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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
BiJuTy: An Interactive HPC-Aware Big Data Cluster Lifecyc...
[Submitted on 23 Jun 2026] · 2026-06-24 · via cs.DC updates on arXiv.org

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Abstract:The increasing demand for data processing has created a pressing need for access to high-performance computing (HPC) systems. Nevertheless, leveraging these systems to execute complex big data processing workflows remains a significant challenge, especially for beginners. This work presents BiJuTy, a solution designed to bridge the accessibility gap for big data workflows on HPC systems within the Jupyter ecosystem. By providing an interactive and user-friendly interface, BiJuTy simplifies cluster lifecycle management and performance assessment, making it more accessible on HPC systems to beginners and experienced users alike. The solution is presented as an interactive interface that guides the user through the entire process, from setting up the cluster configuration to carrying out initial performance assessments. Additionally, the framework enables seamless management of multiple clusters directly within the Jupyter Notebook interface, eliminating the need to switch outside of working environment. The collection of performance metrics from various sources further simplifies the optimization workflow. Furthermore, an illustrative example is provided to demonstrate how BiJuTy can be deployed to optimize the performance of a big data processing application. This example showcases how the entire big data processing lifecycle can be iteratively executed and optimized in just a few clicks, helping to reach the goal of optimization easily and interactively. By facilitating such workflows, this work contributes in bringing the field of big data computing and high-performance computing one step closer to the goal of seamless interaction and usability.

Submission history

From: Apurv Deepak Kulkarni [view email]
[v1] Tue, 23 Jun 2026 10:49:40 UTC (2,366 KB)