arXiv subjects

Yoonho Park

Publications and source records attributed to Yoonho Park.

3 recordsLinked to original sources

arXiv2026

Emerging Personas and Tools for Hybrid Quantum-HPC Systems

Quantum computing is entering an era where quantum processing units (QPUs) with tens to hundreds of qubits are increasingly integrated with high-performance computing (HPC) systems to support hybrid quantum-classical workloads. This paradigm, often referred to as Quantum-Centric SuperComputing (QCSC), introduces new operational, software, and infrastructure challenges. In this paper, we identify and characterize six distinct personas involved in QCSC environments. Particular attention is given to emerging roles that arise at the interface between quantum and HPC systems. We analyze the responsibilities and tools associated with each persona and examine their differing requirements for operational data. Consistent data representations and analytics frameworks will be required to support diverse users across the quantum-HPC ecosystem. By defining personas and their data needs, we provide a foundation for future discussions in this critical area.

quant-ph↗

arXiv2026

Examining QRMI as a Unified Interface for Quantum-HPC Integration

The efficient and scalable integration of quantum resources into high-performance computing (HPC) environments requires standardized mechanisms for resource management, scheduling, and workflow orchestration across diverse and heterogeneous infrastructures. The Quantum Resource Management Interface (QRMI) addresses this challenge through a thin, vendor-agnostic middleware layer that provides standardized APIs for scheduling, executing, and monitoring quantum workloads while exposing quantum resources as first-class schedulable resources alongside CPUs and GPUs. Although previous work demonstrated QRMI integration with the Slurm workload manager, its applicability across other workload managers remained unexamined. This paper extends the validation of QRMI to a broad range of workload managers, including PBS, LSF, Grid Engine, Kubernetes, and the Flux Framework, encompassing traditional batch schedulers, a cloud-native orchestration platform, and a graph-based scheduler. We examine the integration patterns, implementation requirements, and scheduler-specific considerations associated with each environment and compare QRMI with alternative approaches to quantum resource integration. We demonstrate that QRMI provides a portable and flexible abstraction layer that minimizes scheduler-specific modifications while enabling consistent access to heterogeneous quantum resources across both on-premises and cloud environments.

cs.ET↗

arXiv2024

The infrastructure powering IBM's Gen AI model development

AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model training is driven by the emergence of generative AI and foundational models, where on occasion thousands of GPUs must cooperate on a single training job for the model to be trained in a reasonable time. Delivering efficient and high-performing AI training requires an end-to-end solution that combines hardware, software and holistic telemetry to cater for multiple types of AI workloads. In this report, we describe IBM's hybrid cloud infrastructure that powers our generative AI model development. This infrastructure includes (1) Vela: an AI-optimized supercomputing capability directly integrated into the IBM Cloud, delivering scalable, dynamic, multi-tenant and geographically distributed infrastructure for large-scale model training and other AI workflow steps and (2) Blue Vela: a large-scale, purpose-built, on-premises hosting environment that is optimized to support our largest and most ambitious AI model training tasks. Vela provides IBM with the dual benefit of high performance for internal use along with the flexibility to adapt to an evolving commercial landscape. Blue Vela provides us with the benefits of rapid development of our largest and most ambitious models, as well as future-proofing against the evolving model landscape in the industry. Taken together, they provide IBM with the ability to rapidly innovate in the development of both AI models and commercial offerings.

cs.DC↗