Related papers: Reproducible and Portable Workflows for Scientific…
The computational demands for scientific applications are continuously increasing. The emergence of cloud computing has enabled on-demand resource allocation. However, relying solely on infrastructure as a service does not achieve the…
A computing solution combining the GNU Guix functional package manager with the Apptainer container system is presented. This approach provides fully declarative and reproducible software environments suitable for computational materials…
Cloud is now the leading software and computing hardware innovator, and is changing the landscape of compute to one that is optimized for artificial intelligence and machine learning (AI/ML). Computing innovation was initially driven to…
Artificial Intelligence for scientific applications increasingly requires training large models on data that cannot be centralized due to privacy constraints, data sovereignty, or the sheer volume of data generated. Federated learning (FL)…
In the field of computational science and engineering, workflows often entail the application of various software, for instance, for simulation or pre- and postprocessing. Typically, these components have to be combined in arbitrarily…
In this paper, we summarize our effort to create and utilize a simple framework to coordinate computational analytics tasks with the help of a workflow system. Our design is based on a minimalistic approach while at the same time allowing…
The enabling of scientific experiments that are embarrassingly parallel, long running and data-intensive into a cloud-based execution environment is a desirable, though complex undertaking for many researchers. The management of such…
Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units…
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative,…
Output-intensive scientific applications are highly sensitive to low storage throughput. While existing scientific application stacks are optimized for traditional High-Performance Computing (HPC) environments with high remote storage and…
The basic idea behind Cloud computing is that resource providers offer elastic resources to end users. In this paper, we intend to answer one key question to the success of Cloud computing: in Cloud, can small or medium-scale scientific…
Open source cloud technologies provide a wide range of support for creating customized compute node clusters to schedule tasks and managing resources. In cloud infrastructures such as Jetstream and Chameleon, which are used for scientific…
Cloud computing provisions computer resources at a cost-effective way based on demand. Therefore it has become a viable solution for big data analytics and artificial intelligence which have been widely adopted in various domain science.…
The convergence of IoT, Edge, Cloud, and HPC technologies creates a compute continuum that merges cloud scalability and flexibility with HPC's computational power and specialized optimizations. However, integrating cloud and HPC resources…
The need for computational resources grows as computational algorithms gain popularity in different sectors of the scientific community. This search has stimulated the development of several cloud platforms that abstract the complexity of…
A National Science Foundation-sponsored container runtimes investigation was conducted by the Aristotle Cloud Federation to better understand the challenges of selecting and using Docker, Singularity, and X-Containers. The main goal of this…
Oftentimes, there is a need to experiment with different programming languages and technologies when designing software applications. Such experiments must be reproducible and share-able within a team workplace, and manual effort should be…
We propose an approach for improved reproducibility that includes capturing and relating provenance characteristics and performance metrics, in a hybrid queriable system, the ProvEn server. The system capabilities are illustrated on two use…
With the increasing amount of data and use of computation in science, software has become an important component in many different domains. Computing is now being used more often and in more aspects of scientific work including data…
Cloud resource management is often modeled by two-dimensional bin packing with a set of items that correspond to tasks having fixed CPU and memory requirements. However, applications running in clouds are much more flexible: modern…