osre24

Project Idea Description Topics: Virtualization, Containerization, Profiling, Reproducibility Skills: C and Python and DevOps experience. Difficulty: Medium Size: Large; 350 hours Mentors: Tanu Malik This project aims to characterize computer-science related artifacts that are either submitted to conferences or deposited in reproducibility hubs such as Chameleon.

The SC Conference Series, a leading forum on High Performance Computing (HPC), supports scientific rigor through an enhanced reproducibility of accepted papers. To that end, all manuscripts submitted to the SC Technical Papers program must contain an Artifact Description.

Today’s Continuous Integration/Continuous Development (CI/CD) trends encourage rapid design of software using a wide range of software components, followed by frequent updates that are immediately deployed on the cloud. The complexity of cloud systems along with the component diversity and break-neck pace of development amplify the difficulty in identifying or fixing problems related to performance, resilience, and security.

Project Idea Description Topics: HPC, MPI, distributed systems Skills: C++, Python Difficulty: Difficult Size: Large; 350 hours Mentors: Tanu Malik Notebooks have gained wide popularity in scientific computing. A notebook is both a web-based interactive front- end to program workflows and a lightweight container for sharing code and its output.

SciStream is a framework and toolkit that attempts to tackle the problem of enabling high-speed(+100Gbps), memory-to-memory data streaming in scientific environments. This task is particularly challenging because data producers (e.g., data acquisition applications on scientific instruments, simulations on supercomputers) and consumers (e.

Trovi on Chameleon is an open-source service designed to significantly enhance the practical reproducibility of computer science research. By allowing Chameleon users to upload, share, and access packaged experiments and other research artifacts, Trovi aims to streamline the process of replicating and building upon existing studies.

Topics: applied machine learning, data leakage, reproducibility Skills: Python, data analysis, machine learning Difficulty: Medium Size: Large (350 hours) Mentors: Fraida Fund and Mohamed Saeed Project Idea Description Data leakage has been identified as a major cause of irreproducibility of a paper’s findings, when machine learning techniques are applied to problems in science.

Topics: computer networks, congestion control, reproducibility Skills: Python, Bash scripting, Linux, computer network performance evaluation Difficulty: Medium Size: Large (350 hours) Mentors: Fraida Fund and Ashutosh Srivastava Project Idea Description

Topics: Provenance, reproducibility, standards, image creation Skills: Python, JSON, Bash scripting, Linux, image creation and deployment Difficulty: Medium Size: Large (350 hours) Mentors: Raül Sirvent Project Idea Description The COMPSs programming model provides an interface for the programming of a sequential application that is transformed in a workflow that, thanks to the COMPSs runtime, is later scheduled in the available computing resources.

Topics: bioinformatics, spatial transcriptomics, gene imputation, benchmarking, cross-platform/species analysis Skills: Programming Languages: Proficient in Python and/or R, commonly used in bioinformatics. Data Analysis: Experience with statistical data analysis and machine learning models.