reproducibility

Scalable systems lay essential foundations of the modern information industry. HPC data centers tend to have hundreds to thousands of nodes in their clusters. The use of “extreme-scale” distributed systems has given birth to a new type of bug: scalability bugs.

Project idea 1: Software, hardware, and wetware building LabOP with simultaneous language & protocol development & test executions Topics: Software standard development, Laboratory automation, Biology Skills: Python, Semantic Web Technologies (RDF, OWL), interest to think about describing biological & chemical laboratory processes Difficulty: Moderate Size: Large (350 hours) Mentors: Tim Fallon Dan Bryce About: The Laboratory Open Protocol Language (LabOP) See link: https://bioprotocols.

Deep Neural Networks (DNN) have achieved success in many machine learning (ML) tasks including image recognition, video classification and natural language processing. Nonetheless, training DNN models is highly computation intensive and usually requires running complex computations on GPUs, while GPU is a very expensive and scarce resource.

With the flourishing of the ideas like smart cities or smart manufacturing, a massive amount of edge devices (e.g., traffic or security cameras, thermometers, flood sensors, et al.) are deployed and connected to the network to collect/analyze data across the space and time and help the stakeholders like city governments or manufacturers optimizing their plans and operations.

The Data Storage Research Vision 2025, organized in an NSF workshop, calls for more “AI for storage” research. However, performing ML-for-storage research can be a daunting task for new storage researchers.

A high-throughput workflow execution system is needed to continuously gain insights from th e increasingly abundant genomics data. However, genomics workflows often have long execution times (e.g., hours to days) due to their large input files.

Massive storage systems rely heavily on erasure coding (EC) to protect data from drive failures and provide data durability. Existing storage systems mostly adopt single-level erasure coding (SLEC) to protect data, either performing EC at the network level or performing EC at the local level.

High-performance computing (HPC) clusters typically suffer from performance degradation over time. The heterogeneous nature of clusters and the inevitable defects in various infrastructure layers will result in a harder performance prediction inside.

The OpenROAD project is a non-profit, DARPA-funded and Google sponsored project committed to creating low-cost and innovative Electronic Design Automation (EDA) tools and flows for IC design. Our mission is to democratize IC design, break down barriers of cost and access and mitigate schedule risk through native and open source innovation and collaboration with ecosystem partners.