arXiv subjects

Abdelwahab Kawafi

Publications and source records attributed to Abdelwahab Kawafi.

2 recordsLinked to original sources

arXiv2026

Multi-tenant Kubernetes Use Cases for AI, Secure Computing and Data Services, and More

Kubernetes, as a container orchestration engine, has been widely used in cloud-native ecosystems for several years. In supercomputing ecosystems, especially where bare-metal performance for compute and network devices are considered, the adoption is somewhat limited. However, with the increasing diversity of use cases such as AI, secure and confidential computing for sensitive data, and mixed workload orchestration, a traditional, single-tenant batch computing system does not offer the flexibility and reproducibility to which public cloud users are accustomed. Note that Kubernetes is not considered a replacement for batch scheduling systems, which have powerful features for large-scale MPI jobs with thousands of network end points. Rather, it is a complementary service provided as part of a national AI Research Resource. We evaluate Kubernetes deployment on a Hewlett Packard Enterprise (HPE) Cray EX supercomputerwith HPE Slingshot interconnect, called Isambard-AI, with co-design use cases. One is a Trusted Research Environment used for medical and health sciences. The other combines KubeRay, Ray, and vLLM to provide a distributed, sandboxed, persistent AI model hosting service targeting multi-tenant confidential computing. We discuss challenges and lessons learned, and where further development is needed to offer a production Kubernetes-as-a-Service on HPE Cray EX (and later) platforms.

cs.DC↗

arXiv2024

Colloidoscope: Detecting Dense Colloids in 3d with Deep Learning

Colloidoscope is a deep learning pipeline employing a 3D residual Unet architecture, designed to enhance the tracking of dense colloidal suspensions through confocal microscopy. This methodology uses a simulated training dataset that reflects a wide array of real-world imaging conditions, specifically targeting high colloid volume fraction and low-contrast scenarios where traditional detection methods struggle. Central to our approach is the use of experimental signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and point-spread-functions (PSFs) to accurately quantify and simulate the experimental data. Our findings reveal that Colloidoscope achieves superior recall in particle detection (finds more particles) compared to conventional heuristic methods. Simultaneously, high precision is maintained (high fraction of true positives.) The model demonstrates a notable robustness to photobleached samples, thereby prolonging the imaging time and number of frames than may be acquired. Furthermore, Colloidoscope maintains small scale resolution sufficient to classify local structural motifs. Evaluated across both simulated and experimental datasets, Colloidoscope brings the advancements in computer vision offered by deep learning to particle tracking at high volume fractions. We offer a promising tool for researchers in the soft matter community, this model is deployed and available to use pretrained: https://github.com/wahabk/colloidoscope.

cond-mat.soft↗