Search for: All records

The next generation of supercomputing resources is expected to greatly expand the scope of HPC environments, both in terms of more diverse workloads and user bases, as well as the integration of edge computing infrastructures. This will likely require new mechanisms and approaches at the Operating System level to support these broader classes of workloads along with their different security requirements. We claim that a key mechanism needed for these workloads is the ability to securely compartmentalize the system software executing on a given node. In this paper, we present initial efforts in exploring the integration of secure and trusted computing capabilities into an HPC system software stack. As part of this work we have ported the Kitten Lightweight Kernel (LWK) to the ARM64 architecture and integrated it with the Hafnium hypervisor, a reference implementation of a secure partition manager (SPM) that provides security isolation for virtual machines. By integrating Kitten with Hafnium, we are able to replace the commodity oriented Linux based resource management infrastructure and reduce the overheads introduced by using a full weight kernel (FWK) as the node-level resource scheduler. While our results are very preliminary, we are able to demonstrate measurable performance improvements on small scale ARM based SOC platforms. 

Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data. Prevalent clustering methods mainly focus on vector or matrix-variate data and are not applicable to general-order tensors, which arise frequently in modern scientific and business applications. Moreover, there is a gap between statistical guarantees and computational efficiency for existing tensor clustering solutions due to the nature of their non-convex formulations. In this work, we bridge this gap by developing a provable convex formulation of tensor co-clustering. Our convex co-clustering (CoCo) estimator enjoys stability guarantees and its computational and storage costs are polynomial in the size of the data. We further establish a non-asymptotic error bound for the CoCo estimator, which reveals a surprising ``blessing of dimensionality" phenomenon that does not exist in vector or matrix-variate cluster analysis. Our theoretical findings are supported by extensive simulated studies. Finally, we apply the CoCo estimator to the cluster analysis of advertisement click tensor data from a major online company. Our clustering results provide meaningful business insights to improve advertising effectiveness.