296 citations · 660 across the 30 of their papers we have counts for
18 papers · 1 filter
Dynamic Quality Metric Oriented Error-bounded Lossy Compression for Scientific Datasets
Jinyang Liu, Sheng Di, Kai Zhao +3
With the ever-increasing execution scale of high performance computing (HPC) applications, vast amounts of data are being produced by scientific research every day. Error-bounded l…
gZCCL: Compression-Accelerated Collective Communication Framework for GPU Clusters
Jiajun Huang, Sheng Di, Xiaodong Yu +11
GPU-aware collective communication has become a major bottleneck for modern computing platforms as GPU computing power rapidly rises. A traditional approach is to directly integrat…
Accelerating MPI Collectives with Process-in-Process-based Multi-object Techniques
Jiajun Huang, Kaiming Ouyang, Yujia Zhai +8
In the exascale computing era, optimizing MPI collective performance in high-performance computing (HPC) applications is critical. Current algorithms face performance degradation d…
FT-GEMM: A Fault Tolerant High Performance GEMM Implementation on x86 CPUs
Shixun Wu, Yujia Zhai, Jiajun Huang +2
General matrix/matrix multiplication (GEMM) is crucial for scientific computing and machine learning. However, the increased scale of the computing platforms raises concerns about…
Anatomy of High-Performance GEMM with Online Fault Tolerance on GPUs
Shixun Wu, Yujia Zhai, Jinyang Liu +4
General Matrix Multiplication (GEMM) is a crucial algorithm for various applications such as machine learning and scientific computing, and an efficient GEMM implementation is esse…
An Optimized Error-controlled MPI Collective Framework Integrated with Lossy Compression
Jiajun Huang, Sheng Di, Xiaodong Yu +11
With the ever-increasing computing power of supercomputers and the growing scale of scientific applications, the efficiency of MPI collective communications turns out to be a criti…
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…
SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors
Xin Liang, Kai Zhao, Sheng Di +9
Today's scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage spac…
FT-BLAS: A High Performance BLAS Implementation With Online Fault Tolerance
Yujia Zhai, Elisabeth Giem, Quan Fan +3
Basic Linear Algebra Subprograms (BLAS) is a core library in scientific computing and machine learning. This paper presents FT-BLAS, a new implementation of BLAS routines that not…
Efficient Soft-Error Detection for Low-precision Deep Learning Recommendation Models
Sihuan Li, Jianyu Huang, Ping Tak Peter Tang +4
Soft error, namely silent corruption of signal or datum in a computer system, cannot be caverlierly ignored as compute and communication density grow exponentially. Soft error dete…
SDRBench: Scientific Data Reduction Benchmark for Lossy Compressors
Kai Zhao, Sheng Di, Xin Liang +5
Efficient error-controlled lossy compressors are becoming critical to the success of today's large-scale scientific applications because of the ever-increasing volume of data produ…
SDC Resilient Error-bounded Lossy Compressor
Sihuan Li, Sheng Di, Kai Zhao +3
Lossy compression is one of the most important strategies to resolve the big science data issue, however, little work was done to make it resilient against silent data corruptions…
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…
Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP
Dingwen Tao, Sheng Di, Xin Liang +2
With ever-increasing volumes of scientific data produced by HPC applications, significantly reducing data size is critical because of limited capacity of storage space and potentia…
Improving Performance of Iterative Methods by Lossy Checkponting
Dingwen Tao, Sheng Di, Xin Liang +2
Iterative methods are commonly used approaches to solve large, sparse linear systems, which are fundamental operations for many modern scientific simulations. When the large-scale…
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…
In-Depth Exploration of Single-Snapshot Lossy Compression Techniques for N-Body Simulations
Dingwen Tao, Sheng Di, Zizhong Chen +1
In situ lossy compression allowing user-controlled data loss can significantly reduce the I/O burden. For large-scale N-body simulations where only one snapshot can be compressed a…
Algorithmic Energy Saving for Parallel Cholesky, LU, and QR Factorizations
Li Tan, Zizhong Chen
The pressing demands of improving energy efficiency for high performance scientific computing have motivated a large body of software-controlled hard- ware solutions using Dynamic…