le…
Efficient Computing for Medical Image Acquisition and Reconstruction
Xiao Wang, Jayasai Rajagopal, Md Safaiat Hossain +4
Medical imaging systems such as CT, MRI, PET, and SPECT do not directly acquire images. Instead, they measure physical signals that encode anatomical or physiological information,…
RT-RkNN: Reverse k Nearest Neighbor Queries as a Graphics Ray Casting Problem
Zhengyang Bai, Peng Chen, Mohamed Wahib
Reverse k nearest neighbor (RkNN) queries are fundamental in spatial databases, location-based analytics, and recommendation systems. Existing state-of-the-art techniques rely on s…
SHIRO: Near-Optimal Communication Strategies for Distributed Sparse Matrix Multiplication
Chen Zhuang, Lingqi Zhang, Benjamin Brock +5
Distributed Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental operation in high-performance computing and deep learning applications. The major performance bottleneck in…
RAPTOR: Practical Numerical Profiling of Scientific Applications
Faveo Hoerold, Ivan R. Ivanov, Akash Dhruv +4
The proliferation of low-precision units in modern high-performance architectures increasingly burdens domain scientists. Historically, the choice in HPC was easy: can we get away…
Distributed Cross-Channel Hierarchical Aggregation for Foundation Models
Aristeidis Tsaris, Isaac Lyngaas, John Lagregren +6
Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation. This potential stems from their ability to aggregate images fr…
Paradigm Shift in Infrastructure Inspection Technology: Leveraging High-performance Imaging and Advanced AI Analytics to Inspect Road Infrastructure
Du Wu, Enzhi Zhang, Isaac Lyngaas +14
Effective road infrastructure management is crucial for modern society. Traditional manual inspection techniques remain constrained by cost, efficiency, and scalability, while came…
A Unifying Framework to Enable Artificial Intelligence in High Performance Computing Workflows
Jens Domke, Mohamed Wahib, Anshu Dubey +2
Current trends point to a future where large-scale scientific applications are tightly-coupled HPC/AI hybrids. Hence, we urgently need to invest in creating a seamless, scalable fr…
Balanced and Elastic End-to-end Training of Dynamic LLMs
Mohamed Wahib, Muhammed Abdullah Soyturk, Didem Unat
To reduce the computational and memory overhead of Large Language Models, various approaches have been proposed. These include a) Mixture of Experts (MoEs), where token routing aff…
ORBIT-2: Scaling Exascale Vision Foundation Models for Weather and Climate Downscaling
Xiao Wang, Jong-Youl Choi, Takuya Kurihaya +15
Sparse observations and coarse-resolution climate models limit effective regional decision-making, underscoring the need for robust downscaling. However, existing AI methods strugg…
Can Tensor Cores Benefit Memory-Bound Kernels? (No!)
Lingqi Zhang, Jiajun Huang, Sheng Di +2
Tensor cores are specialized processing units within GPUs that have demonstrated significant efficiency gains in compute-bound applications such as Deep Learning Training by accele…
Scaling Large-scale GNN Training to Thousands of Processors on CPU-based Supercomputers
Chen Zhuang, Lingqi Zhang, Du Wu +8
Graph Convolutional Networks (GCNs), particularly for large-scale graphs, are crucial across numerous domains. However, training distributed full-batch GCNs on large-scale graphs s…
A Framework for Automatic Validation and Application of Lossy Data Compression in Ensemble Data Assimilation
Kai Keller, Hisashi Yashiro, Mohamed Wahib +3
Ensemble data assimilation techniques form an indispensable part of numerical weather prediction. As the ensemble size grows and model resolution increases, the amount of required…
Sequence Length Scaling in Vision Transformers for Scientific Images on Frontier
Aristeidis Tsaris, Chengming Zhang, Xiao Wang +9
Vision Transformers (ViTs) are pivotal for foundational models in scientific imagery, including Earth science applications, due to their capability to process large sequence length…
Adaptive Patching for High-resolution Image Segmentation with Transformers
Enzhi Zhang, Isaac Lyngaas, Peng Chen +5
Attention-based models are proliferating in the space of image analytics, including segmentation. The standard method of feeding images to transformer encoders is to divide the ima…
Ultra-Long Sequence Distributed Transformer
Xiao Wang, Isaac Lyngaas, Aristeidis Tsaris +7
Transformer models trained on long sequences often achieve higher accuracy than short sequences. Unfortunately, conventional transformers struggle with long sequence training due t…
KAKURENBO: Adaptively Hiding Samples in Deep Neural Network Training
Truong Thao Nguyen, Balazs Gerofi, Edgar Josafat Martinez-Noriega +2
This paper proposes a method for hiding the least-important samples during the training of deep neural networks to increase efficiency, i.e., to reduce the cost of training. Using…
Exploiting Scratchpad Memory for Deep Temporal Blocking: A case study for 2D Jacobian 5-point iterative stencil kernel (j2d5pt)
Lingqi Zhang, Mohamed Wahib, Peng Chen +4
General Purpose Graphics Processing Units (GPGPU) are used in most of the top systems in HPC. The total capacity of scratchpad memory has increased by more than 40 times in the las…
Revisiting Temporal Blocking Stencil Optimizations
Lingqi Zhang, Mohamed Wahib, Peng Chen +4
Iterative stencils are used widely across the spectrum of High Performance Computing (HPC) applications. Many efforts have been put into optimizing stencil GPU kernels, given the p…
Myths and Legends in High-Performance Computing
Satoshi Matsuoka, Jens Domke, Mohamed Wahib +2
In this thought-provoking article, we discuss certain myths and legends that are folklore among members of the high-performance computing community. We gathered these myths from co…
Image Gradient Decomposition for Parallel and Memory-Efficient Ptychographic Reconstruction
Xiao Wang, Aristeidis Tsaris, Debangshu Mukherjee +5
Ptychography is a popular microscopic imaging modality for many scientific discoveries and sets the record for highest image resolution. Unfortunately, the high image resolution fo…
Preparing for the Future -- Rethinking Proxy Apps
Satoshi Matsuoka, Jens Domke, Mohamed Wahib +5
A considerable amount of research and engineering went into designing proxy applications, which represent common high-performance computing workloads, to co-design and evaluate the…