305 citations · 531 across the 22 of their papers we have counts for
23 papers
Improving Progressive Compression with Adaptive Interpolation and Coefficient Decomposition
Wenbo Li, Xuan Wu, Qian Gong +6
Exascale simulations generate data far faster than it can be stored or analyzed, making efficient data reduction essential. Error-controlled lossy compression offers high compressi…
CRISP: Cliff-awaRe Input-adaptive Sparse Prefilling with Structural-Mass-Motivated Routing
Huu Huy Nguyen, Chien Van Nguyen, Franck Dernoncourt +4
The attention prefilling phase of long-context LLM inference scales quadratically, making self-attention a severe computational bottleneck. Traditional sparse attention methods mit…
BlockMGARD: Accelerating Adaptive Scientific Data Reduction with Region-of-Interest Error Control on GPUs
Yanliang Li, Qian Gong, Qing Liu +5
The growing scale of scientific data makes lossy compression essential for reducing data volume under controllable error. Transformation-based compressors using multilevel decompos…
Quantum Sampling Architecture for Protein Structure Reconstruction on Utility-Scale Hardware
Yuqi Zhang, Bo Fang, Yuxin Yang +6
Predicting the structure of short peptides in protein binding pockets remains difficult because this regime requires physics-based conformational search, yet existing methods do no…
Deep Investigation of Neutral Gas Origins (DINGO): Options for the Processing and Storage of Radio Astronomy Data for robust Deep Spectral Line Imaging in the SKA-Era using uv-Grids
Alexander Williamson, Richard Dodson, Pascal J. Elahi +9
The next generation of radio astronomy telescopes are challenging existing data analysis paradigms, as they have an order of magnitude more antennas and larger bandwidth. Foremost…
Accelerating In-transit Isosurface Generation With Topology Preserving Compression
Yanliang Li, Jieyang Chen
Data visualization through isosurface generation is critical in various scientific fields, including computational fluid dynamics, medical imaging, and geophysics. However, the hig…
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
Qian Gong, Jieyang Chen, Ben Whitney +13
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional data compression capa…
Spatiotemporally adaptive compression for scientific dataset with feature preservation -- a case study on simulation data with extreme climate events analysis
Qian Gong, Chengzhu Zhang, Xin Liang +10
Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated…
Improving Energy Saving of One-sided Matrix Decompositions on CPU-GPU Heterogeneous Systems
Jieyang Chen, Xin Liang, Kai Zhao +3
One-sided dense matrix decompositions (e.g., Cholesky, LU, and QR) are the key components in scientific computing in many different fields. Although their design has been highly op…
Scalable Hybrid Learning Techniques for Scientific Data Compression
Tania Banerjee, Jong Choi, Jaemoon Lee +5
Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific…
MSREP: A Fast yet Light Sparse Matrix Framework for Multi-GPU Systems
Jieyang Chen, Chenhao Xie, Jesun S Firoz +5
Sparse linear algebra kernels play a critical role in numerous applications, covering from exascale scientific simulation to large-scale data analytics. Offloading linear algebra k…
Improving I/O Performance for Exascale Applications through Online Data Layout Reorganization
Lipeng Wan, Axel Huebl, Junmin Gu +12
The applications being developed within the U.S. Exascale Computing Project (ECP) to run on imminent Exascale computers will generate scientific results with unprecedented fidelity…
Scalable Multigrid-based Hierarchical Scientific Data Refactoring on GPUs
Jieyang Chen, Lipeng Wan, Xin Liang +10
Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth makes it increasingly infeasible to store and share all data produced by scientific…
Fast and Scalable Sparse Triangular Solver for Multi-GPU Based HPC Architectures
Chenhao Xie, Jieyang Chen, Jesun S Firoz +5
Designing efficient and scalable sparse linear algebra kernels on modern multi-GPU based HPC systems is a daunting task due to significant irregular memory references and workload…
MGARD+: Optimizing Multilevel Methods for Error-bounded Scientific Data Reduction
Xin Liang, Ben Whitney, Jieyang Chen +8
Data management is becoming increasingly important in dealing with the large amounts of data produced by large-scale scientific simulations and instruments. Existing multilevel com…
Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures
Jiannan Tian, Cody Rivera, Sheng Di +4
Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that da…
BeeSwarm: Enabling Scalability Tests in Continuous Integration
Jieyang Chen, Qiang Guan, Li-Ta Lo +2
Testing is one of the most important steps in software development. It ensures the quality of software. Continuous Integration (CI) is a widely used testing system that can report…
FTRANS: Energy-Efficient Acceleration of Transformers using FPGA
Bingbing Li, Santosh Pandey, Haowen Fang +7
In natural language processing (NLP), the "Transformer" architecture was proposed as the first transduction model replying entirely on self-attention mechanisms without using seque…
Accelerating Multigrid-based Hierarchical Scientific Data Refactoring on GPUs
Jieyang Chen, Lipeng Wan, Xin Liang +8
Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth make it increasingly infeasible to store and share all data produced by scientific…
FT-CNN: Algorithm-Based Fault Tolerance for Convolutional Neural Networks
Kai Zhao, Sheng Di, Sihuan Li +6
Convolutional neural networks (CNNs) are becoming more and more important for solving challenging and critical problems in many fields. CNN inference applications have been deploye…
TSM2X: High-Performance Tall-and-Skinny Matrix-Matrix Multiplication on GPUs
Cody Rivera, Jieyang Chen, Nan Xiong +2
Linear algebra operations have been widely used in big data analytics and scientific computations. Many works have been done on optimizing linear algebra operations on GPUs with re…
Evaluating Modern GPU Interconnect: PCIe, NVLink, NV-SLI, NVSwitch and GPUDirect
Ang Li, Shuaiwen Leon Song, Jieyang Chen +4
High performance multi-GPU computing becomes an inevitable trend due to the ever-increasing demand on computation capability in emerging domains such as deep learning, big data and…
Build and Execution Environment (BEE): an Encapsulated Environment Enabling HPC Applications Running Everywhere
Jieyang Chen, Qiang Guan, Xin Liang +5
Variations in High Performance Computing (HPC) system software configurations mean that applications are typically configured and built for specific HPC environments. Building appl…