SearcharxivSearch

arXiv subjects

Francesco G. Blanco

Publications and source records attributed to Francesco G. Blanco.

2 recordsLinked to original sources

COSMA: Communication-aware Optimization of Fermionic Simulation Kernels for Modular Quantum Architectures

Quantum simulation is a leading application of quantum computing, but scaling to chemically relevant problems requires modular architectures composed of interconnected quantum processing units. In such systems, inter-core quantum communication becomes a major performance bottleneck. In this work, we present COSMA, a communication-aware compilation framework for fermionic simulation kernels targeting modular quantum architectures. Our approach jointly optimizes fermion-to-qubit mapping, Pauli scheduling, and qubit allocation to minimize inter-core state transfers. Evaluated on molecular benchmarks, COSMA achieves up to $2.5\times$ reduction in communication cost compared to state-of-the-art baselines, with a median improvement of $1.7\times$. These results demonstrate that cross-layer co-design is essential for efficient and scalable quantum simulation on multi-core quantum hardware.

quant-ph

Deep Reinforcement Learning based Online Scheduling Policy for Deep Neural Network Multi-Tenant Multi-Accelerator Systems

Currently, there is a growing trend of outsourcing the execution of DNNs to cloud services. For service providers, managing multi-tenancy and ensuring high-quality service delivery, particularly in meeting stringent execution time constraints, assumes paramount importance, all while endeavoring to maintain cost-effectiveness. In this context, the utilization of heterogeneous multi-accelerator systems becomes increasingly relevant. This paper presents RELMAS, a low-overhead deep reinforcement learning algorithm designed for the online scheduling of DNNs in multi-tenant environments, taking into account the dataflow heterogeneity of accelerators and memory bandwidths contentions. By doing so, service providers can employ the most efficient scheduling policy for user requests, optimizing Service-Level-Agreement (SLA) satisfaction rates and enhancing hardware utilization. The application of RELMAS to a heterogeneous multi-accelerator system composed of various instances of Simba and Eyeriss sub-accelerators resulted in up to a 173% improvement in SLA satisfaction rate compared to state-of-the-art scheduling techniques across different workload scenarios, with less than a 1.5% energy overhead.

cs.AR