相关论文: A Scientific Data Management System for Irregular …

Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a…

Time Series Data Server (TSDS) is a software package for implementing a server that provides fast super-setting, sub-setting, filtering, and uniform gridding of time series-like data. TSDS was developed to respond quickly to requests for…

Large language models (LLMs) are increasingly integrated into real-time machine learning applications, where safeguarding user privacy is paramount. Traditional differential privacy mechanisms often struggle to balance privacy and accuracy,…

Science Data Systems (SDS) handle science data from acquisition through processing to distribution. They are deployed in the Cloud today, and the efficiency of Cloud instance utilization is critical to success. Conventional SDS are unable…

Distributed shared memory (DSM) allows to implement and deploy applications onto distributed architectures using the convenient shared memory programming model in which a set of tasks are able to allocate and access data despite their…

As the amount of data produced in society continues to grow at an exponential rate, modern applications are incurring significant performance and energy penalties due to high data movement between the CPU and memory/storage. While…

Designing a scientific software stack to meet the needs of the next-generation of mesh-based simulation demands, not only scalable and efficient mesh and data management on a wide range of platforms, but also an abstraction layer that makes…

Optimal multiple sequence alignment by dynamic programming, like many highly dimensional scientific computing problems, has failed to benefit from the improvements in computing performance brought about by multi-processor systems, due to…

The increasing proliferation of IoT devices and AI applications has created a demand for scalable and efficient computing solutions, particularly for applications requiring real-time processing. The compute continuum integrates edge and…

Message-driven executions with over-decomposition of tasks constitute an important model for parallel programming and have been demonstrated for irregular applications. Supporting efficient execution of such message-driven irregular…

Many astronomers and astrophysicists require large computing resources for their research, which are usually obtained via dedicated (and expensive) parallel machines. Depending on the type of the problem to be solved, an alternative…

This survey reviews several approaches of data mining (DM) in healthindustry from many research groups world wide. The focus is on modern multi-core processors built into today's commodity computers, which are typically found at university…

Designing effective data manipulation methods is a long standing problem in data lakes. Traditional methods, which rely on rules or machine learning models, require extensive human efforts on training data collection and tuning models.…

In materials science and manufacturing, vast amounts of heterogeneous data (e.g., measurement and simulation logs, process data, publications) serve as the bedrock of valuable knowledge for various engineering applications. However,…

Performing massive data mining experiments with multiple datasets and methods is a common task faced by most bioinformatics and computational biology laboratories. WEKA is a machine learning package designed to facilitate this task by…

Large Language Models (LLMs) such as GPT-4 and Llama have shown remarkable capabilities in a variety of software engineering tasks. Despite the advancements, their practical deployment faces challenges, including high financial costs, long…

The difficulty of monitoring biodiversity at fine scales and over large areas limits ecological knowledge and conservation efforts. To fill this gap, Species Distribution Models (SDMs) predict species across space from spatially explicit…

With Dynamic Resource Management (DRM) the resources assigned to a job can be changed dynamically during its execution. From the system's perspective, DRM opens a new level of flexibility in resource allocation and job scheduling and…

This paper aims to create a transition path from file-based IO to streaming-based workflows for scientific applications in an HPC environment. By using the openPMP-api, traditional workflows limited by filesystem bottlenecks can be overcome…

The goal of the Collaborative Research Center 1625 is the establishment of a scientific basis for the atomic-scale understanding and design of multifunctional compositionally complex solid solution surfaces. Next to materials synthesis in…