The Institute for Robust Quantum Simulation will focus on using quantum simulation to gain insight into������������������and thereby exploit������������������the rich behavior of complex quantum systems. Combining expertise from researchers in computer science, engineering, and physics, our team will address the challenge of robustly simulating classically intractable quantum systems of practical interest, and verifying the correctness of the simulation result.

This project proposes to explore solutions to both the workflow scheduling problem and the fault awareness requirements. Its primary aim is to prototype a novel software scheduling technology to manage dynamically changing heterogeneous resource pools on one side and a fault propagation mechanism within Flux instances on the other side. This reflects emerging trends in combing HPC and cloud computing while taking full advantage of characteristics for heterogeneous resource requirements of modern applications workflows under challenges to address faults in a transparent manner.

This work aims to study sparsity in widely-used tensor networks by introducing constraints, regularization, dictionary, and/or domain knowledge for better data compression, faster computation, lower memory storage, along with better interpretability by: 1) Proposing memory hierarchy and microarchitecture-aware representations and effective yet efficient data (re-)arranging; 2) Designing memory hierarchy-aware and balanced algorithm with smart page arrangement; 3) Erasing the curse of dimensionality through memoization and intelligent data allocation; 4) Exploring specialized architecture on GPU and FPGA. We will accelerate six application scenarios by leveraging the scalable and highly optimized sparse tensor network on distributed heterogeneous systems.

Quantum computing has the potential to provide a significant advantage over classical computing in terms of algorithmic complexity. The STAQ project is focused on demonstrating such an advantage on an ion trap quantum hardware platform developed at Duke with 64 or more qubits. This requires a co-design between hardware and software to be successful, which Duke University has been developing. NCSU proposes to complement these efforts, potentially leading to earlier demonstration of quantum advantage, by creating a transpiler to translate Qiskit programs to run on STAQ devices, assess benefits of creating complex native gates, and modeling the reliability of STAQ ion trap quantum computers.

This work seeks to investigate the potential of non-volatile memories (NVM) with its novel properties of byte addressability, persistency, larger capacity, yet higher latency, and addresses the its applicability within the next generation edge cloud infrastructures. The objective of this work is to assess the efficacy of providing services in the edge cloud as opposed to conventional cloud-hosted infrastructure.

Emerging CPS applications increasingly require significant computational power to benefit from machine learning. From lower-end multi-core systems (4-core A72) to embedded GPUs (NVIDIA Drive DGX), highly parallel compute platforms dominate the application space. However, real-time requirements poorly map to such platforms and impose high coding complexity. The objective of this work is to alleviate programmers by extending the OpenMP to real-time tasks and data parallelism. Instead of deploying a real-time operating system with limited compatibility, we propose novel OpenMP extensions, define their semantics and provide an implementation on Linux mapping to privileged priorities under static and dynamic real-time scheduling policies.

We attack a key software challenge in quantum computing, the programmability of quantum computers. Writing quantum programs is vastly more difficult than writing classical programs, with relatively few of the skills required by the latter translating to the former. Furthermore, the two dominant forms of quantum computation������������������circuit-model quantum computing and quantum annealing������������������are programmed fundamentally differently from each other, and each requires substantial effort to master. The goal of this research is to both unify and facilitate the exploitation of circuit-model quantum computers and quantum annealers. As a result, productivity of computational scientists is increased in all scientific disciplines.

CPS and IoT devices are inherently networked, which exposes them to malware attacks. We propose to significantly increase the cyber security specifically of CPS and IoT computing devices by developing real-time monitoring techniques that defeat cyber-attacks.

The goal of this proposal is to optimize and to openly provide to the OA community a new technology to rapidly and automatically measure cartilage thickness, appearance and changes on magnetic resonance images (MRI) of the knee for huge image databases. This will allow assessment of trajectories of cartilage loss over time and associations with clinical outcomes on an unprecedented scale; future work will focus on incorporating additional disease markers, ranging from MRI-derived biomarkers for bone and synovial lesions, to biochemical biomarkers, to genetic information.

This project proposes to explore different solutions of software support for heterogeneous memory architectures for future supercomputers. Its primary aim will be to seamlessly integrate the new memory technologies in to the existing architecture support while taking full advantage of its characteristics for existing HPC applications and making the programmers job easier for developing new applications for the future systems.