23 citations · 67 across the 12 of their papers we have counts for
14 papers
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…
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…
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…
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…
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…
At the Locus of Performance: Quantifying the Effects of Copious 3D-Stacked Cache on HPC Workloads
Jens Domke, Emil Vatai, Balazs Gerofi +9
Over the last three decades, innovations in the memory subsystem were primarily targeted at overcoming the data movement bottleneck. In this paper, we focus on a specific market tr…
PERKS: a Locality-Optimized Execution Model for Iterative Memory-bound GPU Applications
Lingqi Zhang, Mohamed Wahib, Peng Chen +4
Iterative memory-bound solvers commonly occur in HPC codes. Typical GPU implementations have a loop on the host side that invokes the GPU kernel as much as time/algorithm steps the…
Performance Portable Back-projection Algorithms on CPUs: Agnostic Data Locality and Vectorization Optimizations
Peng Chen, Mohamed Wahib, Xiao Wang +4
Computed Tomography (CT) is a key 3D imaging technology that fundamentally relies on the compute-intense back-projection operation to generate 3D volumes. GPUs are typically used f…
Matrix Engines for High Performance Computing:A Paragon of Performance or Grasping at Straws?
Jens Domke, Emil Vatai, Aleksandr Drozd +8
Matrix engines or units, in different forms and affinities, are becoming a reality in modern processors; CPUs and otherwise. The current and dominant algorithmic approach to Deep L…
iFDK: A Scalable Framework for Instant High-resolution Image Reconstruction
Peng Chen, Mohamed Wahib, Shinichiro Takizawa +2
Computed Tomography (CT) is a widely used technology that requires compute-intense algorithms for image reconstruction. We propose a novel back-projection algorithm that reduces th…
A Versatile Software Systolic Execution Model for GPU Memory-Bound Kernels
Peng Chen, Mohamed Wahib, Shinichiro Takizawa +2
This paper proposes a versatile high-performance execution model, inspired by systolic arrays, for memory-bound regular kernels running on CUDA-enabled GPUs. We formulate a systoli…