3 papers
cs.PF2026
DriftSched: Adaptive QoS-Aware Scheduling under Runtime Token Drift for Multi-Tenant GPU Inference
Kathiravan Palaniappan
The rapid growth of large language model (LLM) inference services has increased the demand for efficient multi-tenant GPU scheduling. While modern inference runtimes such as vLLM i…
cs.PF2026
DEEP-GAP: Deep-learning Evaluation of Execution Parallelism in GPU Architectural Performance
Kathiravan Palaniappan
Modern datacenters increasingly rely on low-power, single-slot inference accelerators to balance performance, energy efficiency, and rack density constraints. The NVIDIA T4 GPU has…
cs.PF2026
GDEV-AI: A Generalized Evaluation of Deep Learning Inference Scaling and Architectural Saturation
Kathiravan Palaniappan
The deployment of deep learning inference in production environments continues to grow, where throughput, latency, and hardware efficiency are critical. Although specialized accele…