A5064166827- Advanced Data Storage Technologies
- Parallel Computing and Optimization Techniques
- Algorithms and Data Compression
- Distributed and Parallel Computing Systems
- Advanced Data Compression Techniques
- Advanced Neural Network Applications
- Scientific Computing and Data Management
- Recommender Systems and Techniques
- Computer Graphics and Visualization Techniques
- Caching and Content Delivery
- Advanced Graph Neural Networks
- Machine Learning and Data Classification
- Advanced Memory and Neural Computing
- Power System Optimization and Stability
- Power Systems Fault Detection
- Cloud Computing and Resource Management
- Ferroelectric and Negative Capacitance Devices
- Complex Network Analysis Techniques
- Adversarial Robustness in Machine Learning
- Network Packet Processing and Optimization
- Advanced Image and Video Retrieval Techniques
- High-Voltage Power Transmission Systems
- Multimodal Machine Learning Applications
- Oceanographic and Atmospheric Processes
- Machine Learning and Algorithms
Indiana University Bloomington
2022-2025
Indiana University
2022-2023
State Grid Corporation of China (China)
2023
Washington State University
2020-2022
Northwestern University
2022
University of Alabama
2019-2020
North China Electric Power University
2018
University of Electronic Science and Technology of China
2012
Today's scientific simulations require a significant reduction of data volume because extremely large amounts they produce and the limited I/O bandwidth storage space. Error-bounded lossy compression has been considered one most effective solutions to above problem. In practice, however, best-fit method often needs be customized or optimized in particular diverse characteristics different datasets various user requirements on quality performance. this paper, we address issue with novel...
Increasing data volumes from scientific simulations and instruments (supercomputers, accelerators, telescopes) often exceed network, storage, analysis capabilities. The community's response to this challenge is reduction. Reduction can take many forms, such as triggering, sampling, filtering, quantization, dimensionality This report focuses on a specific technique: lossy compression. Lossy compression retains all points, leveraging correlations controlled reduced accuracy. Quality...
To help understand our universe better, researchers and scientists currently run extreme-scale cosmology simulations on leadership supercomputers. However, such can generate large amounts of scientific data, which often result in expensive costs data associated with movement storage. Lossy compression techniques have become attractive because they significantly reduce size maintain high fidelity for post-analysis. In this paper, we propose to use GPU-based lossy cosmological simulations. Our...
Graph Neural Networks (GNNs) have drawn tremendous attention due to their unique capability extend Machine Learning (ML) approaches applications broadly-defined as having unstructured data, especially graphs. Compared with other modalities, the acceleration of is more challenging irregularity and heterogeneity derived from graph typologies. Existing efforts, however, focused mainly on handling graphs' not studied heterogeneity. To this end we propose H-GCN, a PL (Programmable Logic) AIE (AI...
Today's high-performance computing (HPC) applications are producing vast volumes of data, which challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique mitigate storage burden movement cost. Huffman coding arguably most efficient Entropy algorithm in information theory, it could be found as fundamental step many modern algorithms DEFLATE. On other hand, today's HPC more relying on accelerators GPU supercomputers, while...
Error-bounded lossy compression is one of the most effective techniques for reducing scientific data sizes. However, traditional trial-and-error approach used to configure compressors finding optimal trade-off between reconstructed quality and ratio prohibitively expensive. To resolve this issue, we develop a general-purpose analytical ratio-quality model based on prediction-based framework, which can effectively foresee reduced ratio, as well impact compressed post-hoc analysis quality. Our...
Today's deep neural networks (DNNs) are becoming deeper and wider because of increasing demand on the analysis quality more complex applications to resolve. The wide DNNs, however, require large amounts resources (such as memory, storage, I/O), significantly restricting their utilization resource-constrained platforms. Although some DNN simplification methods weight quantization) have been proposed address this issue, they suffer from either low compression ratios or high errors, which may...
Error-bounded lossy compression is critical to the success of extreme-scale scientific research because ever-increasing volumes data produced by today's high-performance computing (HPC) applications. Not only can error-controlled compressors significantly reduce I/O and storage burden but they retain high fidelity for post analysis. Existing state-of-the-art compressors, however, generally suffer from relatively low decompression throughput (up hundreds megabytes per second on a single CPU...
Today's scientific high-performance computing applications and advanced instruments are producing vast volumes of data across a wide range domains, which impose serious burden on transfer storage. Error-bounded lossy compression has been developed widely used in the community because it not only can significantly reduce but also strictly control distortion based user-specified error bound. Existing compressors, however, cannot offer ultrafast speed, is highly demanded by numerous or use...
Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving reconstructed fidelity very well. Many error-bounded compressors have developed for a wide range of parallel and distributed use cases years. These are designed with distinct models design principles, such that each them features particular pros cons. In this paper we provide comprehensive survey emerging techniques different involving big to process. The key...
While both the database and high-performance computing (HPC) communities utilize lossless compression methods to minimize floating-point data size, a disconnect persists between them. Each community designs assesses in domain-specific manner, making it unclear if HPC techniques can benefit applications or vice versa. With increasingly leaning towards in-situ analysis visualization, more from scientific simulations are being stored databases like Key-Value Stores queried using in-memory...
Error-bounded lossy compression is a state-of-the-art data reduction technique for HPC applications because it not only significantly reduces storage overhead but also can retain high fidelity postanalysis. Because supercomputers and are becoming heterogeneous using accelerator-based architectures, in particular GPUs, several development teams have recently released GPU versions of their compressors. However, existing GPU-based compressors suffer from either low decompression throughput or...
Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. With ever-emerging heterogeneous high-performance computing (HPC) architecture, GPU-accelerated error-bounded compressors (such as CUSZ and cuZFP) have been developed. However, they suffer from either low performance or ratios. To this end, we propose CUSZ+ to target both high ratios throughputs. We identify that sparsity smoothness are key factors Our contributions in work fourfold:...
Today's scientific simulations and instruments are producing a large amount of data, leading to difficulties in storing, transmitting, analyzing these data. While error-controlled lossy compressors effective significantly reducing data volumes efficiently developing databases for multiple applications, they mainly support error controls on raw which leaves significant gap between the user's downstream analysis. This may cause unqualified uncertainties outcomes analysis, a.k.a quantities...
Extreme-scale cosmological simulations have been widely used by today's researchers and scientists on leadership supercomputers. A new generation of error-bounded lossy compressors has in workflows to reduce storage requirements minimize the impact throughput limitations while saving large snapshots high-fidelity data for post-hoc analysis. In this paper, we propose adaptively provide compression configurations compute partitions with newly designed post-analysis aware rate-quality modeling....
Convolutional neural networks (CNNs) are becoming increasingly deeper, wider, and non-linear because of the growing demand on prediction accuracy analysis quality. The wide deep CNNs, however, require a large amount computing resources processing time. Many previous works have studied model pruning to improve inference performance, but little work has been done for effectively reducing training cost. In this paper, we propose ClickTrain: an efficient accurate end-to-end framework CNNs....
The deployment of more and power electronic devices results in the sub‐synchronous oscillation caused by sub/super‐synchronous harmonics associated with inverter‐based renewable generators (IRPGs). New measurement are needed to monitor operation states generator (RPG) areas. In this study, a synchronised device for systems high penetration IRPGs (SMD‐R) is proposed developed. Unlike traditional devices, an SMD‐R able perform real‐time inter‐harmonics fundamental phasor estimation that...
As supercomputers advance towards exascale capabilities, computational intensity increases significantly, and the volume of data requiring storage transmission experiences exponential growth. Adaptive Mesh Refinement (AMR) has emerged as an effective solution to address these two challenges. Concurrently, error-bounded lossy compression is recognized one most efficient approaches tackle latter issue. Despite their respective advantages, few attempts have been made investigate how AMR can...
As HPC systems continue to grow exascale, the amount of data that needs be saved or transmitted is exploding. To this end, many previous works have studied using error-bounded lossy compressors reduce size and improve I/O performance. However, little work has been done for effectively offloading compression onto FPGA-based SmartNICs overhead. In paper, we propose a hardware-algorithm co-design an efficient adaptive compressor scientific on FPGAs (called CEAZ), which first can achieve high...
More and more HPC applications require fast effective compression techniques to handle large volumes of data in storage transmission. Not only do these need compress the effectively during simulation, but they also perform decompression efficiently for post hoc analysis. SZ is an error-bounded lossy compressor scientific data, cuSZ a version designed take advantage GPU's power. At present, cuSZ's performance has been optimized significantly while its still suffers considerably lower because...
Today's large-scale scientific applications running on high-performance computing (HPC) systems generate vast data volumes. Thus, compression is becoming a critical technique to mitigate the storage burden and data-movement cost. However, existing lossy compressors for cannot achieve high ratio throughput simultaneously, hindering their adoption in many requiring fast compression, such as in-memory compression. To this end, work, we develop high- error-bounded compressor GPUs (called...